From 58de3d4a43a8d8a858450982cbfaee98f26be803 Mon Sep 17 00:00:00 2001 From: Saiyam Pathak Date: Tue, 18 Aug 2026 15:13:12 +0530 Subject: [PATCH 01/19] Add blog post: Running a single LLM across two GPUs with vLLM Answers a reader question about splitting a BF16 model across two A40s: how tensor parallelism partitions each layer, the memory math for whether it fits, and measured TP vs PP numbers on a pair of cards with no NVLink. All figures come from a real run (vLLM 0.27.1, Qwen3-32B BF16) on two RTX PRO 6000 cards held to a 40.47 GiB per-card budget to match a 45 GiB A40 at --gpu-memory-utilization 0.90. Adds three CSS animations following the existing series pattern: - two-gpu-tensor-split-animation: column/row-parallel split, one all-reduce - two-gpu-memory-fit-animation: 61.02 GiB against one card, then two - two-gpu-tp-vs-pp-animation: the TP/PP tradeoff plus measured results Marked draft: true pending review. Signed-off-by: Saiyam Pathak --- components/TwoGpuMemoryFitAnimation.jsx | 353 +++++++++++++++ components/TwoGpuTensorSplitAnimation.jsx | 414 ++++++++++++++++++ components/TwoGpuTpVsPpAnimation.jsx | 373 ++++++++++++++++ ...-a-single-llm-across-two-gpus-with-vllm.md | 363 +++++++++++++++ lib/markdown.js | 9 + .../cover.png | Bin 0 -> 193910 bytes .../cover.svg | 51 +++ scripts/gen-two-gpu-vllm-cover.mjs | 239 ++++++++++ 8 files changed, 1802 insertions(+) create mode 100644 components/TwoGpuMemoryFitAnimation.jsx create mode 100644 components/TwoGpuTensorSplitAnimation.jsx create mode 100644 components/TwoGpuTpVsPpAnimation.jsx create mode 100644 content/blog/running-a-single-llm-across-two-gpus-with-vllm.md create mode 100644 public/img/blog/running-a-single-llm-across-two-gpus-with-vllm/cover.png create mode 100644 public/img/blog/running-a-single-llm-across-two-gpus-with-vllm/cover.svg create mode 100644 scripts/gen-two-gpu-vllm-cover.mjs diff --git a/components/TwoGpuMemoryFitAnimation.jsx b/components/TwoGpuMemoryFitAnimation.jsx new file mode 100644 index 000000000..953a27cef --- /dev/null +++ b/components/TwoGpuMemoryFitAnimation.jsx @@ -0,0 +1,353 @@ +const scenarios = [ + { + key: 'one', + verdict: 'fail', + title: 'One card', + flag: '-24.42 GiB', + note: 'vLLM refuses to start', + parts: [ + { label: 'weights', value: '61.03 GiB', width: '151%', color: '#ef4444' }, + ], + }, + { + key: 'two', + verdict: 'pass', + title: 'Two cards, TP=2', + flag: '67,296 tokens', + note: '2.05x concurrency at 32k context', + parts: [ + { label: 'weights', value: '30.59 GiB', width: '75.6%', color: '#0098cc' }, + { label: 'KV cache', value: '8.22 GiB', width: '20.3%', color: '#2bb534' }, + ], + }, +]; + +export default function TwoGpuMemoryFitAnimation() { + return ( +
+ +
+

Memory fit animation

+

+ Qwen3-32B in BF16 against one A40 budget, then two +

+

+ These are the numbers vLLM actually reported on my run. A 61.02 GiB checkpoint has nowhere + to go on a single 45 GiB card, and the failure is not subtle: the KV cache budget comes out + negative before a single token is stored. +

+ +
+ {scenarios.map((scenario) => ( +
+
+ + {scenario.title} + {scenario.note} + + {scenario.flag} +
+ +
+ per-card budget + ceiling 40.47 GiB +
+ +
+
+ {scenario.parts.map((part, index) => ( +
+ + {part.label} {part.value} + +
+ ))} +
+
+ +
+ {scenario.verdict === 'fail' + ? 'Model loading took 61.03 GiB\nAvailable KV cache memory: -24.42 GiB\nValueError: No available memory for the cache blocks.' + : 'Worker_TP0 Model loading took 30.59 GiB\nWorker_TP1 Model loading took 30.59 GiB\nGPU KV cache size: 67,296 tokens'} +
+
+ ))} + +
+
+ KV per token + 256 KiB + + 2 x 64 layers x 8 kv heads x 128 head_dim x 2 bytes + +
+
+ Per card at TP=2 + 128 KiB + + 4 of the 8 kv heads land on each card, so the cache is divided, not copied + +
+
+ Predicted vs reported + 67,338 / 67,296 + + 8.22 GiB divided by 128 KiB, against what vLLM printed + +
+
+
+
+
+ Captured on two RTX PRO 6000 Blackwell cards held to a 40.47 GiB budget with + --gpu-memory-utilization 0.426, which matches a 45 GiB A40 running at 0.90. Weight splitting + does not depend on the architecture, so these memory figures carry over to A40 directly. +
+
+ ); +} diff --git a/components/TwoGpuTensorSplitAnimation.jsx b/components/TwoGpuTensorSplitAnimation.jsx new file mode 100644 index 000000000..f1d997515 --- /dev/null +++ b/components/TwoGpuTensorSplitAnimation.jsx @@ -0,0 +1,414 @@ +const stages = [ + { key: 'broadcast', label: 'X arrives', detail: 'same full copy on both cards' }, + { key: 'column', label: 'Column-parallel', detail: 'each card owns half the columns of A' }, + { key: 'local', label: 'Activation stays local', detail: 'SiLU is elementwise, no comms' }, + { key: 'row', label: 'Row-parallel', detail: 'each card gets a partial sum' }, + { key: 'reduce', label: 'All-reduce', detail: 'partial sums added, both cards get Z' }, +]; + +export default function TwoGpuTensorSplitAnimation() { + return ( +
+ +
+

Tensor parallelism animation

+

+ One MLP block, split down the middle across two cards +

+

+ The weights are split and the activations are not. Each card owns half the columns of the + first matrix and half the rows of the second, so all the maths stays local until the very + end, where one all-reduce adds the two partial sums together. +

+ +
+
+ input X, hidden_size 5120 + replicated, both cards hold the same full copy +
+ +
+ {[0, 1].map((gpu) => ( +
+
+ GPU {gpu} + {gpu === 0 ? 'heads 0-31' : 'heads 32-63'} +
+ + + ))} +
+ +
+ + +
+ full output Z, identical on both cards + next layer starts from here +
+
+ +
+ {stages.map((stage, index) => ( +
+ Step {index + 1} + {stage.label} + {stage.detail} +
+ ))} +
+
+
+ Shapes are Qwen3-32B: hidden_size 5120, intermediate_size 25600 halved to 12800 per card, 64 + attention heads halved to 32. Attention splits the same way, so a full layer costs two + all-reduces, not one. +
+
+ ); +} diff --git a/components/TwoGpuTpVsPpAnimation.jsx b/components/TwoGpuTpVsPpAnimation.jsx new file mode 100644 index 000000000..6e9386fe9 --- /dev/null +++ b/components/TwoGpuTpVsPpAnimation.jsx @@ -0,0 +1,373 @@ +const results = [ + { metric: 'tok/s at concurrency 1', tp: '36.41', pp: '21.00', win: 'tp' }, + { metric: 'tok/s at concurrency 32', tp: '496.60', pp: '487.56', win: 'tie' }, + { metric: 'median TTFT at 32', tp: '3892 ms', pp: '2468 ms', win: 'pp' }, + { metric: 'KV cache tokens', tp: '67,296', pp: '56,640', win: 'tp' }, +]; + +export default function TwoGpuTpVsPpAnimation() { + return ( +
+ +
+

TP versus PP animation

+

+ Two ways to cut the same model, and what each one costs +

+

+ Both modes solve the fitting problem, so the choice is purely about speed. Tensor + parallelism keeps both cards busy on every token and pays 128 all-reduces for it. Pipeline + parallelism barely communicates at all, but runs like a relay race. +

+ +
+
+

+ Tensor parallelism, TP=2 + every layer is split, both cards work on every token +

+
+
+ GPU 0 + all 64 layers, heads 0-31, 30.59 GiB +
+
+ +
+ GPU 1 + all 64 layers, heads 32-63, 30.59 GiB +
+
+

+ Wins decode. Both cards contribute memory bandwidth to the same token, so + concurrency 1 is 73% faster. Pays for it in prefill, where each all-reduce carries 10 MB + instead of 10 KB. +

+
+ +
+

+ Pipeline parallelism, PP=2 + the stack is cut by layer, one activation handoff +

+
+
+ GPU 0 + layers 0-31, all 8 kv heads, 30.52 GiB +
+
+ +
+ GPU 1 + layers 32-63, all 8 kv heads, 30.52 GiB +
+
+

+ Wins prefill. Almost no communication, so time to first token is 37% better at + concurrency 32. But with one request in flight a card is always idle, and the extra + pipeline buffers cost you 19% of the KV cache. +

+
+
+ +
+
+
Measured
+
TP=2
+
PP=2
+
+ {results.map((row) => ( +
+
{row.metric}
+
+ {row.tp} +
+
+ {row.pp} +
+
+ ))} +
+
+
+ Measured with vllm bench serve, 1024 input and 256 output tokens, on two PCIe-connected cards + with no NVLink. The concurrency 32 throughput gap is 1.9%, which is close enough to noise that + I would call it a tie rather than a win. +
+
+ ); +} diff --git a/content/blog/running-a-single-llm-across-two-gpus-with-vllm.md b/content/blog/running-a-single-llm-across-two-gpus-with-vllm.md new file mode 100644 index 000000000..6209c0218 --- /dev/null +++ b/content/blog/running-a-single-llm-across-two-gpus-with-vllm.md @@ -0,0 +1,363 @@ +--- +title: "Running a single LLM across two GPUs with vLLM" +seoTitle: "Running a single LLM across two GPUs with vLLM" +seoDescription: "How tensor parallelism splits one model's weights across two cards, the memory math that tells you if it fits, and measured TP versus PP numbers on a pair of GPUs with no NVLink." +datePublished: 2026-08-18T10:00:00.000Z +slug: running-a-single-llm-across-two-gpus-with-vllm +author: saiyam-pathak +cover: /img/blog/running-a-single-llm-across-two-gpus-with-vllm/cover.png +tags: ["vllm", "gpu", "nvidia", "tensor-parallelism"] +draft: true +--- + +Someone asked me this in a thread the other day, and it is such a good question that it deserves a full walkthrough: + +> Has anyone hosted a single LLM by splitting weights across 2 GPUs and served it through vLLM or another inference engine? I have a couple of A40 with 45GB usable VRAM. And want to host the BF16 variant as-is, like we have an RTX PRO 6000, you know, like on 2 cards. How can I do it and how does it work fundamentally, like are the weights split or what happens? + +Three questions hiding in there, so let's take them in order. Can you do it? Yes. How do you do it? One flag, mostly. And what actually happens to the weights? That is the interesting part, and it is where most people's mental model is a bit off. + +## What you will get from this post + +- The memory math that tells you whether your model fits on two cards, before you download 60 GB +- What tensor parallelism actually does to a weight matrix, layer by layer +- The exact vLLM commands, with real terminal output from a real run +- Why a pair of cards without an NVLink bridge might be faster with pipeline parallelism, and how to measure that yourself +- The A40-specific catches, because Ampere has one limitation that changes your options + +## The setup I tested on + +I need to be upfront about the hardware, because it matters for how you read the numbers. + +I do not have a pair of A40s. What I do have access to is a box with 8x NVIDIA RTX PRO 6000 Blackwell Server Edition cards, so I borrowed two of them and deliberately handicapped them to behave like A40s for the part that matters most, which is the memory budget. An A40 gives you roughly 45 GiB of usable VRAM, and at the usual `--gpu-memory-utilization 0.90` that leaves vLLM a budget of about 40.5 GiB per card. On a 95.01 GiB Blackwell card, the same 40.5 GiB budget is `--gpu-memory-utilization 0.426`, so that is what I used everywhere below. + +There is one thing I did not have to fake. Let's look at the interconnect: + +```console +$ nvidia-smi topo -m + GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 CPU Affinity NUMA Affinity +GPU0 X SYS SYS SYS SYS SYS SYS SYS 48-55,176-183 6 +GPU1 SYS X SYS SYS SYS SYS SYS SYS 32-39,160-167 4 +GPU2 SYS SYS X SYS SYS SYS SYS SYS 0-7,128-135 0 +GPU3 SYS SYS SYS X SYS SYS SYS SYS 16-23,144-151 2 +GPU4 SYS SYS SYS SYS X SYS SYS SYS 112-119,240-247 14 +GPU5 SYS SYS SYS SYS SYS X SYS SYS 96-103,224-231 12 +GPU6 SYS SYS SYS SYS SYS SYS X SYS 64-71,192-199 8 +GPU7 SYS SYS SYS SYS SYS SYS SYS X 80-87,208-215 10 + +Legend: + X = Self + SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI) + NV# = Connection traversing a bonded set of # NVLinks +``` + +Every pair says `SYS`, which means there is no NVLink anywhere on this box. Every GPU-to-GPU hop goes across PCIe and then across the CPU's own interconnect between NUMA nodes. If your two A40s do not have an NVLink bridge physically installed between them, and most people's do not, then you are in exactly this situation. That turns out to be the most important fact in this whole post, and I'll come back to it. + +The software, pinned: + +```console +vllm 0.27.1 +torch 2.13.0+cu130 cuda 13.0 +driver 610.43.02 +GPU 0: NVIDIA RTX PRO 6000 Blackwell Server Edition 95.01 GiB sm_120 +GPU 1: NVIDIA RTX PRO 6000 Blackwell Server Edition 95.01 GiB sm_120 +p2p 0<->1 True +``` + +For the model I picked **Qwen3-32B** in BF16, because it is the honest version of this question. At 32.8B parameters in bfloat16 it genuinely does not fit on one 45 GiB card, but it does fit on two, so the second card is doing real work rather than being a nice-to-have. + +## Why one card is not enough, in numbers + +Before touching any flags, let's do the arithmetic, because you can answer "will this fit" on paper in about a minute. + +A BF16 weight is 2 bytes. So the weights alone are `params x 2 bytes`. For Qwen3-32B that is about 61 GiB, and vLLM tells you the same thing when it reads the checkpoint: + +```console +INFO [weight_utils.py:867] Filesystem type for checkpoints: EXT4. Checkpoint size: 61.02 GiB. Available RAM: 1186.67 GiB. +``` + +61.02 GiB of weights against a 40.5 GiB budget on a single card. That is not close, and it is worth actually watching it fail, because the error message vLLM gives you here is one you will meet again: + +```console +$ docker run --gpus '"device=1"' ... vllm/vllm-openai:latest Qwen/Qwen3-32B \ + --tensor-parallel-size 1 --gpu-memory-utilization 0.426 --max-model-len 32768 + +INFO [model_runner.py:329] Model loading took 61.03 GiB and 16.146783 seconds +INFO [gpu_worker.py:563] Available KV cache memory: -24.42 GiB +ValueError: No available memory for the cache blocks. Try increasing `gpu_memory_utilization` +when initializing the engine. +``` + +**Available KV cache memory: -24.42 GiB.** I love this line. vLLM loaded the weights, then subtracted them and its activation overhead from the budget, and found it was 24 GiB in the hole before storing a single token of context. The suggestion to increase `gpu_memory_utilization` is a red herring here, since there is no value of it that makes 61 GiB fit in 45. + +{{two-gpu-memory-fit-animation}} + +So that is the wall. Now let's get over it. + +## What actually happens to the weights + +### First, the thing NVLink does not do + +Your question mentioned wanting the pair to behave "like we have an RTX PRO 6000", so let me clear up the most common misconception before anything else, because NVIDIA's own datasheet invites it. That datasheet advertises "48 GB GDDR6 memory with NVLink" and says it is "scalable up to 96 GB with NVLink", which certainly reads like two bridged cards turn into one 96 GB card. They do not. The footnote on that same page is where the real story is: + +> Connecting two NVIDIA A40 cards with NVLink to scale performance and memory capacity to 96 GB is only possible if your application supports NVLink technology. Please contact your application provider to confirm their support for NVLink. + +"Only possible if your application supports it" is carrying a lot of weight in that sentence. There is no mode, bridge or no bridge, where CUDA presents your two 48 GB cards to vLLM as a single 96 GB device. Each GPU keeps its own separate memory, and some piece of software has to deliberately cut the model up and coordinate the halves. NVLink never creates the pool, it only makes the conversation between the halves faster. Configuring that software is what the rest of this post is about. + +### Now the split itself + +The short answer to your actual question is **yes, the weights are genuinely split, and it happens inside each layer, not between layers.** + +The technique is called **tensor parallelism**, and it comes from the Megatron-LM paper by Shoeybi and colleagues at NVIDIA. The idea is that a transformer is mostly a stack of big matrix multiplications, and a big matrix multiplication can be cut into pieces that live on different GPUs. + +Take the MLP block in a layer. It is two matrix multiplies with a nonlinearity in between: `Y = GeLU(X x A)` then `Z = Y x B`. You could split the first matrix `A` by rows, but then you would have to glue the pieces back together before applying GeLU, because GeLU is nonlinear and `GeLU(a + b)` is not `GeLU(a) + GeLU(b)`. So Megatron splits `A` **column-wise** instead, and as the paper puts it, "the partitioning allows the GeLU nonlinearity to be independently applied to the output of each partitioned GEMM". Each GPU produces its own complete columns of `Y`, applies GeLU locally, and nobody has to talk to anybody. + +Then the second matrix `B` is split **row-wise**, which lines up perfectly with the column split of the first one. Each GPU multiplies its slice of `Y` by its slice of `B` and gets a partial sum of the final answer. Now, and only now, the GPUs have to add their partial sums together. That is one **all-reduce**. + +Drawn out, one MLP block across two cards looks like this: + +{{two-gpu-tensor-split-animation}} + +Notice what is split and what is not. The **weights** are split, each card holding half of `A` and half of `B` and never seeing the other half. The **activations** flowing through are replicated, so both cards start from the same full copy of `X` and both end up with the same full copy of `Z` after the all-reduce. That is the trade at the heart of tensor parallelism: you halve the weight memory, and you pay for it by keeping the activations in sync. + +A quick note if you go and read the Megatron paper, because the shapes have moved on since 2019. The paper describes a two-matrix MLP with a GeLU in the middle, which is what GPT-2 era models used. Qwen3 and most current models use SwiGLU instead, which has three matrices: `gate_proj`, `up_proj` and `down_proj`, and `silu` rather than GeLU (you can see `"hidden_act": "silu"` in the config below). The partitioning logic carries over unchanged though. `gate_proj` and `up_proj` are both column-parallel, they get multiplied together elementwise which stays local, and `down_proj` is row-parallel and produces the partial sums. Three matrices instead of two, still exactly one all-reduce. + +Attention works the same way, and the split is even more intuitive. The Q, K and V projections are cut column-wise "such that the matrix multiply corresponding to each attention head is done locally on one GPU". Qwen3-32B has 64 attention heads, so with two GPUs each card simply owns 32 whole heads and computes attention for them start to finish with no communication at all. The output projection is then row-wise, which again produces partial sums, which again need one all-reduce. + +Two matrix-multiply blocks, one all-reduce each. The paper states it plainly: this "enables us to perform all GEMMs in a simple transformer layer using only two all-reduces in the forward path and two in the backward path". Inference is forward-only, so for us it is **two all-reduces per layer**. + +Qwen3-32B has 64 layers. So generating a single token means **128 all-reduces**, in sequence, one after another, because layer 5 cannot start until layer 4 has finished exchanging. Hold that thought. + +### The KV cache splits too, and that is a bonus + +This part people often miss. Because each GPU owns a subset of the attention heads, it only needs to cache keys and values for *its own* heads. The KV cache is split right along with the weights. + +Qwen3-32B uses grouped-query attention with 8 key/value heads, so the cache per token for the whole model is: + +``` +2 (K and V) x 64 layers x 8 kv_heads x 128 head_dim x 2 bytes = 262,144 bytes = 256 KiB per token +``` + +With two GPUs, each card holds 4 of those 8 KV heads, so each card stores 128 KiB per token instead of the full 256 KiB. The cache is not duplicated across cards, it is divided, so the memory you free up by splitting the weights turns into context capacity rather than being eaten by a second copy of the cache. That is why the second card buys you two things at once, and it is the number I will check against reality further down. + +### The divisibility rule you need to check first + +Because heads are handed out whole, **your tensor parallel size has to divide your head counts**. Before you commit to a model, open its `config.json` and check. For Qwen3-32B: + +```json +{ + "num_hidden_layers": 64, + "hidden_size": 5120, + "num_attention_heads": 64, + "num_key_value_heads": 8, + "head_dim": 128, + "intermediate_size": 25600, + "torch_dtype": "bfloat16" +} +``` + +64 attention heads divided by 2 is 32, 8 KV heads divided by 2 is 4, and `intermediate_size` 25600 divided by 2 is 12800. All clean, so TP=2 will work. This is why some models refuse to run at TP=8 or TP=3 while being perfectly happy at TP=2, and it is a config-file question, not a mystery. + +## Doing it with vLLM + +After all that theory, the actual change is one flag. Let's run it: + +```bash +docker run -d --name vllm-tp2 \ + --gpus '"device=1,4"' --ipc=host -p 8101:8000 \ + -v /root/.cache/huggingface:/root/.cache/huggingface \ + -e HF_HUB_OFFLINE=1 -e HF_HOME=/root/.cache/huggingface \ + vllm/vllm-openai:latest Qwen/Qwen3-32B \ + --tensor-parallel-size 2 \ + --gpu-memory-utilization 0.426 \ + --max-model-len 32768 \ + --port 8000 +``` + +`--tensor-parallel-size 2` is the whole trick. On a real pair of A40s you would use `--gpu-memory-utilization 0.90` instead of my emulated `0.426`, and everything else stays the same. + +Two container details that will bite you if you skip them. `--ipc=host` matters because the tensor parallel workers are separate processes that talk over shared memory, and Docker's default 64 MB `/dev/shm` is not enough. And `--gpus '"device=1,4"'` with that exact nested quoting is how you hand Docker a specific pair of cards; inside the container they are renumbered 0 and 1. + +Now the proof that the split is real. Here is what vLLM logs on startup: + +```console +(Worker_TP0 pid=611) INFO [model_runner.py:329] Model loading took 30.59 GiB and 20.690370 seconds +(Worker_TP1 pid=612) INFO [model_runner.py:329] Model loading took 30.59 GiB and 20.456472 seconds +(Worker_TP0 pid=611) INFO [gpu_worker.py:563] Available KV cache memory: 8.22 GiB +(EngineCore pid=411) INFO [kv_cache_utils.py:2235] GPU KV cache size: 67,296 tokens +(EngineCore pid=411) INFO [kv_cache_utils.py:2236] Maximum concurrency for 32,768 tokens per request: 2.05x +``` + +**30.59 GiB on each worker**, and 30.59 doubled is 61.18, which is our 61.02 GiB checkpoint plus a rounding hair. There are two workers, `Worker_TP0` and `Worker_TP1`, one per GPU, each holding exactly half the model. The weights are not replicated. They are cut in half. + +And from outside the container: + +```console +$ nvidia-smi --query-gpu=index,memory.used --format=csv,noheader +1, 45171 MiB +4, 45171 MiB +``` + +Identical to the megabyte on both cards, which is what an even split looks like. + +Let's also check that the KV math I did earlier actually predicts reality. Each card reported 8.22 GiB free for cache, and I said each card stores 128 KiB per token: + +``` +8.22 GiB / 128 KiB = 67,338 tokens +``` + +vLLM reported 67,296. That is a match to within the rounding of "8.22", and it means you can predict your own context capacity on paper before you ever start the server. With `--max-model-len 32768`, 67,296 tokens of cache is 2.05 full-length requests in flight, which is exactly the `2.05x` vLLM printed. + +## What those all-reduces actually cost you + +So we are done, right? Two cards, model fits, `-tp 2`, ship it. + +Not quite. Remember those 128 sequential all-reduces per token. Let's think about how big each one actually is. An all-reduce after the attention or MLP block has to exchange a tensor of shape `[tokens_in_batch, hidden_size]`. At `hidden_size` 5120 in BF16, with a single request decoding one token at a time, that is: + +``` +1 token x 5120 x 2 bytes = 10,240 bytes = 10 KB +``` + +Ten kilobytes. That is nothing. The A40 datasheet lists its interconnect as "NVIDIA NVLink 112.5 GB/s (bidirectional), PCIe Gen4: 64GB/s", so NVLink is a bit under twice the bandwidth of the PCIe path. Neither number matters here, though, because you are not moving enough data to care about bandwidth at all. What you are paying is **latency**, 128 times per token, and every one of those hops on a no-NVLink box goes out over PCIe and across the CPU's NUMA interconnect. + +This is why vLLM's own documentation gives advice that surprises people. Straight from their parallelism guide: + +> if the GPUs on the node do not have NVLINK interconnect (e.g. L40S), leverage pipeline parallelism instead of tensor parallelism for higher throughput and lower communication overhead. + +**Pipeline parallelism** splits the model a completely different way: by layers, not inside them. With PP=2 and 64 layers, GPU 0 gets layers 0 to 31 and GPU 1 gets layers 32 to 63. Your memory problem is solved just as well, since each card still holds half the weights. But the communication is utterly different. Instead of 128 all-reduces per token, GPU 0 finishes its 32 layers and hands one activation tensor to GPU 1, once. One point-to-point send instead of 128 collectives. + +The cost is that PP is a relay race. With a single request in flight, GPU 1 sits idle while GPU 0 works, then GPU 0 sits idle while GPU 1 works, so you are using half your silicon at any moment. vLLM notes this too, saying that increasing pipeline parallel size "may cause latency penalties". PP pays off when you have enough concurrent requests to keep both stages busy at once, which is what continuous batching gives you. + +{{two-gpu-tp-vs-pp-animation}} + +So the honest answer is that TP and PP trade against each other, the crossover depends on your interconnect and your concurrency, and you should measure it on your own box. Which is what I did. + +## TP=2 vs PP=2, measured + +Switching to pipeline parallelism is the same kind of one-flag change: + +```bash +docker run -d --name vllm-pp2 \ + --gpus '"device=1,4"' --ipc=host -p 8102:8000 \ + -v /root/.cache/huggingface:/root/.cache/huggingface \ + -e HF_HUB_OFFLINE=1 -e HF_HOME=/root/.cache/huggingface \ + vllm/vllm-openai:latest Qwen/Qwen3-32B \ + --pipeline-parallel-size 2 \ + --gpu-memory-utilization 0.426 \ + --max-model-len 32768 \ + --port 8000 +``` + +And it splits the weights just as effectively, which you can see in the workers being named `PP` instead of `TP` now: + +```console +(Worker_PP0 pid=611) INFO [model_runner.py:329] Model loading took 30.52 GiB and 9.017490 seconds +(Worker_PP1 pid=612) INFO [model_runner.py:329] Model loading took 30.52 GiB and 9.547118 seconds +(Worker_PP0 pid=611) INFO [gpu_worker.py:563] Available KV cache memory: 6.92 GiB +(EngineCore pid=411) INFO [kv_cache_utils.py:2235] GPU KV cache size: 56,640 tokens +(EngineCore pid=411) INFO [kv_cache_utils.py:2236] Maximum concurrency for 32,768 tokens per request: 1.73x +``` + +### The memory difference shows up first + +Look at the KV cache: **56,640 tokens with PP against 67,296 with TP**, on identical hardware and an identical memory budget. Pipeline parallelism gave me 18.8% less usable context. + +The per-token cost per card is actually the same in both modes, which is a nice coincidence worth understanding. Under TP each card holds all 64 layers but only 4 of the 8 KV heads. Under PP each card holds all 8 KV heads but only 32 layers. `64 x 4` and `32 x 8` are the same number, so both come out at 128 KiB per token per card. + +The difference is pure overhead. Subtracting weights and cache from the 40.47 GiB budget, TP left 1.66 GiB of overhead per card and PP left 3.03 GiB, because the pipeline needs extra buffers for activations in flight between the stages. That overhead comes straight out of your context capacity. + +There is a second, smaller difference worth knowing about. Tensor parallelism divided the memory perfectly evenly, while pipeline parallelism did not: + +```console +# TP=2 +1, 45171 MiB +4, 45171 MiB + +# PP=2 +1, 40701 MiB +4, 43667 MiB +``` + +Identical to the megabyte under TP, and about 3 GB apart under PP. That is because a layer split cannot be perfectly even when the ends of the model are not symmetric: the first stage carries the token embedding, the last stage carries the final norm and the language modelling head. It rarely matters at PP=2 on matched cards, but it is exactly the kind of thing that bites you if you ever try to split across two cards of *different* sizes, since your headroom is set by whichever card ends up fuller. + +### Now the throughput + +Same benchmark for both, `vllm bench serve` with a random dataset at 1024 input and 256 output tokens, `--ignore-eos` so every request generates exactly 256 tokens, run at concurrency 1 and again at concurrency 32: + +```bash +vllm bench serve --model Qwen/Qwen3-32B --base-url http://localhost:8000 \ + --dataset-name random --random-input-len 1024 --random-output-len 256 \ + --max-concurrency 1 --num-prompts 16 --seed 42 --ignore-eos +``` + +Before the table, one caveat that I want to put right next to the numbers rather than bury at the end. The memory results above transfer to your A40s directly, because I matched the memory budget on purpose and weight splitting does not care what architecture it runs on. The **throughput** results do not transfer as cleanly, and not simply because Blackwell is faster in absolute terms. The ratio between compute time and communication time is what decides where TP stops winning, and two things move that ratio in opposite directions on your hardware: an A40's slower compute makes each layer's math take longer, which hides the all-reduce latency and helps TP, while PCIe Gen4 instead of Gen5 makes each all-reduce cost more, which hurts TP. I cannot tell you which effect dominates on your box. So read the shape of the result below, not the absolute tok/s, and run the same two commands yourself. + +| Metric | TP=2 | PP=2 | Winner | +|---|---|---|---| +| **Concurrency 1** | | | | +| Output token throughput | 36.41 tok/s | 21.00 tok/s | TP by 73% | +| Median TPOT (per-token latency) | 26.38 ms | 46.96 ms | TP by 44% | +| Median TTFT (time to first token) | 296.37 ms | 208.57 ms | PP by 30% | +| **Concurrency 32** | | | | +| Output token throughput | 496.60 tok/s | 487.56 tok/s | TP by 1.9% | +| Median TPOT | 47.22 ms | 56.40 ms | TP by 16% | +| Median TTFT | 3892.42 ms | 2468.40 ms | PP by 37% | +| Benchmark duration | 65.99 s | 67.21 s | TP by 1.8% | +| **Capacity** | | | | +| KV cache | 67,296 tokens | 56,640 tokens | TP by 19% | + +Let's read what actually happened here, because it is not the clean story the documentation led me to expect. + +**At concurrency 1, tensor parallelism wins convincingly**, 36.41 tok/s against 21.00, and that is exactly the relay-race effect. With one request in flight, PP has one card working and one card waiting at all times, so you get roughly one card's worth of decode speed. TP has both cards grinding on every single token, and since decode speed is mostly about memory bandwidth, using two cards' worth of bandwidth on one request is a real and large win. This is the thing PP fundamentally cannot give you. + +**At concurrency 32, the two are effectively tied.** 496.60 against 487.56 tok/s is a 1.9% gap, which is close enough to run-to-run noise that I would not make a decision on it. This is where I have to be straight with you: vLLM's docs say that without NVLink you should "leverage pipeline parallelism instead of tensor parallelism for higher throughput", and on this box **that did not reproduce**. PP never got ahead on throughput, it just caught up. I would guess that is because these cards sit on PCIe Gen5 rather than Gen4, so the all-reduces are cheaper than the guidance assumes, and because at concurrency 32 the all-reduce payload is 32 tokens wide rather than 1, which uses the link far more efficiently. On your Gen4 A40s the gap will be less favourable to TP than what I measured. Whether it crosses over, I genuinely do not know, which is the whole reason I am telling you to measure rather than handing you a verdict. + +**The one place PP clearly wins is time to first token**, by 30% at concurrency 1 and 37% at concurrency 32. That one took me a moment to see, and it makes sense once you think about payload sizes. Prefill processes your whole 1024-token prompt at once, so each of TP's 128 all-reduces is moving `1024 x 5120 x 2 bytes`, about 10 MB, not the 10 KB a single decode step moves. Suddenly you *are* bandwidth-bound, and 128 ten-megabyte collectives over PCIe is a real cost. PP moves one activation tensor between stages and skips all of it. + +So the shape of the answer, on a box with no NVLink: + +- Interactive, low concurrency, one user at a time: **use TP**. It is not close. +- High concurrency batch throughput: **either**, they tie, so pick TP for the extra 19% of KV cache. +- Long prompts where users are staring at a spinner waiting for the first token: **PP is worth testing**, it was meaningfully faster at prefill in both runs. + +For your A40s I would still start with `--tensor-parallel-size 2`, because it won or tied on every throughput measure here and it gives you more context capacity. Then run these exact two benchmarks with `--pipeline-parallel-size 2` and see whether your slower interconnect changes the verdict. + +## The A40-specific things to know + +A few points that apply to your cards specifically rather than to multi-GPU serving in general. + +**An NVLink bridge is available, and it is worth hunting for.** The A40 does support NVLink, at 112.5 GB/s bidirectional between a pair, via a physical bridge connector you install between two cards. If you have two A40s in one chassis and you can get the bridge, do it before you spend a week tuning flags. It turns the `SYS` line in your topology into `NV#`, and since tensor parallelism already won on my bridge-less box, cheaper all-reduces can only widen that lead and take the decision off your plate entirely. Check what you have today with `nvidia-smi topo -m`, exactly as I did above. + +**FP8 will not save you the way it saves a newer card.** This is the Ampere limitation that changes your options. vLLM's docs are explicit: "FP8 computation is supported on NVIDIA GPUs with compute capability >= 8.9 (Ada Lovelace, Hopper)." The A40 is compute capability 8.6, so it misses that by one minor version. You are not entirely locked out, because "Turing/Ampere GPUs are supported for W8A16 (weight-only FP8) utilizing Marlin kernels", which stores weights at 8 bits and computes in 16. That is a genuinely useful trick for memory: it would take Qwen3-32B's weights from 61 GiB to roughly 31 GiB and let it run on a **single** A40. But you do not get the compute speedup that an Ada or Blackwell card gets from FP8, and you did say you want BF16 as-is, so I mention it only as the escape hatch it is. + +**Both cards read the whole checkpoint.** A small operational note from vLLM's docs that surprises people watching disk I/O: with tensor parallelism "each process will read the whole model and split it into chunks", so startup reads scale with your TP size rather than being divided by it. + +## So should you just buy one RTX PRO 6000 instead? + +Your question framed it as wanting your two A40s to behave "like we have an RTX PRO 6000", so let's compare properly, because on capacity they look similar and on behaviour they are not. + +Two A40s give you about 90 GiB of aggregate VRAM. A single RTX PRO 6000 Blackwell gives 96 GiB on one card. Similar pool, and for pure "does the model fit" purposes they are close to equivalent. + +The differences that actually decide it: + +- **A single card has no interconnect tax at all.** No all-reduces, no PCIe hops, no NVLink bridge to source, no TP-versus-PP tuning. Everything in this post stops being your problem. +- **Blackwell has FP8 and FP4, Ampere has neither.** That is the bigger gap, honestly, and it decides what fits rather than only how fast it runs. A model you can only serve in BF16 on A40s might serve in FP8 on one Blackwell card, in half the memory, at full speed. +- **Two cards give you more aggregate memory bandwidth.** Two A40s is 2 x 696 GB/s of it, and decode speed is largely a memory-bandwidth story. With tensor parallelism you genuinely do get to use both cards' bandwidth on one request, which is a real advantage of TP that PP does not give you. + +My take: if you already own the two A40s, use them, because tensor parallelism works and the setup above is maybe twenty minutes of work. Find out whether you can get the NVLink bridge. If you are spending new money and you are choosing between two more A40s and one Blackwell card, buy the single newer card, mostly for FP8 rather than for avoiding the multi-GPU complexity. + +## Wrapping up + +The mental model to walk away with is that tensor parallelism cuts every big matrix in every layer down the middle, hands each GPU whole attention heads, and pays for it with two all-reduces per layer. That is why it fixes your memory problem completely and your throughput problem only conditionally, because those all-reduces are cheap over NVLink and expensive over PCIe. Pipeline parallelism cuts the stack by layers instead, communicates almost nothing, and needs concurrency to keep both cards busy. + +For your two A40s, start with `--tensor-parallel-size 2`, run the same two benchmarks I ran above at your real concurrency, then try `--pipeline-parallel-size 2` and keep whichever wins. Both of them solve the fitting problem, so you are only choosing on speed, and it is a ten-minute experiment on your own hardware which beats anyone's opinion including mine. + +Give it a try and let me know how it goes, especially if you get an NVLink bridge on those A40s, because I would love to see the before-and-after numbers on real Ampere silicon. + +## Credits and references + +- The tensor parallel scheme comes from **Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism** by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro: [arxiv.org/abs/1909.08053](https://arxiv.org/abs/1909.08053) +- vLLM parallelism and scaling guide: [docs.vllm.ai/en/latest/serving/parallelism_scaling.html](https://docs.vllm.ai/en/latest/serving/parallelism_scaling.html) +- vLLM conserving memory and optimization docs: [docs.vllm.ai/en/latest/configuration/conserving_memory.html](https://docs.vllm.ai/en/latest/configuration/conserving_memory.html) and [optimization.html](https://docs.vllm.ai/en/latest/configuration/optimization.html) +- vLLM FP8 quantization support matrix: [docs.vllm.ai/en/latest/features/quantization/llm_compressor/fp8/](https://docs.vllm.ai/en/latest/features/quantization/llm_compressor/fp8/) +- NVIDIA A40 datasheet, for the 48 GB GDDR6, 696 GB/s and 112.5 GB/s NVLink figures: [nvidia.com A40 datasheet](https://images.nvidia.com/content/Solutions/data-center/a40/nvidia-a40-datasheet.pdf) +- Qwen3-32B model card and config: [huggingface.co/Qwen/Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) diff --git a/lib/markdown.js b/lib/markdown.js index 56f0d4988..23686f632 100644 --- a/lib/markdown.js +++ b/lib/markdown.js @@ -23,6 +23,9 @@ import HAMiBlastRadiusAnimation from '@/components/HAMiBlastRadiusAnimation'; import HAMiRequestFlowAnimation from '@/components/HAMiRequestFlowAnimation'; import HAMiSlotMathAnimation from '@/components/HAMiSlotMathAnimation'; import DynamicMigLifecycleAnimation from '@/components/DynamicMigLifecycleAnimation'; +import TwoGpuTensorSplitAnimation from '@/components/TwoGpuTensorSplitAnimation'; +import TwoGpuMemoryFitAnimation from '@/components/TwoGpuMemoryFitAnimation'; +import TwoGpuTpVsPpAnimation from '@/components/TwoGpuTpVsPpAnimation'; import CodeBlock from '@/components/CodeBlock'; const BLOG_SHORTCODES = { @@ -41,6 +44,9 @@ const BLOG_SHORTCODES = { '{{hami-request-flow-animation}}': 'hami-request-flow-animation', '{{hami-slot-math-animation}}': 'hami-slot-math-animation', '{{dynamic-mig-lifecycle-animation}}': 'dynamic-mig-lifecycle-animation', + '{{two-gpu-tensor-split-animation}}': 'two-gpu-tensor-split-animation', + '{{two-gpu-memory-fit-animation}}': 'two-gpu-memory-fit-animation', + '{{two-gpu-tp-vs-pp-animation}}': 'two-gpu-tp-vs-pp-animation', }; function remarkBlogShortcodes() { 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+GPU 0 +weights 30.59 GiB +KV cache 8.22 GiB +heads 0-31 + + + +GPU 1 +weights 30.59 GiB +KV cache 8.22 GiB +heads 32-63 + + + + + + +all- +reduce +GPU KV cache size: +67,296 tokens +2.05x concurrency + +QWEN3-32B BF16 - 61.02 GiB CHECKPOINT - vLLM 0.27.1 +2 x 128 all-reduces per token, no NVLink, measured not estimated +blog.kubesimplify.com + \ No newline at end of file diff --git a/scripts/gen-two-gpu-vllm-cover.mjs b/scripts/gen-two-gpu-vllm-cover.mjs new file mode 100644 index 000000000..5b95a70e2 --- /dev/null +++ b/scripts/gen-two-gpu-vllm-cover.mjs @@ -0,0 +1,239 @@ +// Excalidraw-style cover for the two-GPU vLLM article. +// Sketch helpers shared with scripts/gen-hami-diagrams.mjs. +import { mkdirSync, writeFileSync } from 'node:fs'; +import { join } from 'node:path'; + +let seed = 42; +const random = () => { + seed = (seed * 16807) % 2147483647; + return seed / 2147483647; +}; +const jitter = (amount) => (random() - 0.5) * amount * 2; + +const COLORS = { + ink: '#172033', + muted: '#5c677d', + green: { stroke: '#5d8f00', fill: '#d8f5a2' }, + blue: { stroke: '#1971c2', fill: '#a5d8ff' }, + violet: { stroke: '#862e9c', fill: '#eebefa' }, + orange: { stroke: '#d9480f', fill: '#ffd8a8' }, + red: { stroke: '#c92a2a', fill: '#ffc9c9' }, + teal: { stroke: '#087f5b', fill: '#b2f2bb' }, + gray: { stroke: '#495057', fill: '#e9ecef' }, +}; + +const FONT = 'Chalkboard SE, Comic Sans MS, sans-serif'; + +function roughLine(x1, y1, x2, y2, amount = 1.8) { + const middleX = (x1 + x2) / 2 + jitter(amount * 1.5); + const middleY = (y1 + y2) / 2 + jitter(amount * 1.5); + return `M ${(x1 + jitter(amount)).toFixed(1)} ${(y1 + jitter(amount)).toFixed(1)} Q ${middleX.toFixed(1)} ${middleY.toFixed(1)} ${(x2 + jitter(amount)).toFixed(1)} ${(y2 + jitter(amount)).toFixed(1)}`; +} + +class Sketch { + constructor(width, height, background = '#ffffff') { + this.width = width; + this.height = height; + this.background = background; + this.parts = []; + this.defs = []; + this.clipId = 0; + } + + add(value) { + this.parts.push(value); + } + + rect(x, y, width, height, options = {}) { + const { + stroke = COLORS.ink, + fill, + strokeWidth = 2.4, + dashed = false, + hachure = true, + radius = 7, + } = options; + + if (fill) { + if (hachure) { + // Hatch lines are clipped in math rather than with an SVG clipPath so + // the file renders identically in renderers without clipPath support. + const hatch = []; + for (let offset = -height; offset < width; offset += 11) { + const tMin = Math.max(0, -offset / height); + const tMax = Math.min(1, (width - offset) / height); + if (tMax - tMin < 0.05) continue; + const x1 = x + offset + height * tMin; + const y1 = y + height - height * tMin; + const x2 = x + offset + height * tMax; + const y2 = y + height - height * tMax; + hatch.push(roughLine(x1, y1, x2, y2, 1)); + } + this.add(``); + } else { + this.add(``); + } + } + + const points = [[x, y], [x + width, y], [x + width, y + height], [x, y + height]]; + for (let pass = 0; pass < 2; pass += 1) { + const path = points.map((point, index) => { + const next = points[(index + 1) % points.length]; + return roughLine(point[0], point[1], next[0], next[1], pass === 0 ? 2 : 1.2); + }).join(' '); + this.add(``); + } + } + + line(x1, y1, x2, y2, options = {}) { + const { stroke = COLORS.ink, strokeWidth = 2.4, dashed = false } = options; + this.add(``); + } + + arrow(x1, y1, x2, y2, options = {}) { + const { stroke = COLORS.ink, strokeWidth = 2.6, dashed = false } = options; + this.line(x1, y1, x2, y2, { stroke, strokeWidth, dashed }); + const angle = Math.atan2(y2 - y1, x2 - x1); + const length = 14; + for (const offset of [Math.PI * 0.82, -Math.PI * 0.82]) { + this.line( + x2, + y2, + x2 + length * Math.cos(angle + offset), + y2 + length * Math.sin(angle + offset), + { stroke, strokeWidth } + ); + } + } + + text(x, y, value, options = {}) { + const { + size = 22, + color = COLORS.ink, + anchor = 'middle', + weight = 500, + family = FONT, + } = options; + const safe = String(value) + .replace(/&/g, '&') + .replace(//g, '>'); + this.add(`${safe}`); + } + + lines(x, y, values, options = {}) { + const lineHeight = (options.size || 22) * (options.lineHeight || 1.28); + values.forEach((value, index) => this.text(x, y + index * lineHeight, value, options)); + } + + save(path) { + const svg = ` +${this.defs.join('')} + +${this.parts.join('\n')} +`; + writeFileSync(path, svg); + } +} + +const output = process.argv[2] || '.'; +mkdirSync(output, { recursive: true }); + + +const W = 1200; +const H = 630; +const sketch = new Sketch(W, H, '#fdfdfb'); + +// ── heading ────────────────────────────────────────────── +sketch.text(64, 84, 'One LLM, two GPUs', { size: 52, weight: 800, anchor: 'start' }); +sketch.text(64, 122, 'tensor parallelism splits every layer, not the stack', { + size: 23, + color: COLORS.muted, + anchor: 'start', +}); +sketch.line(64, 142, 700, 142, { stroke: COLORS.muted, strokeWidth: 1.6, dashed: true }); + +// ── left: one card fails ───────────────────────────────── +sketch.text(64, 190, 'ONE 45 GiB CARD', { size: 19, weight: 800, anchor: 'start', color: COLORS.red.stroke }); + +const boxY = 210; +sketch.rect(64, boxY, 300, 150, { stroke: COLORS.gray.stroke, fill: '#ffffff', hachure: false, dashed: true }); +sketch.text(214, boxY + 34, 'budget 40.47 GiB', { size: 17, color: COLORS.muted }); + +// the weights bar overflowing the box +sketch.rect(80, boxY + 52, 330, 62, { stroke: COLORS.red.stroke, fill: COLORS.red.fill }); +sketch.text(200, boxY + 80, 'weights 61.03 GiB', { size: 20, weight: 800, color: COLORS.red.stroke }); +sketch.text(200, boxY + 103, 'does not fit', { size: 16, color: COLORS.muted }); + +sketch.text(64, boxY + 182, 'Available KV cache memory:', { size: 17, anchor: 'start', color: COLORS.muted }); +sketch.text(64, boxY + 208, '-24.42 GiB', { size: 30, weight: 800, anchor: 'start', color: COLORS.red.stroke }); + +// ── middle divider ─────────────────────────────────────── +sketch.line(470, 190, 470, 470, { stroke: COLORS.muted, strokeWidth: 1.6, dashed: true }); +sketch.text(470, 340, 'vs', { size: 26, weight: 800, color: COLORS.muted }); + +// ── right: two cards work ──────────────────────────────── +sketch.text(560, 190, 'TWO CARDS, --tensor-parallel-size 2', { + size: 19, + weight: 800, + anchor: 'start', + color: COLORS.teal.stroke, +}); + +const cardW = 246; +const cardGap = 84; +const gpuY = 210; +[0, 1].forEach((gpu) => { + const x = 560 + gpu * (cardW + cardGap); + const accent = gpu === 0 ? COLORS.blue : COLORS.green; + sketch.rect(x, gpuY, cardW, 150, { stroke: accent.stroke, fill: accent.fill }); + sketch.text(x + cardW / 2, gpuY + 34, `GPU ${gpu}`, { size: 22, weight: 800, color: accent.stroke }); + sketch.text(x + cardW / 2, gpuY + 66, 'weights 30.59 GiB', { size: 18, weight: 700 }); + sketch.text(x + cardW / 2, gpuY + 94, 'KV cache 8.22 GiB', { size: 17, color: COLORS.muted }); + sketch.text(x + cardW / 2, gpuY + 124, gpu === 0 ? 'heads 0-31' : 'heads 32-63', { + size: 16, + color: COLORS.muted, + }); +}); + +// all-reduce link between the two cards +const gapL = 560 + cardW + 10; +const gapR = 560 + cardW + cardGap - 10; +const gapMid = (gapL + gapR) / 2; +const linkY = gpuY + 66; +sketch.arrow(gapL, linkY, gapR, linkY, { stroke: COLORS.violet.stroke }); +sketch.arrow(gapR, linkY + 24, gapL, linkY + 24, { stroke: COLORS.violet.stroke }); +sketch.text(gapMid, gapMid && linkY + 60, 'all-', { size: 15, weight: 700, color: COLORS.violet.stroke }); +sketch.text(gapMid, linkY + 80, 'reduce', { size: 15, weight: 700, color: COLORS.violet.stroke }); + +sketch.text(560, gpuY + 182, 'GPU KV cache size:', { size: 17, anchor: 'start', color: COLORS.muted }); +sketch.text(560, gpuY + 208, '67,296 tokens', { + size: 30, + weight: 800, + anchor: 'start', + color: COLORS.teal.stroke, +}); +sketch.text(830, gpuY + 208, '2.05x concurrency', { size: 18, anchor: 'start', color: COLORS.muted }); + +// ── footer strip ───────────────────────────────────────── +sketch.line(64, 520, W - 64, 520, { stroke: COLORS.muted, strokeWidth: 1.6 }); +sketch.text(64, 556, 'QWEN3-32B BF16 - 61.02 GiB CHECKPOINT - vLLM 0.27.1', { + size: 19, + weight: 800, + anchor: 'start', + color: COLORS.ink, +}); +sketch.text(64, 586, '2 x 128 all-reduces per token, no NVLink, measured not estimated', { + size: 17, + anchor: 'start', + color: COLORS.muted, +}); +sketch.text(W - 64, 586, 'blog.kubesimplify.com', { + size: 17, + weight: 700, + anchor: 'end', + color: COLORS.muted, +}); + +sketch.save(join(output, 'cover.svg')); +console.log(`Wrote two-GPU vLLM cover to ${output}`); From 8b1d2a5c3e94d3fad7aebfef7cecfa10b6bcc21b Mon Sep 17 00:00:00 2001 From: Saiyam Pathak Date: Tue, 18 Aug 2026 15:16:30 +0530 Subject: [PATCH 02/19] Drop draft flag so the post renders on the Cloudflare preview Drafts are filtered out of production builds (INCLUDE_DRAFTS is false when NODE_ENV is production), so the preview deploy 404s on the post while the flag is set. Removing it makes the PR preview reviewable. The PR itself stays in draft, so nothing publishes until it is marked ready and merged. Signed-off-by: Saiyam Pathak --- content/blog/running-a-single-llm-across-two-gpus-with-vllm.md | 1 - 1 file changed, 1 deletion(-) diff --git a/content/blog/running-a-single-llm-across-two-gpus-with-vllm.md b/content/blog/running-a-single-llm-across-two-gpus-with-vllm.md index 6209c0218..a1d0b1f6c 100644 --- a/content/blog/running-a-single-llm-across-two-gpus-with-vllm.md +++ b/content/blog/running-a-single-llm-across-two-gpus-with-vllm.md @@ -7,7 +7,6 @@ slug: running-a-single-llm-across-two-gpus-with-vllm author: saiyam-pathak cover: /img/blog/running-a-single-llm-across-two-gpus-with-vllm/cover.png tags: ["vllm", "gpu", "nvidia", "tensor-parallelism"] -draft: true --- Someone asked me this in a thread the other day, and it is such a good question that it deserves a full walkthrough: From 649688cb6cc2a70a78c1c80b06a96fccd3019a8d Mon Sep 17 00:00:00 2001 From: Saiyam Pathak Date: Tue, 18 Aug 2026 17:05:03 +0530 Subject: [PATCH 03/19] Rework: big model across multiple GPUs, measured on 4 cards Replaces the two-GPU A40 post. The old version simulated a 2-GPU split by capping memory on larger cards, which meant the central claim was emulated rather than measured. This version uses a model that genuinely does not fit: Qwen3-235B-A22B-Instruct-2507-FP8, 221 GiB on disk, 2.3x too big for one 96GB card. Reframed away from A40 specifics to the general question of how a big model is spread over several GPUs and how inference works once it is. Byline is now Shubham and Saiyam. Rewritten in plain English throughout, with every flag and every line of the docker command explained. All three splitting modes measured on the same 4 GPUs: - TP=4: 17.14 ms/token, 503.68 tok/s at 32 concurrent, 621,392 KV tokens - TP=4 + EP: 18.83 ms/token, 470.93 tok/s, 623,696 KV tokens - PP=4: 21.19 ms/token, 296.48 tok/s, 555,680 KV tokens, best TTFT Plus three real failure modes with their actual error text: an invalid tensor-parallel size, a genuine CUDA OOM at 2 GPUs, and a DeepGEMM "Unknown SF transformation" crash on sm_120 that needs VLLM_USE_DEEP_GEMM=0. Adds Part 1 on downloading and on-disk storage (safetensors shards, the HF cache blob layout, FP8 block scales) and Part 3 on what inference actually does (prefill versus decode, continuous batching, why capacity is set by the KV cache). Includes the disk-pressure hazard that evicted pods on our own test node. Four CSS animations replace the previous three, following the existing series pattern: three-ways-to-split, tensor split across 4 GPUs, expert routing, and memory fit on 1 / 2 / 4 GPUs. Signed-off-by: Saiyam Pathak --- components/MoeExpertRoutingAnimation.jsx | 290 +++++++++ components/MultiGpuMemoryFitAnimation.jsx | 390 ++++++++++++ components/MultiGpuSplitModesAnimation.jsx | 279 +++++++++ components/MultiGpuTensorSplitAnimation.jsx | 374 ++++++++++++ components/TwoGpuMemoryFitAnimation.jsx | 353 ----------- components/TwoGpuTensorSplitAnimation.jsx | 414 ------------- components/TwoGpuTpVsPpAnimation.jsx | 373 ------------ ...-big-llm-across-multiple-gpus-with-vllm.md | 573 ++++++++++++++++++ ...-a-single-llm-across-two-gpus-with-vllm.md | 362 ----------- lib/_blog-feed-data.js | 24 +- lib/markdown.js | 21 +- public/_redirects | 2 + public/_worker.js | 2 +- public/atom.xml | 15 +- .../cover.png | Bin 0 -> 187877 bytes .../cover.svg | 55 ++ .../cover.png | Bin 193910 -> 0 bytes .../cover.svg | 51 -- public/llms-full.txt | 570 +++++++++++++++++ public/llms.txt | 16 +- public/rss.xml | 10 +- scripts/gen-local-llm-glossary-cover.mjs | 2 +- ...cover.mjs => gen-multi-gpu-vllm-cover.mjs} | 113 ++-- vercel.json | 16 + 24 files changed, 2657 insertions(+), 1648 deletions(-) create mode 100644 components/MoeExpertRoutingAnimation.jsx create mode 100644 components/MultiGpuMemoryFitAnimation.jsx create mode 100644 components/MultiGpuSplitModesAnimation.jsx create mode 100644 components/MultiGpuTensorSplitAnimation.jsx delete mode 100644 components/TwoGpuMemoryFitAnimation.jsx delete mode 100644 components/TwoGpuTensorSplitAnimation.jsx delete mode 100644 components/TwoGpuTpVsPpAnimation.jsx create mode 100644 content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md delete mode 100644 content/blog/running-a-single-llm-across-two-gpus-with-vllm.md create mode 100644 public/img/blog/running-a-big-llm-across-multiple-gpus-with-vllm/cover.png create mode 100644 public/img/blog/running-a-big-llm-across-multiple-gpus-with-vllm/cover.svg delete mode 100644 public/img/blog/running-a-single-llm-across-two-gpus-with-vllm/cover.png delete mode 100644 public/img/blog/running-a-single-llm-across-two-gpus-with-vllm/cover.svg rename scripts/{gen-two-gpu-vllm-cover.mjs => gen-multi-gpu-vllm-cover.mjs} (60%) diff --git a/components/MoeExpertRoutingAnimation.jsx b/components/MoeExpertRoutingAnimation.jsx new file mode 100644 index 000000000..2c6424166 --- /dev/null +++ b/components/MoeExpertRoutingAnimation.jsx @@ -0,0 +1,290 @@ +const gpus = [0, 1, 2, 3]; +const PER_GPU = 32; + +export default function MoeExpertRoutingAnimation() { + return ( +

+ +
+

Expert routing animation

+

+ Why a 235B model only does 22B of work per token +

+

+ Every layer of this model has 128 small expert networks, and a tiny router picks just 8 of + them for each token. The other 120 sit still. That is the whole trick of a mixture of + experts: you pay for 235B parameters in memory, but only about 22B of arithmetic per token. +

+ +
+
+ one token arrives + it has already been through attention for this layer +
+ +
router scores all 128 experts, keeps the top 8
+ +
+ {gpus.map((gpu) => { + // 2 of this GPU's 32 experts are picked, so 8 across 4 GPUs + const hot = [3 + gpu, 18 + ((gpu * 5) % 10)]; + return ( +
+

+ GPU {gpu} + experts {gpu * PER_GPU}-{gpu * PER_GPU + PER_GPU - 1} +

+ + ); + })} +
+ +
+
+ In memory + 235B params + + All 128 experts per layer must be resident, which is why the model is big + +
+
+ Active per token + 22B params + + Only the 8 chosen experts do arithmetic, so it runs like a much smaller model + +
+
+ What this costs you + a network hop + + With expert parallelism the token travels to whichever GPU owns its expert, then the + answer travels back + +
+
+
+
+
+ Counts are from the model config: 128 experts per layer, 8 per token, 94 layers. Splitting 128 + experts over 4 GPUs gives 32 each, so on average 2 experts per GPU fire for any given token. + That average is the catch, because routing is not guaranteed to be even. +
+
+ ); +} diff --git a/components/MultiGpuMemoryFitAnimation.jsx b/components/MultiGpuMemoryFitAnimation.jsx new file mode 100644 index 000000000..54032e176 --- /dev/null +++ b/components/MultiGpuMemoryFitAnimation.jsx @@ -0,0 +1,390 @@ +const cases = [ + { + key: 'one', + verdict: 'fail', + title: '1 GPU', + flag: 'will not start', + note: '221 GiB of weights against an 85.51 GiB budget', + parts: [{ label: 'weights', value: '221 GiB', width: '92.08%', color: '#ef4444' }], + log: 'the model is 2.6x larger than the whole budget\nthere is no flag that fixes this', + }, + { + key: 'two', + verdict: 'fail', + title: '2 GPUs', + flag: 'CUDA out of memory', + note: 'about 110 GiB per card, still too much', + parts: [{ label: 'weights per card', value: '110 GiB', width: '46.04%', color: '#f59e0b' }], + log: 'Failed to load model - not enough GPU memory\n95.01 GiB total, of which 438.31 MiB is free', + }, + { + key: 'four', + verdict: 'pass', + title: '4 GPUs', + flag: '621,392 tokens', + note: 'weights fit, with room for about 19 concurrent 32k conversations', + parts: [ + { label: 'weights', value: '55.19 GiB', width: '23.00%', color: '#0098cc' }, + { label: 'KV cache', value: '27.85 GiB', width: '11.60%', color: '#2bb534' }, + ], + log: 'Worker_TP0 Model loading took 55.19 GiB\nAvailable KV cache memory: 27.85 GiB\nGPU KV cache size: 621,392 tokens', + }, +]; + +export default function MultiGpuMemoryFitAnimation() { + return ( +
+ +
+

Memory fit animation

+

+ The same model on 1, 2 and 4 GPUs +

+

+ Every number here came out of a real run. All three bars are drawn to the same scale, and + the dashed line is the 85.51 GiB that vLLM may use on one card at + --gpu-memory-utilization 0.90. A bar reaching past that line means the model does not fit. + Watch it shrink as GPUs are added, and note that it takes 4 before the bar finally lands to + the left of the line. +

+ +
+ {cases.map((c) => ( +
+
+ + {c.title} + {c.note} + + {c.flag} +
+ +
+ what one card must hold + full axis = 240 GiB +
+ +
+ +
+ {c.parts.map((p, i) => ( +
+ + {p.label} {p.value} + +
+ ))} +
+
+ +
{c.log}
+
+ ))} + +
+
+ KV per token, whole model + 188 KiB + 2 x 94 layers x 4 kv heads x 128 head_dim x 2 bytes +
+
+ Per card at TP=4 + 47 KiB + + each card keeps 1 of the 4 kv heads, so the cache divides rather than repeats + +
+
+ Predicted vs reported + 621,337 / 621,392 + + 27.85 GiB divided by 47 KiB, against what vLLM actually printed + +
+
+
+
+
+ Measured on 4x RTX PRO 6000 Blackwell with Qwen3-235B-A22B-Instruct-2507-FP8 on vLLM 0.27.1. + The 1 GPU and 2 GPU bars are what the run actually attempted before failing, not estimates. + Because this model has only 4 key/value heads, its cache is unusually cheap, which is why 4 + cards leave room for about 19 concurrent conversations at the 32,768-token limit we set. +
+
+ ); +} diff --git a/components/MultiGpuSplitModesAnimation.jsx b/components/MultiGpuSplitModesAnimation.jsx new file mode 100644 index 000000000..e58e6c224 --- /dev/null +++ b/components/MultiGpuSplitModesAnimation.jsx @@ -0,0 +1,279 @@ +const modes = [ + { + key: 'tp', + name: 'Tensor parallelism', + flag: '--tensor-parallel-size', + plain: 'Cut every layer into vertical strips. Each GPU holds a strip of all 94 layers.', + talks: 'A lot. Twice per layer, so 188 times per token.', + good: 'Fastest for a single user, because all 4 GPUs work on the same token.', + color: '#0098cc', + }, + { + key: 'pp', + name: 'Pipeline parallelism', + flag: '--pipeline-parallel-size', + plain: 'Cut the stack into horizontal blocks. With 94 layers over 4 GPUs, each one owns about 23 of them.', + talks: 'Barely. One handoff between neighbours per token.', + good: 'Kind to a slow network between GPUs, but a GPU waits its turn.', + color: '#2bb534', + }, + { + key: 'ep', + name: 'Expert parallelism', + flag: '--enable-expert-parallel', + plain: 'Deal the 128 experts out like cards. Each GPU keeps 32 of them, whole.', + talks: 'Medium. Tokens are shipped to whichever GPU owns the expert they need.', + good: 'Only exists for MoE models, and it is how the really big ones are served.', + color: '#a855f7', + }, +]; + +export default function MultiGpuSplitModesAnimation() { + return ( +
+ +
+

Three ways to split animation

+

+ The same model, cut three different ways across four GPUs +

+

+ These are not competing products, they are three different cuts through the same pile of + weights, and you can combine them. Each box below is one GPU. Watch which parts light up, + because that tells you which GPUs are doing work at the same moment. +

+ +
+ {modes.map((mode) => ( +
+

{mode.name}

+ {mode.flag} + + + +
+

+ What it does + {mode.plain} +

+

+ How much it talks + {mode.talks} +

+

+ When it wins + {mode.good} +

+
+
+ ))} +
+
+
+ Layer and expert counts are Qwen3-235B-A22B: 94 layers, 128 experts with 8 picked per token. + Under tensor parallelism all four GPUs light up together on every token. Under pipeline + parallelism they light up in turn, which is the idle time you are trading away. +
+
+ ); +} diff --git a/components/MultiGpuTensorSplitAnimation.jsx b/components/MultiGpuTensorSplitAnimation.jsx new file mode 100644 index 000000000..2e9bbe573 --- /dev/null +++ b/components/MultiGpuTensorSplitAnimation.jsx @@ -0,0 +1,374 @@ +const steps = [ + { label: 'A token arrives', detail: 'all 4 GPUs get the same copy of it' }, + { label: 'Split sideways', detail: 'each GPU owns 16 of the 64 attention heads' }, + { label: 'Work alone', detail: 'no GPU needs to ask the others anything yet' }, + { label: 'Partial answers', detail: 'each GPU has a quarter of the answer' }, + { label: 'Add them up', detail: 'one all-reduce, and all 4 hold the full result' }, +]; + +export default function MultiGpuTensorSplitAnimation() { + return ( +
+ +
+

Tensor parallelism animation

+

+ One layer, sliced four ways +

+

+ This is the part people usually get wrong, so it is worth being precise. The weights get + divided, and the thing flowing through them does not. Every GPU starts each layer holding + an identical copy of the token, does a quarter of the arithmetic on its own slice of the + weights, and ends up with a quarter of an answer. Then they add their quarters together. +

+ +
+
+ the token, 4096 numbers wide + copied to all four GPUs, not divided +
+ +
+ {[0, 1, 2, 3].map((gpu) => ( +
+

+ GPU {gpu} + heads {gpu * 16}-{gpu * 16 + 15} +

+ + ))} +
+ +
+ + +
+ the finished layer output, now identical on all four GPUs + and the next layer does the whole dance again +
+
+ +
+ {steps.map((step, i) => ( +
+ Step {i + 1} + {step.label} + {step.detail} +
+ ))} +
+
+
+ Shapes are Qwen3-235B-A22B: hidden size 4096, 64 attention heads, 4 key/value heads, 94 + layers. Those 4 key/value heads are the reason this model cannot be split cleanly more than 4 + ways, which we come back to later. +
+
+ ); +} diff --git a/components/TwoGpuMemoryFitAnimation.jsx b/components/TwoGpuMemoryFitAnimation.jsx deleted file mode 100644 index 953a27cef..000000000 --- a/components/TwoGpuMemoryFitAnimation.jsx +++ /dev/null @@ -1,353 +0,0 @@ -const scenarios = [ - { - key: 'one', - verdict: 'fail', - title: 'One card', - flag: '-24.42 GiB', - note: 'vLLM refuses to start', - parts: [ - { label: 'weights', value: '61.03 GiB', width: '151%', color: '#ef4444' }, - ], - }, - { - key: 'two', - verdict: 'pass', - title: 'Two cards, TP=2', - flag: '67,296 tokens', - note: '2.05x concurrency at 32k context', - parts: [ - { label: 'weights', value: '30.59 GiB', width: '75.6%', color: '#0098cc' }, - { label: 'KV cache', value: '8.22 GiB', width: '20.3%', color: '#2bb534' }, - ], - }, -]; - -export default function TwoGpuMemoryFitAnimation() { - return ( -
- -
-

Memory fit animation

-

- Qwen3-32B in BF16 against one A40 budget, then two -

-

- These are the numbers vLLM actually reported on my run. A 61.02 GiB checkpoint has nowhere - to go on a single 45 GiB card, and the failure is not subtle: the KV cache budget comes out - negative before a single token is stored. -

- -
- {scenarios.map((scenario) => ( -
-
- - {scenario.title} - {scenario.note} - - {scenario.flag} -
- -
- per-card budget - ceiling 40.47 GiB -
- -
-
- {scenario.parts.map((part, index) => ( -
- - {part.label} {part.value} - -
- ))} -
-
- -
- {scenario.verdict === 'fail' - ? 'Model loading took 61.03 GiB\nAvailable KV cache memory: -24.42 GiB\nValueError: No available memory for the cache blocks.' - : 'Worker_TP0 Model loading took 30.59 GiB\nWorker_TP1 Model loading took 30.59 GiB\nGPU KV cache size: 67,296 tokens'} -
-
- ))} - -
-
- KV per token - 256 KiB - - 2 x 64 layers x 8 kv heads x 128 head_dim x 2 bytes - -
-
- Per card at TP=2 - 128 KiB - - 4 of the 8 kv heads land on each card, so the cache is divided, not copied - -
-
- Predicted vs reported - 67,338 / 67,296 - - 8.22 GiB divided by 128 KiB, against what vLLM printed - -
-
-
-
-
- Captured on two RTX PRO 6000 Blackwell cards held to a 40.47 GiB budget with - --gpu-memory-utilization 0.426, which matches a 45 GiB A40 running at 0.90. Weight splitting - does not depend on the architecture, so these memory figures carry over to A40 directly. -
-
- ); -} diff --git a/components/TwoGpuTensorSplitAnimation.jsx b/components/TwoGpuTensorSplitAnimation.jsx deleted file mode 100644 index f1d997515..000000000 --- a/components/TwoGpuTensorSplitAnimation.jsx +++ /dev/null @@ -1,414 +0,0 @@ -const stages = [ - { key: 'broadcast', label: 'X arrives', detail: 'same full copy on both cards' }, - { key: 'column', label: 'Column-parallel', detail: 'each card owns half the columns of A' }, - { key: 'local', label: 'Activation stays local', detail: 'SiLU is elementwise, no comms' }, - { key: 'row', label: 'Row-parallel', detail: 'each card gets a partial sum' }, - { key: 'reduce', label: 'All-reduce', detail: 'partial sums added, both cards get Z' }, -]; - -export default function TwoGpuTensorSplitAnimation() { - return ( -
- -
-

Tensor parallelism animation

-

- One MLP block, split down the middle across two cards -

-

- The weights are split and the activations are not. Each card owns half the columns of the - first matrix and half the rows of the second, so all the maths stays local until the very - end, where one all-reduce adds the two partial sums together. -

- -
-
- input X, hidden_size 5120 - replicated, both cards hold the same full copy -
- -
- {[0, 1].map((gpu) => ( -
-
- GPU {gpu} - {gpu === 0 ? 'heads 0-31' : 'heads 32-63'} -
- - - ))} -
- -
- - -
- full output Z, identical on both cards - next layer starts from here -
-
- -
- {stages.map((stage, index) => ( -
- Step {index + 1} - {stage.label} - {stage.detail} -
- ))} -
-
-
- Shapes are Qwen3-32B: hidden_size 5120, intermediate_size 25600 halved to 12800 per card, 64 - attention heads halved to 32. Attention splits the same way, so a full layer costs two - all-reduces, not one. -
-
- ); -} diff --git a/components/TwoGpuTpVsPpAnimation.jsx b/components/TwoGpuTpVsPpAnimation.jsx deleted file mode 100644 index 6e9386fe9..000000000 --- a/components/TwoGpuTpVsPpAnimation.jsx +++ /dev/null @@ -1,373 +0,0 @@ -const results = [ - { metric: 'tok/s at concurrency 1', tp: '36.41', pp: '21.00', win: 'tp' }, - { metric: 'tok/s at concurrency 32', tp: '496.60', pp: '487.56', win: 'tie' }, - { metric: 'median TTFT at 32', tp: '3892 ms', pp: '2468 ms', win: 'pp' }, - { metric: 'KV cache tokens', tp: '67,296', pp: '56,640', win: 'tp' }, -]; - -export default function TwoGpuTpVsPpAnimation() { - return ( -
- -
-

TP versus PP animation

-

- Two ways to cut the same model, and what each one costs -

-

- Both modes solve the fitting problem, so the choice is purely about speed. Tensor - parallelism keeps both cards busy on every token and pays 128 all-reduces for it. Pipeline - parallelism barely communicates at all, but runs like a relay race. -

- -
-
-

- Tensor parallelism, TP=2 - every layer is split, both cards work on every token -

-
-
- GPU 0 - all 64 layers, heads 0-31, 30.59 GiB -
-
- -
- GPU 1 - all 64 layers, heads 32-63, 30.59 GiB -
-
-

- Wins decode. Both cards contribute memory bandwidth to the same token, so - concurrency 1 is 73% faster. Pays for it in prefill, where each all-reduce carries 10 MB - instead of 10 KB. -

-
- -
-

- Pipeline parallelism, PP=2 - the stack is cut by layer, one activation handoff -

-
-
- GPU 0 - layers 0-31, all 8 kv heads, 30.52 GiB -
-
- -
- GPU 1 - layers 32-63, all 8 kv heads, 30.52 GiB -
-
-

- Wins prefill. Almost no communication, so time to first token is 37% better at - concurrency 32. But with one request in flight a card is always idle, and the extra - pipeline buffers cost you 19% of the KV cache. -

-
-
- -
-
-
Measured
-
TP=2
-
PP=2
-
- {results.map((row) => ( -
-
{row.metric}
-
- {row.tp} -
-
- {row.pp} -
-
- ))} -
-
-
- Measured with vllm bench serve, 1024 input and 256 output tokens, on two PCIe-connected cards - with no NVLink. The concurrency 32 throughput gap is 1.9%, which is close enough to noise that - I would call it a tie rather than a win. -
-
- ); -} diff --git a/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md b/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md new file mode 100644 index 000000000..f324c45ec --- /dev/null +++ b/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md @@ -0,0 +1,573 @@ +--- +title: "Running a big LLM across multiple GPUs with vLLM" +seoTitle: "Running a big LLM across multiple GPUs with vLLM" +seoDescription: "A plain-English guide to serving a model that is too big for one GPU: how tensor, pipeline, and expert parallelism split it up, what every vLLM flag does, and measured numbers from a 235B model on four RTX PRO 6000 cards." +datePublished: 2026-08-18T10:00:00.000Z +slug: running-a-big-llm-across-multiple-gpus-with-vllm +author: shubham-katara +authors: ["shubham-katara", "saiyam-pathak"] +cover: /img/blog/running-a-big-llm-across-multiple-gpus-with-vllm/cover.png +tags: ["vllm", "gpu", "nvidia", "llm", "platform-engineering"] +--- + +Sooner or later everyone running models locally hits the same wall. You find a model you want, you look at the download size, and it is bigger than the GPU you own. A 235B model needs roughly 236 GB just for its weights. The card we have holds 96 GB, and even the largest data-centre GPUs available today top out well below 236 GB. So the model does not fit, and no amount of clever flags will make 236 GB squeeze into 96 GB. + +The answer is to use more than one GPU. That part everybody knows. The part that is genuinely confusing is what "use more than one GPU" actually means. Does each GPU get a copy of the model? Does the model get cut in half? Do the GPUs take turns? Which of those is happening, and what does it cost you? + +Let's answer that properly, with a real model on real hardware, and let's explain every single flag and command along the way rather than pasting a magic incantation and moving on. + +## What you will learn + +- How to download a 236 GB model, what the 24 files you get actually are, and how they sit on disk +- How to work out on paper whether it fits on your GPUs, before you spend an hour downloading it +- What inference really is: the two completely different phases behind "time to first token" and "tokens per second" +- The three different ways a model can be split across GPUs, in plain English, and when each is used +- What every flag in our vLLM command does, and why it has the value it has +- How to read the startup log, which tells you more than any tutorial can +- The rules that limit how far you can split, and the real errors you get when you break them +- Measured numbers for all three splitting modes on the same model and the same four GPUs + +No prior knowledge of distributed computing is assumed. If you know what a GPU is and you have run a model locally once, you are qualified. + +## The machine and the model + +Here is what we tested on, because numbers mean nothing without the hardware attached. + +**The machine:** a server with 8x NVIDIA RTX PRO 6000 Blackwell Server Edition cards. Each card has 96 GB of memory, and the machine reports 95.01 GiB of that as usable. We borrowed 4 of the 8 cards for this work. + +One detail that matters more than it looks: these GPUs are **not** connected by NVLink. NVLink is NVIDIA's fast direct GPU-to-GPU cable. Without it, GPUs talk to each other over PCIe and through the CPU, which is slower. You can check what you have with one command: + +```bash +nvidia-smi topo -m +``` + +On our machine every pair of GPUs reports `SYS`, which means the traffic goes across PCIe and then across the link between the CPU sockets. If you had NVLink you would see `NV1`, `NV2` and so on instead. Keep this in mind, because it changes which splitting method is fastest. + +**The model:** `Qwen/Qwen3-235B-A22B-Instruct-2507-FP8`. Let's unpack that name, because it is doing a lot of work: + +- **235B** is the total parameter count, 235 billion. +- **A22B** means 22 billion **active** parameters. This is a mixture-of-experts model, and only a fraction of it runs for any given token. More on this shortly, because it is the most interesting thing about serving big models today. +- **FP8** is the number format the weights are stored in, 8 bits each, so one byte per parameter. + +**The software:** vLLM 0.27.1 running in the official container, with PyTorch 2.13.0 and CUDA 13.0, on driver 610.43.02. + +## Part 1: Getting the model onto the machine + +Before anything can be split across GPUs it has to be on the disk, and with a model this size that step is not a formality. It is the step that bit us hardest, so let's do it properly. + +You download it with the Hugging Face CLI: + +```bash +pip install huggingface_hub hf_transfer + +HF_HUB_ENABLE_HF_TRANSFER=1 hf download Qwen/Qwen3-235B-A22B-Instruct-2507-FP8 +``` + +`HF_HUB_ENABLE_HF_TRANSFER=1` switches on a Rust downloader that parallelises across connections. On a 236 GB download that is the difference between an hour and most of an afternoon, so it is worth the extra package. + +### What you actually get + +The download is not one giant file. It arrives as **24 shards**, plus the small text files that describe the model: + +``` +config.json +generation_config.json +model-00001-of-00024.safetensors +model-00002-of-00024.safetensors +... +model-00024-of-00024.safetensors +model.safetensors.index.json +tokenizer.json +``` + +A few things worth understanding here: + +- **`.safetensors`** is the modern format for weights. It is a flat file with a small JSON header at the front listing every tensor's name, dtype, shape and byte range, then the raw bytes. That layout matters for us, because it means a loader can memory-map the file and read exactly the byte ranges it wants without parsing the whole thing, and without the security problems of the old pickle-based `.bin` format. +- **`model.safetensors.index.json`** is the map that says which tensor lives in which shard. This is how vLLM knows to open shard 17 to find layer 62's weights. +- **`config.json`** is the architecture file we keep coming back to: layer count, head counts, expert count. It is a few kilobytes and it determines almost every decision in this post. +- For an FP8 model like this one, the weight tensors are joined by **scale tensors**. FP8 has very little numeric range, so the checkpoint stores a scaling factor per 128x128 block of each weight matrix, and the real value is the 8-bit number multiplied by its block's scale. You can see that arrangement declared in `config.json`: + +```json +"quantization_config": { + "quant_method": "fp8", + "fmt": "e4m3", + "weight_block_size": [128, 128], + "activation_scheme": "dynamic" +} +``` + +Remember those block scales. They are the reason for the most annoying crash we hit, back in Part 12. + +### Where it gets stored + +By default everything lands under `~/.cache/huggingface/hub`, in a layout that looks strange the first time you see it: + +``` +~/.cache/huggingface/hub/models--Qwen--Qwen3-235B-A22B-Instruct-2507-FP8/ +├── blobs/ <- the real files, named by hash +├── refs/ <- which commit "main" points at +└── snapshots/ + └── e156cb4e.../ <- symlinks with friendly names, pointing into blobs/ +``` + +The content lives once in `blobs/` under its hash, and `snapshots/` holds human-readable symlinks into it. That is why pulling two revisions of a model does not always double your disk usage, and it is also why `du` and `df` can disagree with your intuition. + +The practical consequence for serving: mount that whole directory into your container and set `HF_HOME` to it, which is exactly what the `-v` and `-e HF_HOME` flags in Part 8 are doing. Otherwise the container downloads its own copy. + +### The disk trap, which is a real production hazard + +Two things about disk that the model card will not tell you. + +**Each tensor-parallel worker reads the entire checkpoint.** vLLM's own docs say that with tensor parallelism "each process will read the whole model and split it into chunks". So at `-tp 4` the machine performs roughly 4 x 221 GiB of reads at startup, not 221 GiB divided four ways. That is why a big model takes minutes to load even off fast storage, and it is why our first `Model loading took` line reported 45 seconds only because a lot of the file was still in the operating system's page cache from the download. + +**On a shared machine, filling the disk can take down everything else on it.** This is the part we learned the hard way, and it is worth more than a footnote. Our test box also runs a Kubernetes inference platform. Kubernetes treats free disk as a managed resource called ephemeral-storage, and when free space fell below its eviction threshold, the kubelet did exactly what it is designed to do: it evicted pods to reclaim space, tainted the node so nothing new could schedule, and garbage-collected container images. Several of those images had been built locally and existed in no registry, so they could not simply be pulled again. + +Nothing about that is a Kubernetes bug, and nothing about it is specific to our setup. The lesson generalises: **before you download a quarter of a terabyte onto a machine, check what else lives on that disk and what will happen when it fills.** `df -h` before you start, and know your platform's eviction threshold, which is often far higher than "0 bytes free". If the machine is shared, keeping a couple of hundred gigabytes of headroom is not paranoia. + +## Part 2: Why one GPU is not enough + +Let's do the arithmetic, because it is simpler than people expect and it saves you a lot of wasted download time. + +A model is mostly a big pile of numbers called **parameters** or **weights**. To run the model, those numbers have to sit in GPU memory. So the first question is always: how many bytes is one parameter? + +| Format | Bits per parameter | Bytes per parameter | +| --- | --- | --- | +| FP32 | 32 | 4 | +| BF16 or FP16 | 16 | 2 | +| FP8 | 8 | 1 | +| FP4 or NVFP4 | 4 | 0.5 | + +So the weights alone take `number of parameters x bytes per parameter`. For our model that is 235 billion parameters at 1 byte each, which is about 236 GB. Our GPU holds 95.01 GiB. The model is roughly 2.3 times too big for one card. + +But weights are only the first of **three** things that need to fit. This is where most people's mental model is incomplete: + +1. **The weights.** Fixed size. You know it before you start. +2. **The KV cache.** This is the model's memory of the conversation so far. Every token you feed in, and every token the model writes, leaves behind a small record that has to be kept for as long as that request is alive. It grows with how long your prompts are and how many users you serve at once. +3. **Working space.** Temporary scratch memory for the actual calculations, plus some overhead the framework reserves for itself. + +The KV cache is the one that surprises people, so let's size it. The formula looks intimidating but every term is just a number from the model's config file: + +``` +bytes per token = 2 x layers x kv_heads x head_dim x bytes_per_number +``` + +The `2` is because you store two things per token, a key and a value, which is where "KV" comes from. For our model, `layers` is 94, `kv_heads` is 4, `head_dim` is 128, and the cache is kept in BF16 so that is 2 bytes: + +``` +2 x 94 x 4 x 128 x 2 = 192,512 bytes = 188 KiB per token +``` + +188 KiB does not sound like much. But this model supports a 262,144 token context, so one single conversation at full length would need `262,144 x 188 KiB`, which is about **47 GiB**. That is half a GPU for one user. Serving ten users at once with long prompts is where all your leftover memory goes, and it is why "the weights fit, so I am fine" is wrong. + +{{multi-gpu-memory-fit-animation}} + +## Part 3: What actually happens when a request arrives + +Before splitting anything, it helps to know what the work being split actually is, because inference is really two different jobs wearing one coat. Almost everything confusing about multi-GPU performance comes from this split. + +### Phase one: prefill, reading your prompt + +When your prompt arrives, the model has to read all of it. If you send 1,000 tokens, all 1,000 go through every layer **at once**, as one big batch of work. This is called **prefill**, and it is the phase that decides your time to first token. + +Prefill is *compute-heavy*. There is a lot of arithmetic to do and the GPU's matrix engines are the bottleneck. It also produces the keys and values for every one of those 1,000 tokens, which get written into the KV cache and kept. + +### Phase two: decode, writing the answer + +Then the model writes its reply, and here is the part that surprises people: **it can only produce one token at a time.** To write token 2 it needs to have written token 1, because it feeds its own output back in. There is no way around that, it is what "autoregressive" means. + +So decode is a loop. Each pass through it produces exactly one token, reads the entire KV cache built so far, and appends one more entry to that cache. + +Decode is *memory-heavy* rather than compute-heavy. For a single token there is barely any arithmetic to do, but the GPU still has to stream the relevant weights and the whole KV cache past its compute units. The bottleneck is memory bandwidth, not maths. That is why decode speed tracks memory bandwidth so closely, and why giving a single request more GPUs to read from in parallel actually helps. + +Two phases, two different bottlenecks, and they respond differently to everything you tune: + +| | Prefill | Decode | +| --- | --- | --- | +| Work per step | your whole prompt at once | exactly one token | +| Bottleneck | compute | memory bandwidth | +| Metric it drives | time to first token | time per output token | +| Data moved between GPUs | large, whole prompt's worth | tiny, one token's worth | + +That last row is the one to hold on to. It is the reason, later, that pipeline parallelism wins on first-token latency while tensor parallelism wins on tokens per second. The same all-reduce that is trivially cheap during decode is expensive during prefill, because it is carrying a thousand times more data. + +### How the server juggles many users + +A real server is not doing one request at a time. vLLM uses **continuous batching**, which means it does not wait for a batch to fill up or finish. On every step it looks at everything currently in flight and assembles whatever work is ready, so a request that arrives mid-flight joins the very next step rather than queueing behind a whole batch. + +Two consequences worth knowing: + +- **Prefill and decode get mixed together.** A step might carry one user's fresh 1,000-token prompt alongside twenty other users' single decode tokens. That mixing is why a burst of long prompts makes everyone else's tokens arrive more slowly, and it is why `--max-num-batched-tokens` exists as a lever. +- **Capacity is set by the KV cache, not by CPU or queue length.** Every in-flight request is holding cache proportional to its length. When the cache is full, vLLM has to **preempt** somebody: it evicts a request's cache and recomputes it later. That is the real meaning of the `Maximum concurrency` line in the startup log, and it is why we spend so much of this post counting cache bytes. + +Now that the work itself is clear, let's look at the three ways to spread it over more than one GPU. + +## Part 4: The three ways to split a model + +Here is the heart of it. When people say "split the model across GPUs" they could mean three genuinely different things, and mixing them up is the source of most confusion. + +An analogy first, because it makes the rest much easier to hold in your head. Imagine a large restaurant kitchen that has to produce one dish: + +- **Tensor parallelism** is four chefs all working on the same dish at the same time, one chopping, one on sauce, one on protein, one plating. They constantly have to coordinate, but the dish is done fast. +- **Pipeline parallelism** is four chefs at four stations, where the dish moves down the line. Station two cannot start until station one is finished. Very little talking, but three chefs are idle at any moment unless you have several dishes in flight. +- **Expert parallelism** is a kitchen with 128 specialist chefs where each dish only needs 8 of them. You spread those 128 chefs across four rooms, and each dish gets walked to whichever rooms hold the specialists it needs. + +{{multi-gpu-split-modes-animation}} + +All three can be combined, and in production they usually are. Now let's look at each one properly. + +## Part 5: Tensor parallelism, up close + +Tensor parallelism cuts **inside** every layer. This is the important distinction: it does not give GPU 0 the first half of the model and GPU 1 the second half. Every GPU holds a thin slice of **all 94 layers**. + +How can you cut a layer? Because the work a layer does is mostly one big multiplication table, and multiplication tables can be cut up. The technique comes from a 2019 NVIDIA paper called Megatron-LM, and it works in two moves. + +**Move one, cut the first matrix into vertical strips.** Each GPU takes some of the columns. Because each GPU has complete columns, it can finish its part, including the activation function in the middle, without asking anyone anything. In our model the attention block has 64 heads, so with 4 GPUs each one owns 16 whole heads and computes them start to finish alone. + +**Move two, cut the second matrix into horizontal strips.** These line up exactly with the vertical cuts from move one. Each GPU multiplies its slice and gets a **partial answer**, a quarter of the real result. + +Now, and only now, the GPUs have to talk. They add their four partial answers together so that everyone ends up with the complete result. That single operation is called an **all-reduce**: everyone contributes a piece, everyone gets the total back. + +The Megatron paper puts the cost plainly, saying this design lets you run a transformer layer "using only two all-reduces in the forward path". Generating text only uses the forward path, so: + +- 2 all-reduces per layer +- 94 layers +- **188 all-reduces to produce one single token** + +And they happen strictly one after another, because layer 5 cannot begin until layer 4 has finished comparing notes. + +{{multi-gpu-tensor-split-animation}} + +### The KV cache gets divided too, which is a bonus + +Because each GPU owns only some of the attention heads, it only needs to remember keys and values for its own heads. So the KV cache is divided across GPUs rather than duplicated. Four GPUs give you roughly four times the room for conversations, on top of making the weights fit. This is a real and often unmentioned benefit of tensor parallelism. + +## Part 6: The expert part, which is why this model is only 22B of work + +Our model is a **mixture of experts**, and this is the single biggest idea in how large models are served today, so it is worth slowing down for. + +In an ordinary model, every parameter is used for every token. In a mixture-of-experts model, each layer contains many small networks called **experts**, and a tiny component called a **router** decides which few of them each token should visit. Our model has **128 experts per layer** and the router picks **8** of them per token. + +So the model holds 235B parameters in memory, but only about 22B of them do any arithmetic for a given token. That is what "235B-A22B" means, and it is why this model runs far faster than its size suggests. You pay for the full 235B in memory and you pay for only 22B in speed. + +{{moe-expert-routing-animation}} + +This gives you a third way to split. Instead of slicing every expert into strips, you hand out whole experts: with 128 experts and 4 GPUs, each GPU keeps 32 of them intact. That is **expert parallelism**, and in vLLM you switch it on with `--enable-expert-parallel`. + +The trade is different from tensor parallelism. Nothing needs adding up at the end, but tokens have to travel to whichever GPU owns the expert they were routed to, and the answers travel back. It also has a fairness problem: the router does not promise to spread work evenly, so one GPU can end up with more popular experts and become the slow one holding everybody up. + +## Part 7: Every flag, explained + +Before the command, the vocabulary. Here is every flag we use and why it has the value it has. If you only remember one thing from this post, make it this table. + +| Flag | What it does | Why our value | +| --- | --- | --- | +| `--tensor-parallel-size 4` | How many GPUs to slice each layer across. Often shortened to `-tp`. | 236 GB of weights needs at least 3 cards of 95 GiB, and 4 divides the model's head counts cleanly. | +| `--pipeline-parallel-size 1` | How many groups to cut the layer stack into. Often `-pp`. | 1 means off. We test a version with 2 later. | +| `--enable-expert-parallel` | Hand out whole experts per GPU instead of slicing every expert. Mixture-of-experts models only. | Tested both ways, since this is exactly the choice a big MoE forces on you. | +| `--gpu-memory-utilization 0.90` | The fraction of each GPU's memory vLLM is allowed to claim, for weights plus KV cache plus working space. | 0.90 leaves a little headroom. Push it to 0.95 for more cache, but leave room or startup fails. | +| `--max-model-len 32768` | The longest single request, prompt plus reply, in tokens. | The model supports 262,144, but that would eat 47 GiB of cache for one user. 32,768 is a sane serving value. | +| `--max-num-seqs 32` | How many requests may be in flight at once. | Caps how much KV cache can be demanded simultaneously. Lower it if you see requests being preempted. | +| `--served-model-name qwen3-235b` | The name clients use in the API. | Otherwise clients must send the full checkpoint path. | +| `--port 8000` | Port for the OpenAI-compatible API. | Convention. | +| `--distributed-executor-backend mp` | How the GPU worker processes are managed: `mp` for plain Python multiprocessing, `ray` for a Ray cluster. | All 4 GPUs are in one machine, so `mp` is the simpler choice. `ray` is for multiple machines. | +| `--enforce-eager` | Skips building optimised CUDA graphs at startup. | We do **not** use it. It saves memory and starts faster, but generation is slower. Reach for it only if you are out of memory. | +| `--kv-cache-dtype fp8` | Stores the conversation cache at 8 bits instead of 16, roughly halving cache memory. | We left it at the default so our cache numbers are easy to check by hand. It is a good lever if you need more concurrency. | + +Two container flags matter just as much, and neither is a vLLM flag: + +| Docker flag | Why you need it | +| --- | --- | +| `--ipc=host` | The GPU workers are separate processes that pass data through shared memory. Docker's default 64 MB of shared memory is far too small, and leaving this out gives you a confusing hang at startup. | +| `--gpus '"device=1,4,5,6"'` | Hands specific GPUs to the container. The nested quoting is fussy but required. Inside the container they are renumbered 0 to 3. | + +## Part 8: The command, line by line + +Here is the whole thing. Every line is explained above, and we will walk the structure below it. + +```bash +docker run -d --name vllm-tp4 \ + --gpus '"device=1,4,5,6"' \ + --ipc=host \ + -p 8000:8000 \ + -v /root/.cache/huggingface:/root/.cache/huggingface \ + -e HF_HUB_OFFLINE=1 \ + -e HF_HOME=/root/.cache/huggingface \ + -e VLLM_USE_DEEP_GEMM=0 \ + vllm/vllm-openai:latest \ + Qwen/Qwen3-235B-A22B-Instruct-2507-FP8 \ + --served-model-name qwen3-235b \ + --tensor-parallel-size 4 \ + --gpu-memory-utilization 0.90 \ + --max-model-len 32768 \ + --max-num-seqs 32 \ + --port 8000 +``` + +Reading it top to bottom: + +- `docker run -d` starts the container in the background and prints its id. Drop the `-d` if you would rather watch the logs scroll past. +- `--name vllm-tp4` gives it a name so you can say `docker logs vllm-tp4` instead of copying an id. +- `-p 8000:8000` maps the container's port 8000 to the host's port 8000, so you can reach the API from outside. +- `-v /root/.cache/huggingface:/root/.cache/huggingface` shares your downloaded models with the container. Without it the container would download all 236 GB again. +- `-e HF_HUB_OFFLINE=1` tells the Hugging Face library not to phone home. It uses the local copy, which also means startup does not fail if the network is down. +- `vllm/vllm-openai:latest` is the image. Everything after it is passed to vLLM, because the image's entrypoint is already `vllm serve`. +- The first argument after the image is the model. Everything after that is a vLLM flag from the table above. +- `-e VLLM_USE_DEEP_GEMM=0` is here because without it this exact model would not start on these exact GPUs. It is not a general recommendation, and Part 12 explains the crash it avoids. If you are on different hardware, try without it first. + +One thing worth knowing about that entrypoint: because it is already `vllm serve`, running `docker run ... vllm/vllm-openai:latest python3 -c "..."` does **not** work the way you expect. Your Python gets handed to `vllm serve` as arguments and you get a confusing parse error. To run something else inside the image, override it: + +```bash +docker run --rm --gpus '"device=1,4"' --entrypoint python3 vllm/vllm-openai:latest -c " +import torch +print('GPUs visible:', torch.cuda.device_count()) +print('can GPU 0 talk to GPU 1 directly:', torch.cuda.can_device_access_peer(0, 1)) +" +``` + +That is a genuinely useful sanity check before you start a long model load, because it confirms the container can see the cards and that direct GPU-to-GPU access is available. + +## Part 9: How to read the startup log + +The startup log is the best teaching tool in the whole stack, and almost nobody reads it. Four lines tell you everything about whether your configuration is sensible. + +**Line one, how big the weights are per GPU.** You get one of these per worker: + +``` +(Worker_TP0) Model loading took X GiB +``` + +If you divide the full model size by your `--tensor-parallel-size` and get roughly this number, the split worked. If this number equals the **whole** model, something is wrong and you are not actually splitting. + +**Line two, what is left for conversations:** + +``` +Available KV cache memory: X GiB +``` + +If this is **negative**, your weights plus overhead already exceeded the budget, and vLLM will refuse to start. That is the clearest possible signal that you need more GPUs, a smaller number format, or a lower `--max-model-len`. + +**Line three, the cache in tokens:** + +``` +GPU KV cache size: N tokens +``` + +This is the total number of tokens the server can remember across all users at once. You can predict it: take the available cache memory, divide by the bytes-per-token figure we calculated in Part 2. + +**Line four, how many users that really means:** + +``` +Maximum concurrency for 32,768 tokens per request: N.NNx +``` + +This is the one to show your capacity planner. If it says `2.05x`, then two users can each have a full-length 32k conversation, and a third will have to wait or be preempted. It is simply the previous line divided by `--max-model-len`. + +## Part 10: The rules that limit how far you can split + +You cannot pick any number for `--tensor-parallel-size`. There are hard divisibility rules, and hitting them is a common early frustration. + +Because attention heads are handed out whole, **your tensor parallel size must divide the head counts**. Open the model's `config.json` and look: + +```json +{ + "num_hidden_layers": 94, + "hidden_size": 4096, + "num_attention_heads": 64, + "num_key_value_heads": 4, + "head_dim": 128, + "num_experts": 128, + "num_experts_per_tok": 8 +} +``` + +For our model: + +- `num_attention_heads` is 64, so 2, 4, 8, 16 all divide it cleanly. +- `num_key_value_heads` is **4**. This is the binding constraint. At `-tp 4` each GPU gets exactly one key/value head. At `-tp 8` there are not enough to go around, and vLLM has to duplicate them across GPUs, which wastes memory and gives you less benefit than you would hope. +- `num_experts` is 128, which divides evenly by 4 and by 8, so expert parallelism has more freedom than tensor parallelism here. + +That is the real lesson: **the KV head count, not the parameter count, usually decides how wide you can go.** It is the first thing we check on any new model, and it takes ten seconds. + +## Part 11: What we measured + +Once it was running, we compared all three ways of splitting the same model over the same 4 GPUs: tensor parallelism on its own, tensor parallelism plus expert parallelism, and pure pipeline parallelism. Same hardware, same flags otherwise, same benchmark. + +The benchmark is vLLM's own, 1024 tokens in and 256 tokens out per request, with `--ignore-eos` so every request generates exactly 256 tokens and the comparison is fair: + +```bash +docker exec vllm-tp4 vllm bench serve \ + --model Qwen/Qwen3-235B-A22B-Instruct-2507-FP8 \ + --served-model-name qwen3-235b \ + --base-url http://localhost:8000 \ + --dataset-name random --random-input-len 1024 --random-output-len 256 \ + --max-concurrency 1 --num-prompts 12 --seed 42 --ignore-eos +``` + +and then again with 32 requests in flight, which is the same command with two numbers changed: + +```bash + --max-concurrency 32 --num-prompts 128 +``` + +We ran both for every setup, because a single request at a time and 32 at a time behave completely differently, and a configuration that wins one can lose the other. + +### The memory side + +| | TP=4 | TP=4 plus EP | PP=4 | +| --- | --- | --- | --- | +| Weights per GPU | 55.19 GiB | 55.19 GiB | 55.70 GiB | +| KV cache per GPU | 27.85 GiB | 27.96 GiB | 26.84 GiB | +| Total KV cache | 621,392 tokens | **623,696 tokens** | 555,680 tokens | +| Max concurrency at 32k | 18.96x | **19.03x** | 16.96x | +| GPU memory used | 88,211 MiB on all 4 | 88,209 MiB on all 4 | 84,283 / 87,899 / 87,899 / 84,507 | + +Two things to pull out of that table. + +**Expert parallelism did not save memory.** It moved 0.37% of extra room into the cache, which is noise. If you were hoping expert parallelism would let you fit a model that otherwise does not fit, this is your warning that it will not. + +**Pipeline parallelism cost us 11.8% of the cache**, dropping from 621,392 tokens to 555,680, because a pipeline needs extra buffers for the activations travelling between stages, and that comes straight out of your conversation capacity. + +Look at the last row too. Under tensor parallelism all four cards sat at **exactly 88,211 MiB**, the same number on every one of them. Under pipeline parallelism they ranged from 84,283 to 87,899 MiB, about 3.6 GB apart, because a layer split cannot be perfectly even when 94 layers go over 4 GPUs and the ends of the model are not symmetric: the first stage carries the token embedding and the last carries the output head. That evenness check is the quickest sanity test you have that a tensor-parallel split is behaving. + +### The speed side + +| Measurement | TP=4 | TP=4 plus EP | PP=4 | Winner | +| --- | --- | --- | --- | --- | +| Median time per token, 1 request | **17.14 ms** | 18.83 ms | 21.19 ms | TP | +| Output tokens/sec, 32 requests | **503.68** | 470.93 | 296.48 | TP, by 70% over PP | +| Median time to first token, 32 requests | 3,233 ms | 3,705 ms | **2,735 ms** | PP, by 15% | +| Benchmark duration, 32 requests | **65.06 s** | 69.58 s | 110.52 s | TP | + +Tensor parallelism won nearly everything, and the size of one gap deserves attention: at 32 concurrent requests it produced **70% more tokens per second than pipeline parallelism**. That is not a rounding error, that is a different class of performance, and it lines up exactly with the theory from Part 4. Tensor parallelism has all four GPUs working on every token. Pipeline parallelism has each GPU working on a different request's stage, and with only 32 requests spread over 4 stages there is not enough in flight to keep everyone busy, so cards sit idle waiting for their turn. Its median time per token was 24% worse for the same reason. + +**Pipeline parallelism did win one thing, and it is the one theory predicts:** time to first token, by 15%. Processing your 1024-token prompt is where tensor parallelism's chatter gets expensive, because each of those 188 all-reduces is carrying the whole prompt's worth of data rather than a single token's. Pipeline parallelism just hands one activation tensor to the next stage and skips all of it. If your users judge you on how fast the first word appears, that is a real and measurable advantage. + +That is not a knock on expert parallelism, and it is important not to over-read it. Expert parallelism exists to solve a problem we do not have here: models so large that even a tensor-parallel split cannot hold all the experts, and clusters big enough that duplicating experts everywhere would be wasteful. With 4 GPUs and a model that already fits, we are asking it to do a job it was not designed for, and paying an extra network hop per token for nothing. On a 32 or 64 GPU deployment of a trillion-parameter model the answer would very likely flip. + +### The number we are throwing away, and why + +Being straight about this because it is a good lesson in reading your own benchmarks. The very first expert-parallel run at one-request-at-a-time reported **28.71 output tokens per second**, which would have made expert parallelism look catastrophic. It was not real. Look at the two TTFT figures from that run: + +``` +Mean TTFT (ms): 3987.38 +Median TTFT (ms): 265.56 +``` + +A mean fifteen times the median means one request behaved completely differently from the other eleven. One request stalled for about 45 seconds, almost certainly a one-off kernel compilation on the first pass through a code path, and that single stall stretched the whole benchmark from 63 seconds to 107 seconds. Since throughput is just tokens divided by wall-clock, one stall wrecked the headline number. + +This is why the table above uses **median time per token** as the decode measurement rather than aggregate throughput. Median per-token latency does not care that one request had a bad start. + +One more benchmarking trap while we are here. When we re-ran that same benchmark on the warm server, time to first token dropped from 265 ms to **61 ms**, which looks like a wonderful improvement and is actually meaningless: vLLM caches prompt prefixes by default, and we had just sent it those exact prompts with the same `--seed 42`. If you are comparing configurations, either vary the seed or turn prefix caching off, otherwise your second measurement is mostly measuring your cache. + +### What we would actually run + +For a 235B MoE on 4 GPUs with no NVLink between them, we would use plain `--tensor-parallel-size 4` and leave both of the others off. It was faster nearly everywhere, it gives the most conversation capacity, it splits memory perfectly evenly, and it is one less thing to reason about. + +We would reach for the other two in specific situations, not as general upgrades: + +- **Pipeline parallelism** if time to first token is the metric you are judged on, or if you are spanning multiple machines where the network between them is genuinely slow. It was 15% better at first-token latency and it barely uses the interconnect. +- **Expert parallelism** when the model is so large that even a tensor-parallel split cannot hold all the experts, which is a real problem at trillion-parameter scale and simply is not our problem at 235B on 4 cards. Here it cost 7% and returned nothing. + + +## Part 12: Errors you will actually hit + +Every one of these is a real message we collected while doing this, not a hypothetical. + +### "must be divisible by tensor parallel size" + +We asked for 3 GPUs, which is a perfectly reasonable-sounding thing to want, and got: + +``` +pydantic_core._pydantic_core.ValidationError: 1 validation error for VllmConfig + Value error, Total number of attention heads (64) must be divisible by tensor + parallel size (3). +``` + +**What it means:** the rule from Part 10. 64 heads cannot be shared out evenly among 3 GPUs. Good news, it fails in about a second, before loading a single byte of weights. + +**The fix:** pick a `--tensor-parallel-size` that divides your head count. Powers of two are the safe habit. + +### "Failed to load model - not enough GPU memory" + +Then we tried 2 GPUs, which puts about 110 GiB of weights on a 95 GiB card. It got most of the way through loading and then died: + +``` +ERROR [gpu_model_runner.py:5403] Failed to load model - not enough GPU memory. +Try lowering --gpu-memory-utilization to free memory for weights, increasing +--tensor-parallel-size, or using --quantization. +(original error: CUDA out of memory. Tried to allocate 768.00 MiB. GPU 0 has a +total capacity of 95.01 GiB of which 438.31 MiB is free. Including non-PyTorch +memory, this process has 94.57 GiB memory in use.) +``` + +**What it means:** exactly what it says. The weights for half this model do not fit on one of these cards. Note the useful detail in there, `438.31 MiB is free` out of `95.01 GiB`, so it filled the card almost exactly and then had nowhere to put the next 768 MiB chunk. + +**The fix:** vLLM lists the three real options itself, and for our case only one of them helps. Lowering `--gpu-memory-utilization` would make things worse, not better, because it reduces the space available for weights. Quantizing further would work but changes the model. So the answer is more GPUs, which is the whole point of this post. + +Worth knowing: this one is slow to fail, because it has to read and place most of the weights before it runs out. Budget several minutes, unlike the divisibility error which fails instantly. + +### "Unknown SF transformation", the one that cost us the most time + +This is the error we did not see coming, and it is worth the whole section. With 4 GPUs and everything sized correctly, all four workers died during startup: + +``` +RuntimeError: Assertion error (/workspace/.deps/deepgemm-src/csrc/apis/layout.hpp:60): +Unknown SF transformation +``` + +**What it means:** this model stores its FP8 weights in blocks, with a separate scale factor per 128x128 block, which you can see in its config as `"weight_block_size": [128, 128]`. vLLM hands that kind of matrix multiplication to a library called DeepGEMM, and DeepGEMM did not know how to lay out those scale factors ("SF" is scale factor) on our particular GPU. The RTX PRO 6000 is Blackwell, but it reports as `sm_120`, which is not the same silicon target as the data-centre Blackwell parts that DeepGEMM is usually exercised on. + +Notice how unhelpful the message is if you do not know that background. Nothing in it mentions FP8, quantization, or your GPU. + +**The fix**, which is one environment variable: + +```bash +docker run -d ... -e VLLM_USE_DEEP_GEMM=0 ... vllm/vllm-openai:latest ... +``` + +That tells vLLM to use its own FP8 kernels instead of DeepGEMM. Startup then went through cleanly. There is a performance cost to giving up a specialised kernel, so on hardware where DeepGEMM works you would leave it on. + +**The general lesson:** a quantized model is a contract between the checkpoint's format and a kernel that understands it. When a big quantized model fails to start on hardware that clearly has enough memory, suspect the kernel and the number format before you suspect your parallelism settings. + +### A confusing parse error when you try to run something else in the container + +``` +vllm serve: error: argument --compilation-config/-cc: Invalid JSON: expected value at line 2 +``` + +**What it means:** you ran `docker run ... vllm/vllm-openai:latest python3 -c "..."`, but the image's entrypoint is already `vllm serve`, so your Python source got handed to vLLM as a command-line argument. + +**The fix:** `--entrypoint python3`, as shown in Part 8. + +### "No available shared memory broadcast block found in 60 seconds" + +**What it means:** usually nothing. It shows up while vLLM is busy compiling or capturing CUDA graphs and the worker processes have not checked in for a minute. If it repeats forever and startup never finishes, then you probably forgot `--ipc=host` and the workers cannot pass data to each other through shared memory. + +**The fix:** add `--ipc=host`. If you already have it, wait a bit longer, because CUDA graph capture on a big model is genuinely slow. + + +## Wrapping up + +If you take five things away from this, let them be these. + +**One.** Inference is two jobs, not one. Prefill reads your whole prompt at once and is limited by compute; decode writes one token at a time and is limited by memory bandwidth. Every confusing multi-GPU result in this post traces back to that split, so when a change helps one metric and hurts the other, this is why. + +**Two.** Work out the memory on paper first. Parameters times bytes-per-parameter gives you the weights, and then remember that the weights are only one of three things that must fit, alongside the conversation cache and the working space. A model whose weights just barely fit is a model that cannot serve anybody. + +**Three.** "Splitting across GPUs" is three different things. Tensor parallelism slices every layer and makes all your GPUs work on the same token, at the cost of constant chatter. Pipeline parallelism cuts the layer stack into blocks and barely communicates, at the cost of GPUs waiting their turn. Expert parallelism only exists for mixture-of-experts models and hands out whole experts. You can combine them, and for big models you usually do. + +**Four.** Read the startup log. `Model loading took`, `Available KV cache memory`, `GPU KV cache size` and `Maximum concurrency` tell you, in four lines, whether your setup is sane and how many users it can actually hold. A negative cache number is the clearest error message in the whole stack. + +**Five.** Check `num_key_value_heads` in `config.json` before you plan your hardware. It, not the parameter count, is usually what limits how many GPUs you can split across cleanly. + +One last practical warning, because it cost us more than any GPU problem did. **Check your disk before you download.** A quarter of a terabyte of model weights on a shared machine is not just a storage question, it is a question about everything else living on that disk. Ours was a Kubernetes node, free space crossed the kubelet's eviction threshold, and it evicted the platform's own pods and garbage-collected locally-built images that no registry could replace. `df -h` first, and leave real headroom. + +Try it on whatever you have. Two GPUs are enough to see every concept in this post in action, and the log lines mean the same thing whether you are running 4 GPUs or 40. If you hit something we did not cover, tell us and we will add it. + +## Credits and references + +- The tensor parallel scheme is from **Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism** by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro: [arxiv.org/abs/1909.08053](https://arxiv.org/abs/1909.08053) +- vLLM parallelism and scaling guide: [docs.vllm.ai/en/latest/serving/parallelism_scaling.html](https://docs.vllm.ai/en/latest/serving/parallelism_scaling.html) +- vLLM memory and optimization docs: [conserving_memory](https://docs.vllm.ai/en/latest/configuration/conserving_memory.html) and [optimization](https://docs.vllm.ai/en/latest/configuration/optimization.html) +- Model card and config: [huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507-FP8](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507-FP8) +- Thanks to the vLLM maintainers, whose startup logging is the best free lesson in distributed inference available anywhere. diff --git a/content/blog/running-a-single-llm-across-two-gpus-with-vllm.md b/content/blog/running-a-single-llm-across-two-gpus-with-vllm.md deleted file mode 100644 index a1d0b1f6c..000000000 --- a/content/blog/running-a-single-llm-across-two-gpus-with-vllm.md +++ /dev/null @@ -1,362 +0,0 @@ ---- -title: "Running a single LLM across two GPUs with vLLM" -seoTitle: "Running a single LLM across two GPUs with vLLM" -seoDescription: "How tensor parallelism splits one model's weights across two cards, the memory math that tells you if it fits, and measured TP versus PP numbers on a pair of GPUs with no NVLink." -datePublished: 2026-08-18T10:00:00.000Z -slug: running-a-single-llm-across-two-gpus-with-vllm -author: saiyam-pathak -cover: /img/blog/running-a-single-llm-across-two-gpus-with-vllm/cover.png -tags: ["vllm", "gpu", "nvidia", "tensor-parallelism"] ---- - -Someone asked me this in a thread the other day, and it is such a good question that it deserves a full walkthrough: - -> Has anyone hosted a single LLM by splitting weights across 2 GPUs and served it through vLLM or another inference engine? I have a couple of A40 with 45GB usable VRAM. And want to host the BF16 variant as-is, like we have an RTX PRO 6000, you know, like on 2 cards. How can I do it and how does it work fundamentally, like are the weights split or what happens? - -Three questions hiding in there, so let's take them in order. Can you do it? Yes. How do you do it? One flag, mostly. And what actually happens to the weights? That is the interesting part, and it is where most people's mental model is a bit off. - -## What you will get from this post - -- The memory math that tells you whether your model fits on two cards, before you download 60 GB -- What tensor parallelism actually does to a weight matrix, layer by layer -- The exact vLLM commands, with real terminal output from a real run -- Why a pair of cards without an NVLink bridge might be faster with pipeline parallelism, and how to measure that yourself -- The A40-specific catches, because Ampere has one limitation that changes your options - -## The setup I tested on - -I need to be upfront about the hardware, because it matters for how you read the numbers. - -I do not have a pair of A40s. What I do have access to is a box with 8x NVIDIA RTX PRO 6000 Blackwell Server Edition cards, so I borrowed two of them and deliberately handicapped them to behave like A40s for the part that matters most, which is the memory budget. An A40 gives you roughly 45 GiB of usable VRAM, and at the usual `--gpu-memory-utilization 0.90` that leaves vLLM a budget of about 40.5 GiB per card. On a 95.01 GiB Blackwell card, the same 40.5 GiB budget is `--gpu-memory-utilization 0.426`, so that is what I used everywhere below. - -There is one thing I did not have to fake. Let's look at the interconnect: - -```console -$ nvidia-smi topo -m - GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 CPU Affinity NUMA Affinity -GPU0 X SYS SYS SYS SYS SYS SYS SYS 48-55,176-183 6 -GPU1 SYS X SYS SYS SYS SYS SYS SYS 32-39,160-167 4 -GPU2 SYS SYS X SYS SYS SYS SYS SYS 0-7,128-135 0 -GPU3 SYS SYS SYS X SYS SYS SYS SYS 16-23,144-151 2 -GPU4 SYS SYS SYS SYS X SYS SYS SYS 112-119,240-247 14 -GPU5 SYS SYS SYS SYS SYS X SYS SYS 96-103,224-231 12 -GPU6 SYS SYS SYS SYS SYS SYS X SYS 64-71,192-199 8 -GPU7 SYS SYS SYS SYS SYS SYS SYS X 80-87,208-215 10 - -Legend: - X = Self - SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI) - NV# = Connection traversing a bonded set of # NVLinks -``` - -Every pair says `SYS`, which means there is no NVLink anywhere on this box. Every GPU-to-GPU hop goes across PCIe and then across the CPU's own interconnect between NUMA nodes. If your two A40s do not have an NVLink bridge physically installed between them, and most people's do not, then you are in exactly this situation. That turns out to be the most important fact in this whole post, and I'll come back to it. - -The software, pinned: - -```console -vllm 0.27.1 -torch 2.13.0+cu130 cuda 13.0 -driver 610.43.02 -GPU 0: NVIDIA RTX PRO 6000 Blackwell Server Edition 95.01 GiB sm_120 -GPU 1: NVIDIA RTX PRO 6000 Blackwell Server Edition 95.01 GiB sm_120 -p2p 0<->1 True -``` - -For the model I picked **Qwen3-32B** in BF16, because it is the honest version of this question. At 32.8B parameters in bfloat16 it genuinely does not fit on one 45 GiB card, but it does fit on two, so the second card is doing real work rather than being a nice-to-have. - -## Why one card is not enough, in numbers - -Before touching any flags, let's do the arithmetic, because you can answer "will this fit" on paper in about a minute. - -A BF16 weight is 2 bytes. So the weights alone are `params x 2 bytes`. For Qwen3-32B that is about 61 GiB, and vLLM tells you the same thing when it reads the checkpoint: - -```console -INFO [weight_utils.py:867] Filesystem type for checkpoints: EXT4. Checkpoint size: 61.02 GiB. Available RAM: 1186.67 GiB. -``` - -61.02 GiB of weights against a 40.5 GiB budget on a single card. That is not close, and it is worth actually watching it fail, because the error message vLLM gives you here is one you will meet again: - -```console -$ docker run --gpus '"device=1"' ... vllm/vllm-openai:latest Qwen/Qwen3-32B \ - --tensor-parallel-size 1 --gpu-memory-utilization 0.426 --max-model-len 32768 - -INFO [model_runner.py:329] Model loading took 61.03 GiB and 16.146783 seconds -INFO [gpu_worker.py:563] Available KV cache memory: -24.42 GiB -ValueError: No available memory for the cache blocks. Try increasing `gpu_memory_utilization` -when initializing the engine. -``` - -**Available KV cache memory: -24.42 GiB.** I love this line. vLLM loaded the weights, then subtracted them and its activation overhead from the budget, and found it was 24 GiB in the hole before storing a single token of context. The suggestion to increase `gpu_memory_utilization` is a red herring here, since there is no value of it that makes 61 GiB fit in 45. - -{{two-gpu-memory-fit-animation}} - -So that is the wall. Now let's get over it. - -## What actually happens to the weights - -### First, the thing NVLink does not do - -Your question mentioned wanting the pair to behave "like we have an RTX PRO 6000", so let me clear up the most common misconception before anything else, because NVIDIA's own datasheet invites it. That datasheet advertises "48 GB GDDR6 memory with NVLink" and says it is "scalable up to 96 GB with NVLink", which certainly reads like two bridged cards turn into one 96 GB card. They do not. The footnote on that same page is where the real story is: - -> Connecting two NVIDIA A40 cards with NVLink to scale performance and memory capacity to 96 GB is only possible if your application supports NVLink technology. Please contact your application provider to confirm their support for NVLink. - -"Only possible if your application supports it" is carrying a lot of weight in that sentence. There is no mode, bridge or no bridge, where CUDA presents your two 48 GB cards to vLLM as a single 96 GB device. Each GPU keeps its own separate memory, and some piece of software has to deliberately cut the model up and coordinate the halves. NVLink never creates the pool, it only makes the conversation between the halves faster. Configuring that software is what the rest of this post is about. - -### Now the split itself - -The short answer to your actual question is **yes, the weights are genuinely split, and it happens inside each layer, not between layers.** - -The technique is called **tensor parallelism**, and it comes from the Megatron-LM paper by Shoeybi and colleagues at NVIDIA. The idea is that a transformer is mostly a stack of big matrix multiplications, and a big matrix multiplication can be cut into pieces that live on different GPUs. - -Take the MLP block in a layer. It is two matrix multiplies with a nonlinearity in between: `Y = GeLU(X x A)` then `Z = Y x B`. You could split the first matrix `A` by rows, but then you would have to glue the pieces back together before applying GeLU, because GeLU is nonlinear and `GeLU(a + b)` is not `GeLU(a) + GeLU(b)`. So Megatron splits `A` **column-wise** instead, and as the paper puts it, "the partitioning allows the GeLU nonlinearity to be independently applied to the output of each partitioned GEMM". Each GPU produces its own complete columns of `Y`, applies GeLU locally, and nobody has to talk to anybody. - -Then the second matrix `B` is split **row-wise**, which lines up perfectly with the column split of the first one. Each GPU multiplies its slice of `Y` by its slice of `B` and gets a partial sum of the final answer. Now, and only now, the GPUs have to add their partial sums together. That is one **all-reduce**. - -Drawn out, one MLP block across two cards looks like this: - -{{two-gpu-tensor-split-animation}} - -Notice what is split and what is not. The **weights** are split, each card holding half of `A` and half of `B` and never seeing the other half. The **activations** flowing through are replicated, so both cards start from the same full copy of `X` and both end up with the same full copy of `Z` after the all-reduce. That is the trade at the heart of tensor parallelism: you halve the weight memory, and you pay for it by keeping the activations in sync. - -A quick note if you go and read the Megatron paper, because the shapes have moved on since 2019. The paper describes a two-matrix MLP with a GeLU in the middle, which is what GPT-2 era models used. Qwen3 and most current models use SwiGLU instead, which has three matrices: `gate_proj`, `up_proj` and `down_proj`, and `silu` rather than GeLU (you can see `"hidden_act": "silu"` in the config below). The partitioning logic carries over unchanged though. `gate_proj` and `up_proj` are both column-parallel, they get multiplied together elementwise which stays local, and `down_proj` is row-parallel and produces the partial sums. Three matrices instead of two, still exactly one all-reduce. - -Attention works the same way, and the split is even more intuitive. The Q, K and V projections are cut column-wise "such that the matrix multiply corresponding to each attention head is done locally on one GPU". Qwen3-32B has 64 attention heads, so with two GPUs each card simply owns 32 whole heads and computes attention for them start to finish with no communication at all. The output projection is then row-wise, which again produces partial sums, which again need one all-reduce. - -Two matrix-multiply blocks, one all-reduce each. The paper states it plainly: this "enables us to perform all GEMMs in a simple transformer layer using only two all-reduces in the forward path and two in the backward path". Inference is forward-only, so for us it is **two all-reduces per layer**. - -Qwen3-32B has 64 layers. So generating a single token means **128 all-reduces**, in sequence, one after another, because layer 5 cannot start until layer 4 has finished exchanging. Hold that thought. - -### The KV cache splits too, and that is a bonus - -This part people often miss. Because each GPU owns a subset of the attention heads, it only needs to cache keys and values for *its own* heads. The KV cache is split right along with the weights. - -Qwen3-32B uses grouped-query attention with 8 key/value heads, so the cache per token for the whole model is: - -``` -2 (K and V) x 64 layers x 8 kv_heads x 128 head_dim x 2 bytes = 262,144 bytes = 256 KiB per token -``` - -With two GPUs, each card holds 4 of those 8 KV heads, so each card stores 128 KiB per token instead of the full 256 KiB. The cache is not duplicated across cards, it is divided, so the memory you free up by splitting the weights turns into context capacity rather than being eaten by a second copy of the cache. That is why the second card buys you two things at once, and it is the number I will check against reality further down. - -### The divisibility rule you need to check first - -Because heads are handed out whole, **your tensor parallel size has to divide your head counts**. Before you commit to a model, open its `config.json` and check. For Qwen3-32B: - -```json -{ - "num_hidden_layers": 64, - "hidden_size": 5120, - "num_attention_heads": 64, - "num_key_value_heads": 8, - "head_dim": 128, - "intermediate_size": 25600, - "torch_dtype": "bfloat16" -} -``` - -64 attention heads divided by 2 is 32, 8 KV heads divided by 2 is 4, and `intermediate_size` 25600 divided by 2 is 12800. All clean, so TP=2 will work. This is why some models refuse to run at TP=8 or TP=3 while being perfectly happy at TP=2, and it is a config-file question, not a mystery. - -## Doing it with vLLM - -After all that theory, the actual change is one flag. Let's run it: - -```bash -docker run -d --name vllm-tp2 \ - --gpus '"device=1,4"' --ipc=host -p 8101:8000 \ - -v /root/.cache/huggingface:/root/.cache/huggingface \ - -e HF_HUB_OFFLINE=1 -e HF_HOME=/root/.cache/huggingface \ - vllm/vllm-openai:latest Qwen/Qwen3-32B \ - --tensor-parallel-size 2 \ - --gpu-memory-utilization 0.426 \ - --max-model-len 32768 \ - --port 8000 -``` - -`--tensor-parallel-size 2` is the whole trick. On a real pair of A40s you would use `--gpu-memory-utilization 0.90` instead of my emulated `0.426`, and everything else stays the same. - -Two container details that will bite you if you skip them. `--ipc=host` matters because the tensor parallel workers are separate processes that talk over shared memory, and Docker's default 64 MB `/dev/shm` is not enough. And `--gpus '"device=1,4"'` with that exact nested quoting is how you hand Docker a specific pair of cards; inside the container they are renumbered 0 and 1. - -Now the proof that the split is real. Here is what vLLM logs on startup: - -```console -(Worker_TP0 pid=611) INFO [model_runner.py:329] Model loading took 30.59 GiB and 20.690370 seconds -(Worker_TP1 pid=612) INFO [model_runner.py:329] Model loading took 30.59 GiB and 20.456472 seconds -(Worker_TP0 pid=611) INFO [gpu_worker.py:563] Available KV cache memory: 8.22 GiB -(EngineCore pid=411) INFO [kv_cache_utils.py:2235] GPU KV cache size: 67,296 tokens -(EngineCore pid=411) INFO [kv_cache_utils.py:2236] Maximum concurrency for 32,768 tokens per request: 2.05x -``` - -**30.59 GiB on each worker**, and 30.59 doubled is 61.18, which is our 61.02 GiB checkpoint plus a rounding hair. There are two workers, `Worker_TP0` and `Worker_TP1`, one per GPU, each holding exactly half the model. The weights are not replicated. They are cut in half. - -And from outside the container: - -```console -$ nvidia-smi --query-gpu=index,memory.used --format=csv,noheader -1, 45171 MiB -4, 45171 MiB -``` - -Identical to the megabyte on both cards, which is what an even split looks like. - -Let's also check that the KV math I did earlier actually predicts reality. Each card reported 8.22 GiB free for cache, and I said each card stores 128 KiB per token: - -``` -8.22 GiB / 128 KiB = 67,338 tokens -``` - -vLLM reported 67,296. That is a match to within the rounding of "8.22", and it means you can predict your own context capacity on paper before you ever start the server. With `--max-model-len 32768`, 67,296 tokens of cache is 2.05 full-length requests in flight, which is exactly the `2.05x` vLLM printed. - -## What those all-reduces actually cost you - -So we are done, right? Two cards, model fits, `-tp 2`, ship it. - -Not quite. Remember those 128 sequential all-reduces per token. Let's think about how big each one actually is. An all-reduce after the attention or MLP block has to exchange a tensor of shape `[tokens_in_batch, hidden_size]`. At `hidden_size` 5120 in BF16, with a single request decoding one token at a time, that is: - -``` -1 token x 5120 x 2 bytes = 10,240 bytes = 10 KB -``` - -Ten kilobytes. That is nothing. The A40 datasheet lists its interconnect as "NVIDIA NVLink 112.5 GB/s (bidirectional), PCIe Gen4: 64GB/s", so NVLink is a bit under twice the bandwidth of the PCIe path. Neither number matters here, though, because you are not moving enough data to care about bandwidth at all. What you are paying is **latency**, 128 times per token, and every one of those hops on a no-NVLink box goes out over PCIe and across the CPU's NUMA interconnect. - -This is why vLLM's own documentation gives advice that surprises people. Straight from their parallelism guide: - -> if the GPUs on the node do not have NVLINK interconnect (e.g. L40S), leverage pipeline parallelism instead of tensor parallelism for higher throughput and lower communication overhead. - -**Pipeline parallelism** splits the model a completely different way: by layers, not inside them. With PP=2 and 64 layers, GPU 0 gets layers 0 to 31 and GPU 1 gets layers 32 to 63. Your memory problem is solved just as well, since each card still holds half the weights. But the communication is utterly different. Instead of 128 all-reduces per token, GPU 0 finishes its 32 layers and hands one activation tensor to GPU 1, once. One point-to-point send instead of 128 collectives. - -The cost is that PP is a relay race. With a single request in flight, GPU 1 sits idle while GPU 0 works, then GPU 0 sits idle while GPU 1 works, so you are using half your silicon at any moment. vLLM notes this too, saying that increasing pipeline parallel size "may cause latency penalties". PP pays off when you have enough concurrent requests to keep both stages busy at once, which is what continuous batching gives you. - -{{two-gpu-tp-vs-pp-animation}} - -So the honest answer is that TP and PP trade against each other, the crossover depends on your interconnect and your concurrency, and you should measure it on your own box. Which is what I did. - -## TP=2 vs PP=2, measured - -Switching to pipeline parallelism is the same kind of one-flag change: - -```bash -docker run -d --name vllm-pp2 \ - --gpus '"device=1,4"' --ipc=host -p 8102:8000 \ - -v /root/.cache/huggingface:/root/.cache/huggingface \ - -e HF_HUB_OFFLINE=1 -e HF_HOME=/root/.cache/huggingface \ - vllm/vllm-openai:latest Qwen/Qwen3-32B \ - --pipeline-parallel-size 2 \ - --gpu-memory-utilization 0.426 \ - --max-model-len 32768 \ - --port 8000 -``` - -And it splits the weights just as effectively, which you can see in the workers being named `PP` instead of `TP` now: - -```console -(Worker_PP0 pid=611) INFO [model_runner.py:329] Model loading took 30.52 GiB and 9.017490 seconds -(Worker_PP1 pid=612) INFO [model_runner.py:329] Model loading took 30.52 GiB and 9.547118 seconds -(Worker_PP0 pid=611) INFO [gpu_worker.py:563] Available KV cache memory: 6.92 GiB -(EngineCore pid=411) INFO [kv_cache_utils.py:2235] GPU KV cache size: 56,640 tokens -(EngineCore pid=411) INFO [kv_cache_utils.py:2236] Maximum concurrency for 32,768 tokens per request: 1.73x -``` - -### The memory difference shows up first - -Look at the KV cache: **56,640 tokens with PP against 67,296 with TP**, on identical hardware and an identical memory budget. Pipeline parallelism gave me 18.8% less usable context. - -The per-token cost per card is actually the same in both modes, which is a nice coincidence worth understanding. Under TP each card holds all 64 layers but only 4 of the 8 KV heads. Under PP each card holds all 8 KV heads but only 32 layers. `64 x 4` and `32 x 8` are the same number, so both come out at 128 KiB per token per card. - -The difference is pure overhead. Subtracting weights and cache from the 40.47 GiB budget, TP left 1.66 GiB of overhead per card and PP left 3.03 GiB, because the pipeline needs extra buffers for activations in flight between the stages. That overhead comes straight out of your context capacity. - -There is a second, smaller difference worth knowing about. Tensor parallelism divided the memory perfectly evenly, while pipeline parallelism did not: - -```console -# TP=2 -1, 45171 MiB -4, 45171 MiB - -# PP=2 -1, 40701 MiB -4, 43667 MiB -``` - -Identical to the megabyte under TP, and about 3 GB apart under PP. That is because a layer split cannot be perfectly even when the ends of the model are not symmetric: the first stage carries the token embedding, the last stage carries the final norm and the language modelling head. It rarely matters at PP=2 on matched cards, but it is exactly the kind of thing that bites you if you ever try to split across two cards of *different* sizes, since your headroom is set by whichever card ends up fuller. - -### Now the throughput - -Same benchmark for both, `vllm bench serve` with a random dataset at 1024 input and 256 output tokens, `--ignore-eos` so every request generates exactly 256 tokens, run at concurrency 1 and again at concurrency 32: - -```bash -vllm bench serve --model Qwen/Qwen3-32B --base-url http://localhost:8000 \ - --dataset-name random --random-input-len 1024 --random-output-len 256 \ - --max-concurrency 1 --num-prompts 16 --seed 42 --ignore-eos -``` - -Before the table, one caveat that I want to put right next to the numbers rather than bury at the end. The memory results above transfer to your A40s directly, because I matched the memory budget on purpose and weight splitting does not care what architecture it runs on. The **throughput** results do not transfer as cleanly, and not simply because Blackwell is faster in absolute terms. The ratio between compute time and communication time is what decides where TP stops winning, and two things move that ratio in opposite directions on your hardware: an A40's slower compute makes each layer's math take longer, which hides the all-reduce latency and helps TP, while PCIe Gen4 instead of Gen5 makes each all-reduce cost more, which hurts TP. I cannot tell you which effect dominates on your box. So read the shape of the result below, not the absolute tok/s, and run the same two commands yourself. - -| Metric | TP=2 | PP=2 | Winner | -|---|---|---|---| -| **Concurrency 1** | | | | -| Output token throughput | 36.41 tok/s | 21.00 tok/s | TP by 73% | -| Median TPOT (per-token latency) | 26.38 ms | 46.96 ms | TP by 44% | -| Median TTFT (time to first token) | 296.37 ms | 208.57 ms | PP by 30% | -| **Concurrency 32** | | | | -| Output token throughput | 496.60 tok/s | 487.56 tok/s | TP by 1.9% | -| Median TPOT | 47.22 ms | 56.40 ms | TP by 16% | -| Median TTFT | 3892.42 ms | 2468.40 ms | PP by 37% | -| Benchmark duration | 65.99 s | 67.21 s | TP by 1.8% | -| **Capacity** | | | | -| KV cache | 67,296 tokens | 56,640 tokens | TP by 19% | - -Let's read what actually happened here, because it is not the clean story the documentation led me to expect. - -**At concurrency 1, tensor parallelism wins convincingly**, 36.41 tok/s against 21.00, and that is exactly the relay-race effect. With one request in flight, PP has one card working and one card waiting at all times, so you get roughly one card's worth of decode speed. TP has both cards grinding on every single token, and since decode speed is mostly about memory bandwidth, using two cards' worth of bandwidth on one request is a real and large win. This is the thing PP fundamentally cannot give you. - -**At concurrency 32, the two are effectively tied.** 496.60 against 487.56 tok/s is a 1.9% gap, which is close enough to run-to-run noise that I would not make a decision on it. This is where I have to be straight with you: vLLM's docs say that without NVLink you should "leverage pipeline parallelism instead of tensor parallelism for higher throughput", and on this box **that did not reproduce**. PP never got ahead on throughput, it just caught up. I would guess that is because these cards sit on PCIe Gen5 rather than Gen4, so the all-reduces are cheaper than the guidance assumes, and because at concurrency 32 the all-reduce payload is 32 tokens wide rather than 1, which uses the link far more efficiently. On your Gen4 A40s the gap will be less favourable to TP than what I measured. Whether it crosses over, I genuinely do not know, which is the whole reason I am telling you to measure rather than handing you a verdict. - -**The one place PP clearly wins is time to first token**, by 30% at concurrency 1 and 37% at concurrency 32. That one took me a moment to see, and it makes sense once you think about payload sizes. Prefill processes your whole 1024-token prompt at once, so each of TP's 128 all-reduces is moving `1024 x 5120 x 2 bytes`, about 10 MB, not the 10 KB a single decode step moves. Suddenly you *are* bandwidth-bound, and 128 ten-megabyte collectives over PCIe is a real cost. PP moves one activation tensor between stages and skips all of it. - -So the shape of the answer, on a box with no NVLink: - -- Interactive, low concurrency, one user at a time: **use TP**. It is not close. -- High concurrency batch throughput: **either**, they tie, so pick TP for the extra 19% of KV cache. -- Long prompts where users are staring at a spinner waiting for the first token: **PP is worth testing**, it was meaningfully faster at prefill in both runs. - -For your A40s I would still start with `--tensor-parallel-size 2`, because it won or tied on every throughput measure here and it gives you more context capacity. Then run these exact two benchmarks with `--pipeline-parallel-size 2` and see whether your slower interconnect changes the verdict. - -## The A40-specific things to know - -A few points that apply to your cards specifically rather than to multi-GPU serving in general. - -**An NVLink bridge is available, and it is worth hunting for.** The A40 does support NVLink, at 112.5 GB/s bidirectional between a pair, via a physical bridge connector you install between two cards. If you have two A40s in one chassis and you can get the bridge, do it before you spend a week tuning flags. It turns the `SYS` line in your topology into `NV#`, and since tensor parallelism already won on my bridge-less box, cheaper all-reduces can only widen that lead and take the decision off your plate entirely. Check what you have today with `nvidia-smi topo -m`, exactly as I did above. - -**FP8 will not save you the way it saves a newer card.** This is the Ampere limitation that changes your options. vLLM's docs are explicit: "FP8 computation is supported on NVIDIA GPUs with compute capability >= 8.9 (Ada Lovelace, Hopper)." The A40 is compute capability 8.6, so it misses that by one minor version. You are not entirely locked out, because "Turing/Ampere GPUs are supported for W8A16 (weight-only FP8) utilizing Marlin kernels", which stores weights at 8 bits and computes in 16. That is a genuinely useful trick for memory: it would take Qwen3-32B's weights from 61 GiB to roughly 31 GiB and let it run on a **single** A40. But you do not get the compute speedup that an Ada or Blackwell card gets from FP8, and you did say you want BF16 as-is, so I mention it only as the escape hatch it is. - -**Both cards read the whole checkpoint.** A small operational note from vLLM's docs that surprises people watching disk I/O: with tensor parallelism "each process will read the whole model and split it into chunks", so startup reads scale with your TP size rather than being divided by it. - -## So should you just buy one RTX PRO 6000 instead? - -Your question framed it as wanting your two A40s to behave "like we have an RTX PRO 6000", so let's compare properly, because on capacity they look similar and on behaviour they are not. - -Two A40s give you about 90 GiB of aggregate VRAM. A single RTX PRO 6000 Blackwell gives 96 GiB on one card. Similar pool, and for pure "does the model fit" purposes they are close to equivalent. - -The differences that actually decide it: - -- **A single card has no interconnect tax at all.** No all-reduces, no PCIe hops, no NVLink bridge to source, no TP-versus-PP tuning. Everything in this post stops being your problem. -- **Blackwell has FP8 and FP4, Ampere has neither.** That is the bigger gap, honestly, and it decides what fits rather than only how fast it runs. A model you can only serve in BF16 on A40s might serve in FP8 on one Blackwell card, in half the memory, at full speed. -- **Two cards give you more aggregate memory bandwidth.** Two A40s is 2 x 696 GB/s of it, and decode speed is largely a memory-bandwidth story. With tensor parallelism you genuinely do get to use both cards' bandwidth on one request, which is a real advantage of TP that PP does not give you. - -My take: if you already own the two A40s, use them, because tensor parallelism works and the setup above is maybe twenty minutes of work. Find out whether you can get the NVLink bridge. If you are spending new money and you are choosing between two more A40s and one Blackwell card, buy the single newer card, mostly for FP8 rather than for avoiding the multi-GPU complexity. - -## Wrapping up - -The mental model to walk away with is that tensor parallelism cuts every big matrix in every layer down the middle, hands each GPU whole attention heads, and pays for it with two all-reduces per layer. That is why it fixes your memory problem completely and your throughput problem only conditionally, because those all-reduces are cheap over NVLink and expensive over PCIe. Pipeline parallelism cuts the stack by layers instead, communicates almost nothing, and needs concurrency to keep both cards busy. - -For your two A40s, start with `--tensor-parallel-size 2`, run the same two benchmarks I ran above at your real concurrency, then try `--pipeline-parallel-size 2` and keep whichever wins. Both of them solve the fitting problem, so you are only choosing on speed, and it is a ten-minute experiment on your own hardware which beats anyone's opinion including mine. - -Give it a try and let me know how it goes, especially if you get an NVLink bridge on those A40s, because I would love to see the before-and-after numbers on real Ampere silicon. - -## Credits and references - -- The tensor parallel scheme comes from **Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism** by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro: [arxiv.org/abs/1909.08053](https://arxiv.org/abs/1909.08053) -- vLLM parallelism and scaling guide: [docs.vllm.ai/en/latest/serving/parallelism_scaling.html](https://docs.vllm.ai/en/latest/serving/parallelism_scaling.html) -- vLLM conserving memory and optimization docs: [docs.vllm.ai/en/latest/configuration/conserving_memory.html](https://docs.vllm.ai/en/latest/configuration/conserving_memory.html) and [optimization.html](https://docs.vllm.ai/en/latest/configuration/optimization.html) -- vLLM FP8 quantization support matrix: [docs.vllm.ai/en/latest/features/quantization/llm_compressor/fp8/](https://docs.vllm.ai/en/latest/features/quantization/llm_compressor/fp8/) -- NVIDIA A40 datasheet, for the 48 GB GDDR6, 696 GB/s and 112.5 GB/s NVLink figures: [nvidia.com A40 datasheet](https://images.nvidia.com/content/Solutions/data-center/a40/nvidia-a40-datasheet.pdf) -- Qwen3-32B model card and config: [huggingface.co/Qwen/Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) diff --git a/lib/_blog-feed-data.js b/lib/_blog-feed-data.js index 434f3996b..6e7748a53 100644 --- a/lib/_blog-feed-data.js +++ b/lib/_blog-feed-data.js @@ -1,5 +1,17 @@ // AUTO-GENERATED by scripts/generate-feeds.mjs. Do not edit by hand. export const FEED_POSTS = [ + { + "slug": "running-a-big-llm-across-multiple-gpus-with-vllm", + "title": "Running a big LLM across multiple GPUs with vLLM", + "description": "A plain-English guide to serving a model that is too big for one GPU: how tensor, pipeline, and expert parallelism split it up, what every vLLM flag does, and measured numbers from a 235B model on four RTX PRO 6000 cards.", + "datePublished": "2026-08-18T10:00:00.000Z", + "cover": "/img/blog/running-a-big-llm-across-multiple-gpus-with-vllm/cover.png", + "tags": [ + "vllm", + "gpu", + "nvidia" + ] + }, { "slug": "local-llm-glossary", "title": "The Local LLM Glossary: Every Term, Flag, and Number in Plain English", @@ -343,17 +355,5 @@ export const FEED_POSTS = [ "docker-images", "docker-container" ] - }, - { - "slug": "day-3-stop-writing-dockerfiles-from-scratch", - "title": "Day 3: Stop Writing Dockerfiles From Scratch", - "description": "Stop writing Dockerfiles from scratch. A Docker Captain walks through docker init, layer caching, multi-stage builds, and docker debug for 2026.", - "datePublished": "2026-04-24T17:10:21.609Z", - "cover": "/img/blog/day-3-stop-writing-dockerfiles-from-scratch/9a9c22be-d40d-4d2e-a85a-9877ce728557.svg", - "tags": [ - "docker", - "dockerfile", - "docker-images" - ] } ]; diff --git a/lib/markdown.js b/lib/markdown.js index 23686f632..17330723f 100644 --- a/lib/markdown.js +++ b/lib/markdown.js @@ -23,9 +23,10 @@ import HAMiBlastRadiusAnimation from '@/components/HAMiBlastRadiusAnimation'; import HAMiRequestFlowAnimation from '@/components/HAMiRequestFlowAnimation'; import HAMiSlotMathAnimation from '@/components/HAMiSlotMathAnimation'; import DynamicMigLifecycleAnimation from '@/components/DynamicMigLifecycleAnimation'; -import TwoGpuTensorSplitAnimation from '@/components/TwoGpuTensorSplitAnimation'; -import TwoGpuMemoryFitAnimation from '@/components/TwoGpuMemoryFitAnimation'; -import TwoGpuTpVsPpAnimation from '@/components/TwoGpuTpVsPpAnimation'; +import MultiGpuSplitModesAnimation from '@/components/MultiGpuSplitModesAnimation'; +import MultiGpuTensorSplitAnimation from '@/components/MultiGpuTensorSplitAnimation'; +import MultiGpuMemoryFitAnimation from '@/components/MultiGpuMemoryFitAnimation'; +import MoeExpertRoutingAnimation from '@/components/MoeExpertRoutingAnimation'; import CodeBlock from '@/components/CodeBlock'; const BLOG_SHORTCODES = { @@ -44,9 +45,10 @@ const BLOG_SHORTCODES = { '{{hami-request-flow-animation}}': 'hami-request-flow-animation', '{{hami-slot-math-animation}}': 'hami-slot-math-animation', '{{dynamic-mig-lifecycle-animation}}': 'dynamic-mig-lifecycle-animation', - '{{two-gpu-tensor-split-animation}}': 'two-gpu-tensor-split-animation', - '{{two-gpu-memory-fit-animation}}': 'two-gpu-memory-fit-animation', - '{{two-gpu-tp-vs-pp-animation}}': 'two-gpu-tp-vs-pp-animation', + '{{multi-gpu-split-modes-animation}}': 'multi-gpu-split-modes-animation', + '{{multi-gpu-tensor-split-animation}}': 'multi-gpu-tensor-split-animation', + '{{multi-gpu-memory-fit-animation}}': 'multi-gpu-memory-fit-animation', + '{{moe-expert-routing-animation}}': 'moe-expert-routing-animation', }; function remarkBlogShortcodes() { @@ -119,9 +121,10 @@ const processor = unified() 'hami-request-flow-animation': HAMiRequestFlowAnimation, 'hami-slot-math-animation': HAMiSlotMathAnimation, 'dynamic-mig-lifecycle-animation': DynamicMigLifecycleAnimation, - 'two-gpu-tensor-split-animation': TwoGpuTensorSplitAnimation, - 'two-gpu-memory-fit-animation': TwoGpuMemoryFitAnimation, - 'two-gpu-tp-vs-pp-animation': TwoGpuTpVsPpAnimation, + 'multi-gpu-split-modes-animation': MultiGpuSplitModesAnimation, + 'multi-gpu-tensor-split-animation': MultiGpuTensorSplitAnimation, + 'multi-gpu-memory-fit-animation': MultiGpuMemoryFitAnimation, + 'moe-expert-routing-animation': MoeExpertRoutingAnimation, pre: CodeBlock, }, }); diff --git a/public/_redirects b/public/_redirects index bff5d41f7..3e2e5eea9 100644 --- a/public/_redirects +++ b/public/_redirects @@ -183,6 +183,7 @@ /blog/qwen3-8-27b-on-dgx-spark /qwen3-8-27b-on-dgx-spark 301! /blog/rancher-desktop-evolution /rancher-desktop-evolution 301! /blog/ready-for-wasm-day-2023 /ready-for-wasm-day-2023 301! +/blog/running-a-big-llm-across-multiple-gpus-with-vllm /running-a-big-llm-across-multiple-gpus-with-vllm 301! /blog/sharing-gpus-in-kubernetes-with-hami /sharing-gpus-in-kubernetes-with-hami 301! /blog/simplified-introduction-to-bacalhau /simplified-introduction-to-bacalhau 301! /blog/slicing-gpus-in-kubernetes-with-nvidia-mig /slicing-gpus-in-kubernetes-with-nvidia-mig 301! @@ -408,6 +409,7 @@ /qwen3-8-27b-on-dgx-spark /blog/qwen3-8-27b-on-dgx-spark 200! /rancher-desktop-evolution /blog/rancher-desktop-evolution 200! /ready-for-wasm-day-2023 /blog/ready-for-wasm-day-2023 200! +/running-a-big-llm-across-multiple-gpus-with-vllm /blog/running-a-big-llm-across-multiple-gpus-with-vllm 200! /sharing-gpus-in-kubernetes-with-hami /blog/sharing-gpus-in-kubernetes-with-hami 200! /simplified-introduction-to-bacalhau /blog/simplified-introduction-to-bacalhau 200! /slicing-gpus-in-kubernetes-with-nvidia-mig /blog/slicing-gpus-in-kubernetes-with-nvidia-mig 200! diff --git a/public/_worker.js b/public/_worker.js index f13b5074b..9e3dff2b7 100644 --- a/public/_worker.js +++ b/public/_worker.js @@ -795,7 +795,7 @@ async function handleNewsletterApi(request, env) { const KUBESIMPLIFY_ROUTES = new Set(['/about', '/workshops', '/partnerships', '/resources', '/products', '/learn', '/privacy']); const KUBESIMPLIFY_PREFIXES = ['/products/', '/learn/']; -const BLOG_SLUGS = new Set(["10-things-you-might-not-know-about-k9s","12-practical-grep-command-examples-in-linux","a-beginners-guide-to-dualbooting-windows-with-ubuntu-part-1","a-beginners-guide-to-dualbooting-windows-with-ubuntu-part-2","a-complete-walk-through-of-devops","a-kubeconfig-for-gke-that-doesnt-need-gcloud","a-simple-way-to-structure-your-terraform-code","a-simplified-guide-to-yaml","about-my-pdf-editor-project","an-overview-of-gitops-and-argocd","announcing-buildsafe","api-response-in-go","arkade","automate-repetitive-tasks-shell-scripting","automated-github-releases-with-github-actions-and-conventional-commits","avoid-overspending-with-kubecost","aws-elastic-cloud-compute","bake-your-container-images-with-bake","become-a-hashicorp-certified-terraform-associate-preparation-guide","best-devops-tools-2025","bonsai-27b-rtx-pro-6000-dgx-spark","breaking-down-docker","building-a-zero-cve-strategy","building-apigateway-with-lambda-using-pulumi","certified-kubernetes-security-specialist-cks-2022-exam-guide","cicd-pipeline-github-actions-with-aws-ecs","ckad-exam-april-2022","claude-code-leak-what-the-source-actually-teaches","clawspark-your-private-openclaw-ai-assistant-that-never-phones-home","cloud-computing","cloud-native-buildpacks-concepts","confidential-containers-running-on-kubernetes","container-and-kubernetes-security","controlling-mcp-tools-with-agentgateway-on-kubernetes","coolify","creating-multi-node-kubernetes-cluster-locally","day-1-the-local-llm-revolution-why-your-desk-just-became-the-new-datacenter","day-1-what-actually-happens-when-you-type-docker-run","day-2-anatomy-of-an-llm-inference-request-from-prompt-to-answer-step-by-step","day-2-your-images-are-a-supply-chain-and-it-s-probably-broken","day-3-stop-writing-dockerfiles-from-scratch","day-3-the-dgx-spark-unpacked-gb10-unified-memory-sm-121-and-the-one-reason-this-hardware-exists","day-4-breaking-isolation-on-purpose-volumes-networks-and-the-real-world","day-4-quantization-demystified-bf16-fp8-nvfp4-mxfp4-int4-gguf-and-why-it-all-matters","day-5-docker-compose-how-docker-actually-gets-used","day-5-local-llm-inference-engines-wrappers-and-what-to-pick","day-6-run-an-llm-on-your-laptop-with-docker","day-7-ship-it-and-what-comes-next","deploy-a-maven-project-on-a-tomcat-server-using-jenkins-and-aws","deploy-a-simple-server-using-aws-terraform","deploying-java-application-using-docker-and-kubernetes-devops-project","devin-outposts-on-kubernetes","ditch-the-overheating-laptop-supercharge-your-docker-workflow-with-docker-offload","diy-how-to-build-a-kubernetes-policy-engine","docker-captain-journey","docker-mcp-catalog","docker-networking-demystified","dynamic-mig-in-kubernetes-with-hami","embed-http-servers-in-wasm-with-rust-and-csharp","enhancing-runtime-security-with-falco-my-hands-on-experience","ephemeral-pull-request-environment-using-vcluster","essential-linux-commands-for-devops","event-driven-architecture-simplified-monolith-to-microservices","everything-you-need-to-know-about-docker-compose","everything-you-need-to-know-about-the-linux-ls-command","exploiting-metasploitable2-using-msfconsole-kali-linux-lab","firewall-a-networks-gatekeeper","four-pillars-of-observability-in-kubernetes","get-good-at-git","getting-started-with-kind-creating-a-multi-node-local-kubernetes-cluster","getting-started-with-ko-a-fast-container-image-builder-for-your-go-applications","getting-started-with-kyverno","git-and-github-a-beginners-guide","github-actions-101-what-are-github-actions-and-how-to-use-them-a-beginners-guide","gitops-demystified","ha-kubernetes","how-a-kubernetes-service-actually-works-and-all-5-types-you-need","how-get-started-with-hashicorp-vault","how-kubernetes-endpointslices-actually-work-and-why-endpoints-had-to-die","how-to-backup-kubernetes-with-kasten-community-edition","how-to-change-directory-in-shell-scripts","how-to-install-a-kubernetes-cluster-with-kubeadm-containerd-and-cilium-a-hands-on-guide","how-to-setup-your-ftp-server-in-linux","implementing-kubernetes-network-policies-a-comprehensive-guide","important-concepts-of-operating-systems","ing-switch-119-annotations-gateway-api-traefik-impact-ratings","ing-switch-migrate-from-ingress-nginx-to-traefik-or-gateway-api-in-minutes-not-days","installing-prometheus-with-selinux","introducing-kiac-kubernetes-in-apple-containers","introducing-unikraft-lightweight-virtualization-using-unikernels","introduction-of-jenkins-pipeline","introduction-to-cicd-and-cicd-pipeline","introduction-to-cri","introduction-to-developer-platforms-with-gimlet","introduction-to-helm","introduction-to-jenkins","introduction-to-kubernetes","introduction-to-terraform","iptables-demo","istio-service-mesh","k8sgpt-tutorial-when-kubernetes-meets-ai","keptn-getting-started","ksctl-making-kubernetes-easy-across-clouds","kube-proxy-deep-dive","kube-scheduler-deep-dive","kubecon-cloudnativecon-north-america-2024-recap-themes-innovations-and-community-spirit","kubecon-cloudnativecon-rejekts-and-wasm-io-wrap-up-a-leap-into-the-future-with-webassembly-ai-and-sustainable-cloud-practices","kubectl-run-nginx-inside","kubeflow-machine-learning-on-kubernetes-part-1","kubeflow-notebooks-ml-experimentation-made-easier-part-2","kubeflow-pipelines-orchestrating-machine-learning-workflows-part-3","kubernetes-125-dockerd","kubernetes-126","kubernetes-access-control-with-authentication-authorization-admission-control","kubernetes-adoption-key-challenges-in-migrating-to-kubernetes","kubernetes-backup-using-cloudcasa","kubernetes-containerd-setup","kubernetes-crio","kubernetes-management-with-rust-a-dive-into-generic-client-go-controller-abstractions-and-crd-macros-with-kubers","kubernetes-on-apple-macbooks-m-series","kubernetes-scheduling-the-complete-guide","kubernetes-v133-key-features-updates-and-what-you-need-to-know","kubernetes-v135-whats-new-whats-changing-and-what-you-should-know","kubesimplify-a-journey-to-remember","kubesimplify-at-wasmio-and-kubecon-eu-2024","kyverno-and-cosign","kyverno-cli","lets-learn-terraform","lets-simplify-golang-part-1","lets-simplify-golang-part-2","lets-simplify-golang-part-3","lets-talk-about-ansible","linux-boot-process-simplified","linux-system-directories-explained","llm-costs-and-observability-with-agentgateway-on-kubernetes","local-llm-glossary","managing-contexts-in-kubernetes-with-plugins","managing-your-operating-system-with-package-managers","mastering-kubernetes-costs-from-monitoring-to-automation","microservices","mlxcel-rust-native-inference-engine-tested-on-m1-max","moving-code-between-git-repositories-with-copybara","multi-stage-docker-build","multi-tenancy-in-2025-and-beyond","my-first-international-conference-open-source-summit-2022","my-journey-to-kubestronaut-on-kubernetes-10th-birthday","my-kubecon-euvirtual-experience","my-schedule-for-kubecon-cloudnativecon-eu-2022","navigating-through-cncf-landscape","nemotron-3-5-lightning-on-dgx-spark","nemotron3-on-dgx-spark","networking-fundamentals-for-devops","nexus-repository-manager-what-is-it-and-how-to-configure-it-on-a-digital-ocean-droplet","nudgebee-ai-sre-copilot-hands-on","nvcf-is-now-open-source-inside-nvidia-s-gpu-function-platform","operating-systems-101-essential-knowledge-for-devopssre-engineers","optimizing-kubernetes-costs-balancing-spot-and-on-demand-instances-with-topology-spread-constraints","optimizing-scalability-a-deep-dive-into-load-testing-with-locust-on-eks","package-managers-demystified","perform-crud-operations-on-kubernetes-using-golang","platform-engineering-demystified-navigating-the-basics","pods-in-kubernetes","practical-guide-to-kubernetes-api","progressive-rollouts-with-argo-cd-rollouts","prometheus-explained","pure-cilium-a-guide-for-local-load-balancing-and-bgp","quick-bites-of-fluxcd-health-assessment","qwen3-8-27b-on-dgx-spark","rancher-desktop-evolution","ready-for-wasm-day-2023","sharing-gpus-in-kubernetes-with-hami","simplified-introduction-to-bacalhau","slicing-gpus-in-kubernetes-with-nvidia-mig","speeding-up-using-microk8s","ssh-into-your-dgx-spark-from-anywhere-in-the-world-using-tailscale","starting-your-devops-journey-as-a-windows-user","statefulsets","supply-chain-security-using-slsa-part-1-fundamentals","supply-chain-security-using-slsa-part-2-the-framework","terraform-best-practices","testing-docker-ais-gordon-how-smart-is-it","the-complete-guide-to-the-dd-command-in-linux","the-secret-gems-behind-building-container-images-enter-buildkit-and-docker-buildx","the-ultimate-guide-to-audit-logging-in-kubernetes-from-setup-to-analysis","the-webassembly-course","tutorial-build-a-cloud-cost-monitoring-system-with-terraform-ansible-and-komiser","understanding-docker-desktop-all-in-one-platform-for-containers","understanding-etcd-in-kubernetes-a-beginners-guide","understanding-how-containers-work-behind-the-scenes","understanding-the-architecture-of-kubernetes-a-beginners-guide","understanding-the-ins-and-outs-of-git-using-github","wandler-local-openai-compatible-inference-transformersjs-webgpu","what-is-reproducibility-and-why-does-it-matter","what-is-shell-scripting","why-are-network-policies-in-kubernetes-so-hard-to-understand","why-devops-case-study","wtf-is-linux-shell-command-substitution","yours-kindly-drone"]); +const BLOG_SLUGS = new Set(["10-things-you-might-not-know-about-k9s","12-practical-grep-command-examples-in-linux","a-beginners-guide-to-dualbooting-windows-with-ubuntu-part-1","a-beginners-guide-to-dualbooting-windows-with-ubuntu-part-2","a-complete-walk-through-of-devops","a-kubeconfig-for-gke-that-doesnt-need-gcloud","a-simple-way-to-structure-your-terraform-code","a-simplified-guide-to-yaml","about-my-pdf-editor-project","an-overview-of-gitops-and-argocd","announcing-buildsafe","api-response-in-go","arkade","automate-repetitive-tasks-shell-scripting","automated-github-releases-with-github-actions-and-conventional-commits","avoid-overspending-with-kubecost","aws-elastic-cloud-compute","bake-your-container-images-with-bake","become-a-hashicorp-certified-terraform-associate-preparation-guide","best-devops-tools-2025","bonsai-27b-rtx-pro-6000-dgx-spark","breaking-down-docker","building-a-zero-cve-strategy","building-apigateway-with-lambda-using-pulumi","certified-kubernetes-security-specialist-cks-2022-exam-guide","cicd-pipeline-github-actions-with-aws-ecs","ckad-exam-april-2022","claude-code-leak-what-the-source-actually-teaches","clawspark-your-private-openclaw-ai-assistant-that-never-phones-home","cloud-computing","cloud-native-buildpacks-concepts","confidential-containers-running-on-kubernetes","container-and-kubernetes-security","controlling-mcp-tools-with-agentgateway-on-kubernetes","coolify","creating-multi-node-kubernetes-cluster-locally","day-1-the-local-llm-revolution-why-your-desk-just-became-the-new-datacenter","day-1-what-actually-happens-when-you-type-docker-run","day-2-anatomy-of-an-llm-inference-request-from-prompt-to-answer-step-by-step","day-2-your-images-are-a-supply-chain-and-it-s-probably-broken","day-3-stop-writing-dockerfiles-from-scratch","day-3-the-dgx-spark-unpacked-gb10-unified-memory-sm-121-and-the-one-reason-this-hardware-exists","day-4-breaking-isolation-on-purpose-volumes-networks-and-the-real-world","day-4-quantization-demystified-bf16-fp8-nvfp4-mxfp4-int4-gguf-and-why-it-all-matters","day-5-docker-compose-how-docker-actually-gets-used","day-5-local-llm-inference-engines-wrappers-and-what-to-pick","day-6-run-an-llm-on-your-laptop-with-docker","day-7-ship-it-and-what-comes-next","deploy-a-maven-project-on-a-tomcat-server-using-jenkins-and-aws","deploy-a-simple-server-using-aws-terraform","deploying-java-application-using-docker-and-kubernetes-devops-project","devin-outposts-on-kubernetes","ditch-the-overheating-laptop-supercharge-your-docker-workflow-with-docker-offload","diy-how-to-build-a-kubernetes-policy-engine","docker-captain-journey","docker-mcp-catalog","docker-networking-demystified","dynamic-mig-in-kubernetes-with-hami","embed-http-servers-in-wasm-with-rust-and-csharp","enhancing-runtime-security-with-falco-my-hands-on-experience","ephemeral-pull-request-environment-using-vcluster","essential-linux-commands-for-devops","event-driven-architecture-simplified-monolith-to-microservices","everything-you-need-to-know-about-docker-compose","everything-you-need-to-know-about-the-linux-ls-command","exploiting-metasploitable2-using-msfconsole-kali-linux-lab","firewall-a-networks-gatekeeper","four-pillars-of-observability-in-kubernetes","get-good-at-git","getting-started-with-kind-creating-a-multi-node-local-kubernetes-cluster","getting-started-with-ko-a-fast-container-image-builder-for-your-go-applications","getting-started-with-kyverno","git-and-github-a-beginners-guide","github-actions-101-what-are-github-actions-and-how-to-use-them-a-beginners-guide","gitops-demystified","ha-kubernetes","how-a-kubernetes-service-actually-works-and-all-5-types-you-need","how-get-started-with-hashicorp-vault","how-kubernetes-endpointslices-actually-work-and-why-endpoints-had-to-die","how-to-backup-kubernetes-with-kasten-community-edition","how-to-change-directory-in-shell-scripts","how-to-install-a-kubernetes-cluster-with-kubeadm-containerd-and-cilium-a-hands-on-guide","how-to-setup-your-ftp-server-in-linux","implementing-kubernetes-network-policies-a-comprehensive-guide","important-concepts-of-operating-systems","ing-switch-119-annotations-gateway-api-traefik-impact-ratings","ing-switch-migrate-from-ingress-nginx-to-traefik-or-gateway-api-in-minutes-not-days","installing-prometheus-with-selinux","introducing-kiac-kubernetes-in-apple-containers","introducing-unikraft-lightweight-virtualization-using-unikernels","introduction-of-jenkins-pipeline","introduction-to-cicd-and-cicd-pipeline","introduction-to-cri","introduction-to-developer-platforms-with-gimlet","introduction-to-helm","introduction-to-jenkins","introduction-to-kubernetes","introduction-to-terraform","iptables-demo","istio-service-mesh","k8sgpt-tutorial-when-kubernetes-meets-ai","keptn-getting-started","ksctl-making-kubernetes-easy-across-clouds","kube-proxy-deep-dive","kube-scheduler-deep-dive","kubecon-cloudnativecon-north-america-2024-recap-themes-innovations-and-community-spirit","kubecon-cloudnativecon-rejekts-and-wasm-io-wrap-up-a-leap-into-the-futu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export default { async fetch(request, env) { diff --git a/public/atom.xml b/public/atom.xml index 20687f4ca..63892a840 100644 --- a/public/atom.xml +++ b/public/atom.xml @@ -5,11 +5,24 @@ https://blog.kubesimplify.com/ - 2026-08-18T08:33:37.156Z + 2026-08-18T11:33:53.748Z Kubesimplify hello@kubesimplify.com + + Running a big LLM across multiple GPUs with vLLM + + https://blog.kubesimplify.com/running-a-big-llm-across-multiple-gpus-with-vllm + 2026-08-18T10:00:00.000Z + 2026-08-18T10:00:00.000Z + A plain-English guide to serving a model that is too big for one GPU: how tensor, pipeline, and expert parallelism split it up, what every vLLM flag does, and measured numbers from a 235B model on four RTX PRO 6000 cards. + + + + + + The Local LLM Glossary: Every Term, Flag, and Number in Plain English diff --git a/public/img/blog/running-a-big-llm-across-multiple-gpus-with-vllm/cover.png b/public/img/blog/running-a-big-llm-across-multiple-gpus-with-vllm/cover.png new file mode 100644 index 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Sooner or later everyone running models locally hits the same wall. You find a model you want, you look at the download size, and it is bigger than the GPU you own. A 235B model needs roughly 236 GB just for its weights. The card we have holds 96 GB, and even the largest data-centre GPUs available today top out well below 236 GB. So the model does not fit, and no amount of clever flags will make 236 GB squeeze into 96 GB. The answer is to use more than one GPU. That part everybody knows. The part that is genuinely confusing is what "use more than one GPU" actually means. Does each GPU get a copy of the model? Does the model get cut in half? Do the GPUs take turns? Which of those is happening, and what does it cost you? -Let's answer that properly, with a real model on real hardware, and let's explain every single flag and command along the way rather than pasting a magic incantation and moving on. +Let's answer that properly, with a real model on real hardware. -## What you will learn +## What this post covers -- How to download a 236 GB model, what the 24 files you get actually are, and how they sit on disk -- How to work out on paper whether it fits on your GPUs, before you spend an hour downloading it -- What inference really is: the two completely different phases behind "time to first token" and "tokens per second" -- The three different ways a model can be split across GPUs, in plain English, and when each is used -- What every flag in our vLLM command does, and why it has the value it has -- How to read the startup log, which tells you more than any tutorial can -- The rules that limit how far you can split, and the real errors you get when you break them -- Measured numbers for all three splitting modes on the same model and the same four GPUs +This is the runbook. Eight steps, from downloading a 236 GB model to serving it across four GPUs, with every command, flag, startup log line and real error explained. It is written for the person with root on the box, and it assumes no prior knowledge of distributed computing: if you know what a GPU is and you have run a model locally once, you are qualified. -No prior knowledge of distributed computing is assumed. If you know what a GPU is and you have run a model locally once, you are qualified. +It deliberately does not explain the machinery underneath. Why splitting a layer across GPUs makes prefill faster but costs you an all-reduce per layer, why that trade lands differently on decode, and why NVLink is the variable that decides the winner, are all part two, coming next. Where a "why" would otherwise interrupt the work, this post says so and moves on. + +New to the jargon? Every term, flag, and benchmark number here is explained in plain English in the [local LLM glossary](https://blog.kubesimplify.com/local-llm-glossary). ## The machine and the model -Here is what we tested on, because numbers mean nothing without the hardware attached. +Numbers mean nothing without the hardware attached, so here it is once. **The machine:** a server with 8x NVIDIA RTX PRO 6000 Blackwell Server Edition cards. Each card has 96 GB of memory, and the machine reports 95.01 GiB of that as usable. We borrowed 4 of the 8 cards for this work. One detail that matters more than it looks: these GPUs are **not** connected by NVLink. NVLink is NVIDIA's fast direct GPU-to-GPU cable. Without it, GPUs talk to each other over PCIe and through the CPU, which is slower. You can check what you have with one command: ```bash -nvidia-smi topo -m +root@utho-gpu-rtxpro6000-8-62383:~# nvidia-smi topo -m + +| Device | GPU0 | GPU1 | GPU2 | GPU3 | GPU4 | GPU5 | GPU6 | GPU7 | NIC0 | CPU Affinity | NUMA Affinity | GPU NUMA ID | +| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :--- | :---: | :---: | +| **GPU0** | **X** | SYS | SYS | SYS | SYS | SYS | SYS | SYS | SYS | 48-55,176-183 | 6 | N/A | +| **GPU1** | SYS | **X** | SYS | SYS | SYS | SYS | SYS | SYS | PHB | 32-39,160-167 | 4 | N/A | +| **GPU2** | SYS | SYS | **X** | SYS | SYS | SYS | SYS | SYS | SYS | 0-7,128-135 | 0 | N/A | +| **GPU3** | SYS | SYS | SYS | **X** | SYS | SYS | SYS | SYS | SYS | 16-23,144-151 | 2 | N/A | +| **GPU4** | SYS | SYS | SYS | SYS | **X** | SYS | SYS | SYS | SYS | 112-119,240-247 | 14 | N/A | +| **GPU5** | SYS | SYS | SYS | SYS | SYS | **X** | SYS | SYS | SYS | 96-103,224-231 | 12 | N/A | +| **GPU6** | SYS | SYS | SYS | SYS | SYS | SYS | **X** | SYS | SYS | 64-71,192-199 | 8 | N/A | +| **GPU7** | SYS | SYS | SYS | SYS | SYS | SYS | SYS | **X** | SYS | 80-87,208-215 | 10 | N/A | +| **NIC0** | SYS | PHB | SYS | SYS | SYS | SYS | SYS | SYS | **X** | | | | + +**Legend:** + +| Symbol | Description | +| :--- | :--- | +| **X** | Self | +| **SYS** | Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI) | +| **NODE** | Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node | +| **PHB** | Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU) | +| **PXB** | Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge) | +| **PIX** | Connection traversing at most a single PCIe bridge | +| **NV#** | Connection traversing a bonded set of `#` NVLinks | +| **NIC0** | `mlx4_0` | ``` On our machine every pair of GPUs reports `SYS`, which means the traffic goes across PCIe and then across the link between the CPU sockets. If you had NVLink you would see `NV1`, `NV2` and so on instead. Keep this in mind, because it changes which splitting method is fastest. @@ -47,24 +67,39 @@ On our machine every pair of GPUs reports `SYS`, which means the traffic goes ac **The model:** `Qwen/Qwen3-235B-A22B-Instruct-2507-FP8`. Let's unpack that name, because it is doing a lot of work: - **235B** is the total parameter count, 235 billion. -- **A22B** means 22 billion **active** parameters. This is a mixture-of-experts model, and only a fraction of it runs for any given token. More on this shortly, because it is the most interesting thing about serving big models today. +- **A22B** means 22 billion **active** parameters. This is a mixture-of-experts model: each layer holds 128 small expert networks and a router picks just 8 of them per token, so you pay for 235B in memory but only about 22B in arithmetic. - **FP8** is the number format the weights are stored in, 8 bits each, so one byte per parameter. **The software:** vLLM 0.27.1 running in the official container, with PyTorch 2.13.0 and CUDA 13.0, on driver 610.43.02. -## Part 1: Getting the model onto the machine +--- -Before anything can be split across GPUs it has to be on the disk, and with a model this size that step is not a formality. It is the step that bit us hardest, so let's do it properly. +## Step 1: Getting the model onto the machine -You download it with the Hugging Face CLI: +Before anything can be split across GPUs it has to be on disk, and with a model this size that is not a formality. It is the step that bit us hardest, so it goes first. + +### Check your disk first, because this is a real production hazard + +**On a shared machine, filling the disk can take down everything else on it.** This is the part we learned the hard way, and it is worth more than a footnote. Our test box also runs a Kubernetes inference platform. Kubernetes treats free disk as a managed resource called ephemeral-storage, and when free space fell below its eviction threshold, the kubelet did exactly what it is designed to do. + +It evicted pods to reclaim space, tainted the node so nothing new could schedule, and garbage-collected container images. Several of those images had been built locally and existed in no registry, so they could not simply be pulled again. + +Nothing about that is a Kubernetes bug, and nothing about it is specific to our setup. The lesson generalises: **before you download a quarter of a terabyte onto a machine, check what else lives on that disk and what will happen when it fills.** `df -h` before you start, and know your platform's eviction threshold, which is often far higher than "0 bytes free". If the machine is shared, keeping a couple of hundred gigabytes of headroom is not paranoia. + +### The download + +With the headroom confirmed, you download it with the Hugging Face CLI: ```bash -pip install huggingface_hub hf_transfer +root@utho-gpu-rtxpro6000-8-62383:~# pip install huggingface_hub hf_transfer +root@utho-gpu-rtxpro6000-8-62383:~# HF_XET_HIGH_PERFORMANCE=1 hf download Qwen/Qwen3-235B-A22B-Instruct-2507-FP8 +Downloading bytes: ████████████████████████████████████████████████▏ | 24.4GB, 234MB/s +Reconstructing (incomplete total...): 13%|███████████████▋ | 10.0GB / 80.0GB, 104MB/s +Fetching 34 files: 0%| | 0/34 [00:00https://blog.kubesimplify.com/running-a-big-llm-across-multiple-gpus-with-vllm https://blog.kubesimplify.com/running-a-big-llm-across-multiple-gpus-with-vllm Tue, 18 Aug 2026 10:00:00 GMT - A plain-English guide to serving a model that is too big for one GPU: how tensor, pipeline, and expert parallelism split it up, what every vLLM flag does, and measured numbers from a 235B model on four RTX PRO 6000 cards. + A runbook for serving a model too big for one GPU: download to serving in eight steps, with every vLLM flag, startup log line and real error explained, measured on a 235B model across four RTX PRO 6000 cards. vllmgpunvidiallmplatform-engineering From c793d7debd85601712568318a62f715b4fa36302 Mon Sep 17 00:00:00 2001 From: Saiyam Pathak Date: Mon, 31 Aug 2026 11:30:07 +0530 Subject: [PATCH 10/19] Restore the evidence and the why, and drop the part-two promises Shubham's eight-step runbook is the right shape and stays as the spine. But the cut removed both benchmark tables while keeping the verdict that rests on them, so three headline percentages had nothing behind them, and it deferred the post's central question to a part two we have not scoped. This makes the post stand alone. Restored, placed where the reader is making a decision rather than front-loaded as theory: - Step 3 gains the restaurant-kitchen analogy, what a tensor-parallel split costs (2 all-reduces per layer, 94 layers, 188 per token), and why a 235B MoE is only 22B of arithmetic. The three animations that were left orphaned by the cut now have homes here. - Step 7 gains the prefill-versus-decode split, which is the only thing that explains why pipeline parallelism wins first-token latency while tensor parallelism wins throughput, then both measurement tables, the 28.71 tok/s outlier we discarded and why, and the NVLink caveat. The TP throughput figure was re-measured at --num-prompts 640 while PP and EP were measured at 128, so the speed table now says so. TP came out at 507.09 against 503.68 at the smaller scale, a 0.68% difference, so the comparison holds at both. Fixes: the startup-log section told readers to grep for "Available KV cache memory", which vLLM 0.27.1 does not print and which appears nowhere in our own pasted log. Model loading time said 45 seconds in prose and 48.5 in the log 120 lines below. "Those 10 GB boundaries" referred to a figure removed with the shard section. Two flag-table rows promised tests that had been deleted. Co-Authored-By: Claude Opus 5 (1M context) --- ...-big-llm-across-multiple-gpus-with-vllm.md | 141 +++++++++++++++--- lib/_blog-feed-data.js | 2 +- public/atom.xml | 4 +- public/llms-full.txt | 141 +++++++++++++++--- public/llms.txt | 2 +- public/rss.xml | 2 +- 6 files changed, 249 insertions(+), 43 deletions(-) diff --git a/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md b/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md index bbe3dd129..78038417d 100644 --- a/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md +++ b/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md @@ -1,7 +1,7 @@ --- title: "Running a big LLM across multiple GPUs with vLLM" seoTitle: "Running a big LLM across multiple GPUs with vLLM" -seoDescription: "A runbook for serving a model too big for one GPU: download to serving in eight steps, with every vLLM flag, startup log line and real error explained, measured on a 235B model across four RTX PRO 6000 cards." +seoDescription: "A runbook for serving a model too big for one GPU: download to serving in eight steps, with every vLLM flag, startup log line and real error explained, plus tensor, pipeline and expert parallelism benchmarked head to head on a 235B model across four RTX PRO 6000 cards." datePublished: 2026-08-18T10:00:00.000Z slug: running-a-big-llm-across-multiple-gpus-with-vllm author: shubham-katara @@ -20,7 +20,7 @@ Let's answer that properly, with a real model on real hardware. This is the runbook. Eight steps, from downloading a 236 GB model to serving it across four GPUs, with every command, flag, startup log line and real error explained. It is written for the person with root on the box, and it assumes no prior knowledge of distributed computing: if you know what a GPU is and you have run a model locally once, you are qualified. -It deliberately does not explain the machinery underneath. Why splitting a layer across GPUs makes prefill faster but costs you an all-reduce per layer, why that trade lands differently on decode, and why NVLink is the variable that decides the winner, are all part two, coming next. Where a "why" would otherwise interrupt the work, this post says so and moves on. +The theory arrives where you need it to make a decision, not before. Step 3 explains what a tensor-parallel split actually costs, because that is where you pick one, and Step 7 explains why the three options trade against each other, because that is where you read the numbers. Nothing here is theory for its own sake. New to the jargon? Every term, flag, and benchmark number here is explained in plain English in the [local LLM glossary](https://blog.kubesimplify.com/local-llm-glossary). @@ -134,7 +134,7 @@ One more detail, because a crash in Step 8 depends on it. Because this model is Remember those block scales. They are the reason for the most annoying crash we hit, down in Step 8. -Do not spend any time on the shard count itself. How the weights are packaged changes nothing about the numbers inside them, so shard size is a distribution question, not an inference question. Part two covers where those 10 GB boundaries come from and what actually sits inside each file, which is not "layers 1 to 4". +Do not spend any time on the shard count itself. Ours uses a 10 GB cap, 23 shards of exactly 10.00 GB and a 24th holding the remaining 6.45 GB, and somewhere in the 5 to 10 GB range is the common choice across the Hub. The layout is fixed by whoever uploaded the model, there is no download flag to change it, and it makes no difference to serving: the weights are identical either way, and safetensors are memory-mapped so the loader reads the byte ranges it wants regardless of how they are grouped. Shard size is a distribution question, not an inference question. ### Where it gets stored @@ -156,7 +156,7 @@ One more thing about loading that surprises people. When you split the model ove vLLM's own docs say it plainly: with tensor parallelism, "each process will read the whole model and split it into chunks". So at `-tp 4` the machine reads the 236 GB not once but four times, close to a full terabyte of disk reads before the server can answer anything. That is why a big model takes minutes to load even from a fast disk. -Our first `Model loading took` line said 45 seconds, but only because we had just downloaded the model, so most of it was still sitting in RAM, where the operating system keeps recently used files. From a cold disk it takes much longer. +Our own `Model loading took` line, which you can see in Step 5, reported 48.5 seconds, and that was a flattering number: we had just downloaded the model, so most of it was still sitting in RAM where the operating system keeps recently used files. From a cold disk it takes much longer. ## Step 2: Will it fit? The ten-minute check @@ -193,11 +193,19 @@ One piece of good news: under tensor parallelism the KV cache is **divided** acr ## Step 3: Pick your split, then check it divides -vLLM gives you three ways to spread a model over GPUs, and they are genuinely different things. The one-minute version, so you can pick a flag and move on: +vLLM gives you three ways to spread a model over GPUs, and they are genuinely different things. Mixing them up is the source of most confusion, so here is the analogy first. Imagine a restaurant kitchen that has to produce one dish: + +- **Tensor parallelism** is four chefs all working on the same dish at once, one chopping, one on sauce, one on protein, one plating. They constantly have to coordinate, but the dish is done fast. +- **Pipeline parallelism** is four chefs at four stations with the dish moving down the line. Station two cannot start until station one finishes. Very little talking, but three chefs are idle at any moment unless several dishes are in flight. +- **Expert parallelism** is a kitchen of 128 specialists where each dish needs only 8 of them. You spread the 128 across four rooms and walk each dish to whichever rooms hold the specialists it needs. + +In flags, and with the trade-off each one buys you: - **Tensor parallelism** (`--tensor-parallel-size`) slices every layer across all GPUs, so they all work on the same token at once. Best tokens per second, evenly split memory, divided KV cache. The default choice for GPUs inside one machine. This is what we run. - **Pipeline parallelism** (`--pipeline-parallel-size`) gives each GPU a block of consecutive layers and passes the work along. The GPUs barely need to talk to each other, so it is the tool for spanning machines with a slow network, and it wins on time to first token, but GPUs spend time waiting their turn. -- **Expert parallelism** (`--enable-expert-parallel`) exists only for mixture-of-experts models and hands out whole experts instead of slicing them. Its job is trillion-parameter-scale clusters where even a tensor-parallel split cannot hold all the experts. It is **not** a memory saver at our scale, as our measurements below show. +- **Expert parallelism** (`--enable-expert-parallel`) exists only for mixture-of-experts models and hands out whole experts instead of slicing them. Its job is trillion-parameter-scale clusters where even a tensor-parallel split cannot hold all the experts. It is **not** a memory saver at our scale, as our measurements in Step 7 show. + +{{multi-gpu-split-modes-animation}} The first two are easiest to hold in your head as two ways of cutting a layer cake: @@ -207,6 +215,32 @@ That is the whole distinction. Pipeline parallelism cuts across the layers and e The drawing shows three cards because it is illustrating the two shapes, not our setup. Card counts are not free choices, which is exactly what the next check is about. +### What the tensor-parallel split costs: 188 all-reduces per token + +Tensor parallelism cuts **inside** every layer, and it is worth knowing what that costs before you commit to it, because it explains every result in Step 7. + +The work a layer does is mostly one big multiplication table, and multiplication tables can be cut up. The technique comes from NVIDIA's 2019 Megatron-LM paper. Cut the first matrix into vertical strips and each GPU can finish its columns alone. Cut the second into horizontal strips lining up with the first, and each GPU produces a **partial answer**, a quarter of the real result. + +Now, and only now, the GPUs have to talk. They add their four partial answers together so everyone ends up with the complete result. That operation is an **all-reduce**: everyone contributes a piece, everyone gets the total back. Megatron puts the cost plainly, saying the design runs a transformer layer "using only two all-reduces in the forward path". Generating text only uses the forward path, so: + +- 2 all-reduces per layer +- 94 layers +- **188 all-reduces to produce one single token** + +They happen strictly one after another, because layer 5 cannot start until layer 4 has compared notes. On our machine those 188 round trips cross PCIe rather than NVLink, which is the single fact that shapes every number in Step 7. + +{{multi-gpu-tensor-split-animation}} + +### Why this model is only 22B of work + +The third option needs one idea first, because it is also why this model runs far faster than 235B suggests. + +In an ordinary model every parameter is used for every token. In a **mixture-of-experts** model each layer holds many small networks called experts, and a tiny **router** picks which few each token visits. Ours has 128 experts per layer and the router picks 8. So the model holds 235B parameters in memory but only about 22B do any arithmetic for a given token. That is what "235B-A22B" means: you pay for the full 235B in memory and for only 22B in speed. + +{{moe-expert-routing-animation}} + +That is what gives you the third way to split. Instead of slicing every expert into strips, hand out whole experts: 128 experts over 4 GPUs is 32 intact experts each. Nothing needs adding up at the end, but tokens have to travel to whichever GPU owns the expert they were routed to, and the answers travel back. It also has a fairness problem, because the router does not promise to spread work evenly, so one GPU can end up holding the popular experts and everybody waits on it. + **Then the ten-second pre-flight check.** You cannot pick any number for `--tensor-parallel-size`: because attention heads are handed out whole, your TP size must divide the model's head counts. Open `config.json`: ```json @@ -236,8 +270,8 @@ Before the command, the vocabulary. Here is every flag we use and why it has the | Flag | What it does | Why our value | | ----------------------------------- | --------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------ | | `--tensor-parallel-size 4` | How many GPUs to slice each layer across. Often shortened to `-tp`. | 236 GB of weights needs at least 3 cards of 95 GiB, and 4 divides the model's head counts cleanly. | -| `--pipeline-parallel-size 1` | How many groups to cut the layer stack into. Often `-pp`. | 1 means off. We test a version with 4 later. | -| `--enable-expert-parallel` | Hand out whole experts per GPU instead of slicing every expert. Mixture-of-experts models only. | Tested both ways, since this is exactly the choice a big MoE forces on you. | +| `--pipeline-parallel-size 1` | How many groups to cut the layer stack into. Often `-pp`. | 1 means off. We measure a `-pp 4` build in Step 7. | +| `--enable-expert-parallel` | Hand out whole experts per GPU instead of slicing every expert. Mixture-of-experts models only. | Tested both ways in Step 7, the choice a big MoE forces on you. | | `--gpu-memory-utilization 0.90` | The fraction of each GPU's memory vLLM is allowed to claim, for weights plus KV cache plus working space. | 0.90 leaves a little headroom. Push it to 0.95 for more cache, but leave room or startup fails. | | `--max-model-len 32768` | The longest single request, prompt plus reply, in tokens. | The model supports 262,144, but that would eat 47 GiB of cache for one user. 32,768 is a sane serving value. | | `--max-num-seqs 32` | How many requests may be in flight at once. | Caps how much KV cache can be demanded simultaneously. Lower it if you see requests being preempted. | @@ -326,13 +360,13 @@ The startup log is the best teaching tool in the whole stack, and almost nobody If you divide the full model size by your `--tensor-parallel-size` and get roughly this number, the split worked. If this number equals the **whole** model, something is wrong and you are not actually splitting. -**Line two, what is left for conversations:** +**Line two, what is left for conversations.** On vLLM 0.27.1 this arrives inside the long `gpu_worker.py` line you can see in Step 5, phrased as: ``` -Available KV cache memory: X GiB +Current kv cache memory in use is X GiB ``` -If this is **negative**, your weights plus overhead already exceeded the budget, and vLLM will refuse to start. That is the clearest possible signal that you need more GPUs, a smaller number format, or a lower `--max-model-len`. +Older versions print a dedicated `Available KV cache memory: X GiB` line instead, so search for both. If the figure is **negative**, your weights plus overhead already exceeded the budget and vLLM will refuse to start. That is the clearest possible signal that you need more GPUs, a smaller number format, or a lower `--max-model-len`. **Line three, the cache in tokens:** @@ -350,13 +384,30 @@ Maximum concurrency for 32,768 tokens per request: N.NNx This is the one to show your capacity planner. If it says `2.05x`, then two users can each have a full-length 32k conversation, and a third will have to wait or be preempted. It is simply the previous line divided by `--max-model-len`. -For our run the four lines came out as: `Model loading took 55.19 GiB` per worker, `Available KV cache memory: 27.85 GiB`, `GPU KV cache size: 621,392 tokens`, and a maximum concurrency of `18.96x` at 32k. Predicting that token count by hand, 27.85 GiB divided by 47 KiB per token per card, gives 621,337 against the 621,392 vLLM printed, which is the kind of agreement that tells you the mental model is right. +For our run the four figures came out as: `Model loading took 55.19 GiB` per worker, 27.85 GiB of kv cache in use, `GPU KV cache size: 621,392 tokens`, and a maximum concurrency of `18.96x` at 32k. Predicting that token count by hand, 27.85 GiB divided by 47 KiB per token per card, gives 621,337 against the 621,392 vLLM printed, which is the kind of agreement that tells you the mental model is right. ## Step 7: Benchmark it, and what we would run -Once it was running, we compared all three ways of splitting the same model over the same 4 GPUs: tensor parallelism on its own, tensor parallelism plus expert parallelism, and pure pipeline parallelism. Same hardware, same flags otherwise, same benchmark. +Once it was running, we compared all three ways of splitting the same model over the same 4 GPUs: tensor parallelism on its own, tensor parallelism plus expert parallelism, and pure pipeline parallelism. Same hardware, same flags otherwise, same benchmark shape. + +Two numbers do most of the talking. **Time to first token** is how long the user waits before anything appears, and **output tokens per second** is how fast the answer then streams. Every configuration trades one against the other, and one idea explains why. + +### The two jobs hiding inside inference + +Inference is really two different jobs wearing one coat, and almost everything confusing about multi-GPU performance comes from this split. + +**Prefill reads your prompt.** All 1,024 tokens go through every layer at once, as one big batch. This is the phase that decides time to first token. It is *compute-heavy*: there is a lot of arithmetic and the matrix engines are the bottleneck. It also writes keys and values for all 1,024 tokens into the KV cache. + +**Decode writes the answer**, and it can only produce one token at a time. To write token 2 the model needs token 1, because it feeds its own output back in. That is what "autoregressive" means, and there is no way around it. Each pass produces exactly one token, reads the whole KV cache built so far, and appends one entry. Decode is *memory-heavy* rather than compute-heavy: for a single token there is barely any arithmetic, but the GPU still has to stream the weights and the entire cache past its compute units. + +| | Prefill | Decode | +| --- | --- | --- | +| Work per step | your whole prompt at once | exactly one token | +| Bottleneck | compute | memory bandwidth | +| Metric it drives | time to first token | time per output token | +| Data moved between GPUs | large, whole prompt's worth | tiny, one token's worth | -Two numbers do most of the talking. **Time to first token** is how long the user waits before anything appears, and **output tokens per second** is how fast the answer then streams. Every configuration trades one against the other; part two is about why. +That last row is the one to hold on to, and it is the whole explanation for the table below. Those 188 all-reduces from Step 3 are trivially cheap during decode, because each one carries a single token's worth of data. During prefill the same 188 all-reduces carry a thousand times more. So tensor parallelism pays its communication bill mostly at first-token time, and pipeline parallelism, which just hands one activation tensor to the next stage, skips it. That is the trade, and you are about to watch it happen. The benchmark is vLLM's own, 1024 tokens in and 256 tokens out per request, with `--ignore-eos` so every request generates exactly 256 tokens and the comparison is fair: @@ -451,11 +502,63 @@ One subtlety: 32 is not the only ceiling in play. The startup log said this conf One benchmarking warning before you copy this: if you re-run against a warm server, either vary the `--seed` or turn prefix caching off. We forgot, and time to first token "improved" from 265 ms to 61 ms purely because we had just sent the server those same prompts with the same seed. -**The verdict.** Tensor parallelism won nearly everything: 507.09 output tokens/sec at 32 concurrent requests, which is 70% faster than pipeline parallelism; the fastest single-request decode at 17.14 ms median per token; the largest conversation capacity at 621,392 cached tokens; and perfectly even memory across all four cards. Pipeline parallelism won exactly one metric, time to first token, by 15%. Expert parallelism cost 7% and returned nothing at this scale. +### The memory side + +We ran the same pair of benchmarks against all three configurations. First, where the memory went: + +| | TP=4 | TP=4 plus EP | PP=4 | +| --- | --- | --- | --- | +| Weights per GPU | 55.19 GiB | 55.19 GiB | 55.70 GiB | +| KV cache per GPU | 27.85 GiB | 27.96 GiB | 26.84 GiB | +| Total KV cache | 621,392 tokens | **623,696 tokens** | 555,680 tokens | +| Max concurrency at 32k | 18.96x | **19.03x** | 16.96x | +| GPU memory used | 88,211 MiB on all 4 | 88,209 MiB on all 4 | 84,283 / 87,899 / 87,899 / 84,507 | + +**Expert parallelism did not save memory.** It moved 0.37% of extra room into the cache, which is noise. If you were hoping expert parallelism would let you fit a model that otherwise does not fit, this is your warning that it will not. + +**Pipeline parallelism cost 10.6% of the cache**, dropping from 621,392 tokens to 555,680, because a pipeline needs extra buffers for the activations travelling between stages and that comes straight out of your conversation capacity. + +Look at the last row. Under tensor parallelism all four cards sat at **exactly 88,211 MiB**, the same number on every one. Under pipeline parallelism they ranged from 84,283 to 87,899 MiB, about 3.5 GiB apart, because a layer split cannot be perfectly even when 94 layers go over 4 GPUs and the ends of the model are not symmetric: the first stage carries the token embedding and the last carries the output head. That evenness check is the quickest sanity test you have that a tensor-parallel split is behaving. + +### The speed side + +| Measurement | TP=4 | TP=4 plus EP | PP=4 | Winner | +| --- | --- | --- | --- | --- | +| Median time per token, 1 request | **17.14 ms** | 18.83 ms | 21.19 ms | TP | +| Output tokens/sec, 32 requests | **507.09** | 470.93 | 296.48 | TP, by 70% over PP | +| Median time to first token, 32 requests | 3,211 ms | 3,705 ms | **2,735 ms** | PP, by 15% | + +Every figure in that table is a rate or a latency rather than a total, which matters because the runs were not all the same size. We measured all three at `--num-prompts 128` first, then re-ran the winner at `--num-prompts 640` to be sure the result held up. It did: TP produced 507.09 tokens/sec at the larger scale against 503.68 at the smaller, and a median first-token latency of 3,211 ms against 3,233 ms. Both inside 1%, so the numbers above are the 640-prompt figures for TP and the 128-prompt figures for the other two, and the comparison is sound. The full output pasted above is from the 640-prompt run. + +Tensor parallelism won nearly everything, and one gap deserves attention. At 32 concurrent requests it produced **70% more tokens per second than pipeline parallelism**. That is not a rounding error, it is a different class of performance, and it lines up exactly with the prefill and decode split above. Tensor parallelism has all four GPUs working on every token. Pipeline parallelism has each GPU working on a different request's stage, and with 32 requests spread over 4 stages there is not enough in flight to keep everyone busy, so cards sit idle waiting their turn. Its median time per token was 24% worse for the same reason. + +**Pipeline parallelism did win one thing, and it is the one the theory predicts:** time to first token, by 15%. Processing a 1,024-token prompt is exactly where tensor parallelism's 188 all-reduces get expensive, because each one carries the whole prompt's worth of data rather than a single token's. Pipeline parallelism hands one activation tensor to the next stage and skips all of it. If your users judge you on how fast the first word appears, that is a real and measurable advantage. + +Expert parallelism costing 7% is not a knock on the technique, and it is important not to over-read it. Expert parallelism exists to solve a problem we do not have: models so large that even a tensor-parallel split cannot hold all the experts, on clusters big enough that duplicating experts everywhere would be wasteful. With 4 GPUs and a model that already fits, we are asking it to do a job it was not designed for and paying an extra network hop per token for nothing. On a 32 or 64 GPU deployment of a trillion-parameter model the answer would very likely flip. + +### One number we threw away, and why + +Being straight about this, because it is a good lesson in reading your own benchmarks. The very first expert-parallel run at one request at a time reported **28.71 output tokens per second**, which would have made expert parallelism look catastrophic. It was not real. Look at the two TTFT figures from that run: + +``` +Mean TTFT (ms): 3987.38 +Median TTFT (ms): 265.56 +``` + +A mean fifteen times the median means one request behaved completely differently from the other eleven. One request stalled for about 45 seconds, almost certainly a one-off kernel compilation on the first pass through a code path, and that single stall stretched the whole benchmark from 63 seconds to 107 seconds. Since throughput is just tokens divided by wall-clock, one stall wrecked the headline number. + +That is why the speed table uses **median time per token** as its decode measurement rather than aggregate throughput. Median per-token latency does not care that one request had a bad start. + +### What we would actually run + +For a 235B MoE on 4 GPUs with no NVLink between them, we would use plain `--tensor-parallel-size 4` and leave both of the others off. It was faster nearly everywhere, it gives the most conversation capacity, it splits memory perfectly evenly, and it is one less thing to reason about. + +We would reach for the other two in specific situations, not as general upgrades: -So for a 235B MoE on 4 GPUs with no NVLink between them, we would use plain `--tensor-parallel-size 4` and leave both of the others off. +- **Pipeline parallelism** if time to first token is the metric you are judged on, or if you are spanning multiple machines where the network between them is genuinely slow. It was 15% better at first-token latency and it barely touches the interconnect. +- **Expert parallelism** when the model is so large that even a tensor-parallel split cannot hold all the experts. That is a real problem at trillion-parameter scale and simply is not our problem at 235B on 4 cards. -When the other two are the right call, why the results fall out this way, and why NVLink is the one component that would reshuffle them, is part two. +One more thing worth saying plainly, because it is the biggest caveat on every number above: **our GPUs have no NVLink.** Every one of those 188 all-reduces per token crosses PCIe and the link between CPU sockets. On a machine with NVLink the all-reduce gets dramatically cheaper, tensor parallelism's one weakness at first-token latency shrinks, and pipeline parallelism's single win would likely disappear. If you are reading this table to plan hardware, the interconnect is the variable to check first. ## Step 8: Errors you will actually hit @@ -547,13 +650,13 @@ Four things to carry out of this, all of them checks you can run in a minute. **Three.** Work out the memory on paper first. Parameters times bytes-per-parameter gives you the weights, and then remember the weights are only one of three things that must fit, alongside the conversation cache and the working space. A model whose weights just barely fit is a model that cannot serve anybody. -**Four.** Read the startup log. `Model loading took`, `Available KV cache memory`, `GPU KV cache size` and `Maximum concurrency` tell you, in four lines, whether your setup is sane and how many users it can actually hold. A negative cache number is the clearest error message in the whole stack. +**Four.** Read the startup log. `Model loading took`, the kv cache memory figure, `GPU KV cache size` and `Maximum concurrency` tell you, in four lines, whether your setup is sane and how many users it can actually hold. A negative cache number is the clearest error message in the whole stack. And for a 235B mixture-of-experts model on four GPUs with no NVLink between them, the answer is plain `--tensor-parallel-size 4`. It was faster nearly everywhere, it gives the most conversation capacity, and it splits memory perfectly evenly. Try it on whatever you have. Two GPUs are enough to see every one of these steps in action, and the log lines mean the same thing whether you are running 4 GPUs or 40. If you hit something we did not cover, tell us and we will add it. -**Part two is the why.** How slicing a layer across GPUs divides the prefill arithmetic between them, why that costs you an all-reduce at every layer, why the bill lands hard on time to first token but barely registers during decode, and why NVLink is the single variable that decides whether tensor parallelism wins. Coming next. +**And check your interconnect before you buy anything.** Every number in Step 7 was measured on GPUs with no NVLink between them, so all 188 all-reduces per token crossed PCIe. That single fact is why pipeline parallelism managed to win first-token latency at all. On a machine with NVLink we would expect that win to vanish. `nvidia-smi topo -m` tells you which world you are in, and it is the first command we run on any new box. ## Credits and references diff --git a/lib/_blog-feed-data.js b/lib/_blog-feed-data.js index 5166b9fa7..bba41d0e4 100644 --- a/lib/_blog-feed-data.js +++ b/lib/_blog-feed-data.js @@ -3,7 +3,7 @@ export const FEED_POSTS = [ { "slug": "running-a-big-llm-across-multiple-gpus-with-vllm", "title": "Running a big LLM across multiple GPUs with vLLM", - "description": "A runbook for serving a model too big for one GPU: download to serving in eight steps, with every vLLM flag, startup log line and real error explained, measured on a 235B model across four RTX PRO 6000 cards.", + "description": "A runbook for serving a model too big for one GPU: download to serving in eight steps, with every vLLM flag, startup log line and real error explained, plus tensor, pipeline and expert parallelism benchmarked head to head on a 235B model across four RTX PRO 6000 cards.", "datePublished": "2026-08-18T10:00:00.000Z", "cover": "/img/blog/running-a-big-llm-across-multiple-gpus-with-vllm/cover.png", "tags": [ diff --git a/public/atom.xml b/public/atom.xml index 9308f4f69..772067f5f 100644 --- a/public/atom.xml +++ b/public/atom.xml @@ -5,7 +5,7 @@ https://blog.kubesimplify.com/ - 2026-08-28T19:13:14.583Z + 2026-08-31T06:02:03.648Z Kubesimplify hello@kubesimplify.com @@ -16,7 +16,7 @@ https://blog.kubesimplify.com/running-a-big-llm-across-multiple-gpus-with-vllm 2026-08-18T10:00:00.000Z 2026-08-18T10:00:00.000Z -

    A runbook for serving a model too big for one GPU: download to serving in eight steps, with every vLLM flag, startup log line and real error explained, measured on a 235B model across four RTX PRO 6000 cards. + A runbook for serving a model too big for one GPU: download to serving in eight steps, with every vLLM flag, startup log line and real error explained, plus tensor, pipeline and expert parallelism benchmarked head to head on a 235B model across four RTX PRO 6000 cards. diff --git a/public/llms-full.txt b/public/llms-full.txt index 871a6c80c..e86c73bcf 100644 --- a/public/llms-full.txt +++ b/public/llms-full.txt @@ -9,7 +9,7 @@ - Canonical: https://blog.kubesimplify.com/running-a-big-llm-across-multiple-gpus-with-vllm - Published: 2026-08-18 -- Summary: A runbook for serving a model too big for one GPU: download to serving in eight steps, with every vLLM flag, startup log line and real error explained, measured on a 235B model across four RTX PRO 6000 cards. +- Summary: A runbook for serving a model too big for one GPU: download to serving in eight steps, with every vLLM flag, startup log line and real error explained, plus tensor, pipeline and expert parallelism benchmarked head to head on a 235B model across four RTX PRO 6000 cards. Sooner or later everyone running models locally hits the same wall. You find a model you want, you look at the download size, and it is bigger than the GPU you own. A 235B model needs roughly 236 GB just for its weights. The card we have holds 96 GB, and even the largest data-centre GPUs available today top out well below 236 GB. So the model does not fit, and no amount of clever flags will make 236 GB squeeze into 96 GB. @@ -21,7 +21,7 @@ Let's answer that properly, with a real model on real hardware. This is the runbook. Eight steps, from downloading a 236 GB model to serving it across four GPUs, with every command, flag, startup log line and real error explained. It is written for the person with root on the box, and it assumes no prior knowledge of distributed computing: if you know what a GPU is and you have run a model locally once, you are qualified. -It deliberately does not explain the machinery underneath. Why splitting a layer across GPUs makes prefill faster but costs you an all-reduce per layer, why that trade lands differently on decode, and why NVLink is the variable that decides the winner, are all part two, coming next. Where a "why" would otherwise interrupt the work, this post says so and moves on. +The theory arrives where you need it to make a decision, not before. Step 3 explains what a tensor-parallel split actually costs, because that is where you pick one, and Step 7 explains why the three options trade against each other, because that is where you read the numbers. Nothing here is theory for its own sake. New to the jargon? Every term, flag, and benchmark number here is explained in plain English in the [local LLM glossary](https://blog.kubesimplify.com/local-llm-glossary). @@ -135,7 +135,7 @@ One more detail, because a crash in Step 8 depends on it. Because this model is Remember those block scales. They are the reason for the most annoying crash we hit, down in Step 8. -Do not spend any time on the shard count itself. How the weights are packaged changes nothing about the numbers inside them, so shard size is a distribution question, not an inference question. Part two covers where those 10 GB boundaries come from and what actually sits inside each file, which is not "layers 1 to 4". +Do not spend any time on the shard count itself. Ours uses a 10 GB cap, 23 shards of exactly 10.00 GB and a 24th holding the remaining 6.45 GB, and somewhere in the 5 to 10 GB range is the common choice across the Hub. The layout is fixed by whoever uploaded the model, there is no download flag to change it, and it makes no difference to serving: the weights are identical either way, and safetensors are memory-mapped so the loader reads the byte ranges it wants regardless of how they are grouped. Shard size is a distribution question, not an inference question. ### Where it gets stored @@ -157,7 +157,7 @@ One more thing about loading that surprises people. When you split the model ove vLLM's own docs say it plainly: with tensor parallelism, "each process will read the whole model and split it into chunks". So at `-tp 4` the machine reads the 236 GB not once but four times, close to a full terabyte of disk reads before the server can answer anything. That is why a big model takes minutes to load even from a fast disk. -Our first `Model loading took` line said 45 seconds, but only because we had just downloaded the model, so most of it was still sitting in RAM, where the operating system keeps recently used files. From a cold disk it takes much longer. +Our own `Model loading took` line, which you can see in Step 5, reported 48.5 seconds, and that was a flattering number: we had just downloaded the model, so most of it was still sitting in RAM where the operating system keeps recently used files. From a cold disk it takes much longer. ## Step 2: Will it fit? The ten-minute check @@ -194,11 +194,19 @@ One piece of good news: under tensor parallelism the KV cache is **divided** acr ## Step 3: Pick your split, then check it divides -vLLM gives you three ways to spread a model over GPUs, and they are genuinely different things. The one-minute version, so you can pick a flag and move on: +vLLM gives you three ways to spread a model over GPUs, and they are genuinely different things. Mixing them up is the source of most confusion, so here is the analogy first. Imagine a restaurant kitchen that has to produce one dish: + +- **Tensor parallelism** is four chefs all working on the same dish at once, one chopping, one on sauce, one on protein, one plating. They constantly have to coordinate, but the dish is done fast. +- **Pipeline parallelism** is four chefs at four stations with the dish moving down the line. Station two cannot start until station one finishes. Very little talking, but three chefs are idle at any moment unless several dishes are in flight. +- **Expert parallelism** is a kitchen of 128 specialists where each dish needs only 8 of them. You spread the 128 across four rooms and walk each dish to whichever rooms hold the specialists it needs. + +In flags, and with the trade-off each one buys you: - **Tensor parallelism** (`--tensor-parallel-size`) slices every layer across all GPUs, so they all work on the same token at once. Best tokens per second, evenly split memory, divided KV cache. The default choice for GPUs inside one machine. This is what we run. - **Pipeline parallelism** (`--pipeline-parallel-size`) gives each GPU a block of consecutive layers and passes the work along. The GPUs barely need to talk to each other, so it is the tool for spanning machines with a slow network, and it wins on time to first token, but GPUs spend time waiting their turn. -- **Expert parallelism** (`--enable-expert-parallel`) exists only for mixture-of-experts models and hands out whole experts instead of slicing them. Its job is trillion-parameter-scale clusters where even a tensor-parallel split cannot hold all the experts. It is **not** a memory saver at our scale, as our measurements below show. +- **Expert parallelism** (`--enable-expert-parallel`) exists only for mixture-of-experts models and hands out whole experts instead of slicing them. Its job is trillion-parameter-scale clusters where even a tensor-parallel split cannot hold all the experts. It is **not** a memory saver at our scale, as our measurements in Step 7 show. + +{{multi-gpu-split-modes-animation}} The first two are easiest to hold in your head as two ways of cutting a layer cake: @@ -208,6 +216,32 @@ That is the whole distinction. Pipeline parallelism cuts across the layers and e The drawing shows three cards because it is illustrating the two shapes, not our setup. Card counts are not free choices, which is exactly what the next check is about. +### What the tensor-parallel split costs: 188 all-reduces per token + +Tensor parallelism cuts **inside** every layer, and it is worth knowing what that costs before you commit to it, because it explains every result in Step 7. + +The work a layer does is mostly one big multiplication table, and multiplication tables can be cut up. The technique comes from NVIDIA's 2019 Megatron-LM paper. Cut the first matrix into vertical strips and each GPU can finish its columns alone. Cut the second into horizontal strips lining up with the first, and each GPU produces a **partial answer**, a quarter of the real result. + +Now, and only now, the GPUs have to talk. They add their four partial answers together so everyone ends up with the complete result. That operation is an **all-reduce**: everyone contributes a piece, everyone gets the total back. Megatron puts the cost plainly, saying the design runs a transformer layer "using only two all-reduces in the forward path". Generating text only uses the forward path, so: + +- 2 all-reduces per layer +- 94 layers +- **188 all-reduces to produce one single token** + +They happen strictly one after another, because layer 5 cannot start until layer 4 has compared notes. On our machine those 188 round trips cross PCIe rather than NVLink, which is the single fact that shapes every number in Step 7. + +{{multi-gpu-tensor-split-animation}} + +### Why this model is only 22B of work + +The third option needs one idea first, because it is also why this model runs far faster than 235B suggests. + +In an ordinary model every parameter is used for every token. In a **mixture-of-experts** model each layer holds many small networks called experts, and a tiny **router** picks which few each token visits. Ours has 128 experts per layer and the router picks 8. So the model holds 235B parameters in memory but only about 22B do any arithmetic for a given token. That is what "235B-A22B" means: you pay for the full 235B in memory and for only 22B in speed. + +{{moe-expert-routing-animation}} + +That is what gives you the third way to split. Instead of slicing every expert into strips, hand out whole experts: 128 experts over 4 GPUs is 32 intact experts each. Nothing needs adding up at the end, but tokens have to travel to whichever GPU owns the expert they were routed to, and the answers travel back. It also has a fairness problem, because the router does not promise to spread work evenly, so one GPU can end up holding the popular experts and everybody waits on it. + **Then the ten-second pre-flight check.** You cannot pick any number for `--tensor-parallel-size`: because attention heads are handed out whole, your TP size must divide the model's head counts. Open `config.json`: ```json @@ -237,8 +271,8 @@ Before the command, the vocabulary. Here is every flag we use and why it has the | Flag | What it does | Why our value | | ----------------------------------- | --------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------ | | `--tensor-parallel-size 4` | How many GPUs to slice each layer across. Often shortened to `-tp`. | 236 GB of weights needs at least 3 cards of 95 GiB, and 4 divides the model's head counts cleanly. | -| `--pipeline-parallel-size 1` | How many groups to cut the layer stack into. Often `-pp`. | 1 means off. We test a version with 4 later. | -| `--enable-expert-parallel` | Hand out whole experts per GPU instead of slicing every expert. Mixture-of-experts models only. | Tested both ways, since this is exactly the choice a big MoE forces on you. | +| `--pipeline-parallel-size 1` | How many groups to cut the layer stack into. Often `-pp`. | 1 means off. We measure a `-pp 4` build in Step 7. | +| `--enable-expert-parallel` | Hand out whole experts per GPU instead of slicing every expert. Mixture-of-experts models only. | Tested both ways in Step 7, the choice a big MoE forces on you. | | `--gpu-memory-utilization 0.90` | The fraction of each GPU's memory vLLM is allowed to claim, for weights plus KV cache plus working space. | 0.90 leaves a little headroom. Push it to 0.95 for more cache, but leave room or startup fails. | | `--max-model-len 32768` | The longest single request, prompt plus reply, in tokens. | The model supports 262,144, but that would eat 47 GiB of cache for one user. 32,768 is a sane serving value. | | `--max-num-seqs 32` | How many requests may be in flight at once. | Caps how much KV cache can be demanded simultaneously. Lower it if you see requests being preempted. | @@ -327,13 +361,13 @@ The startup log is the best teaching tool in the whole stack, and almost nobody If you divide the full model size by your `--tensor-parallel-size` and get roughly this number, the split worked. If this number equals the **whole** model, something is wrong and you are not actually splitting. -**Line two, what is left for conversations:** +**Line two, what is left for conversations.** On vLLM 0.27.1 this arrives inside the long `gpu_worker.py` line you can see in Step 5, phrased as: ``` -Available KV cache memory: X GiB +Current kv cache memory in use is X GiB ``` -If this is **negative**, your weights plus overhead already exceeded the budget, and vLLM will refuse to start. That is the clearest possible signal that you need more GPUs, a smaller number format, or a lower `--max-model-len`. +Older versions print a dedicated `Available KV cache memory: X GiB` line instead, so search for both. If the figure is **negative**, your weights plus overhead already exceeded the budget and vLLM will refuse to start. That is the clearest possible signal that you need more GPUs, a smaller number format, or a lower `--max-model-len`. **Line three, the cache in tokens:** @@ -351,13 +385,30 @@ Maximum concurrency for 32,768 tokens per request: N.NNx This is the one to show your capacity planner. If it says `2.05x`, then two users can each have a full-length 32k conversation, and a third will have to wait or be preempted. It is simply the previous line divided by `--max-model-len`. -For our run the four lines came out as: `Model loading took 55.19 GiB` per worker, `Available KV cache memory: 27.85 GiB`, `GPU KV cache size: 621,392 tokens`, and a maximum concurrency of `18.96x` at 32k. Predicting that token count by hand, 27.85 GiB divided by 47 KiB per token per card, gives 621,337 against the 621,392 vLLM printed, which is the kind of agreement that tells you the mental model is right. +For our run the four figures came out as: `Model loading took 55.19 GiB` per worker, 27.85 GiB of kv cache in use, `GPU KV cache size: 621,392 tokens`, and a maximum concurrency of `18.96x` at 32k. Predicting that token count by hand, 27.85 GiB divided by 47 KiB per token per card, gives 621,337 against the 621,392 vLLM printed, which is the kind of agreement that tells you the mental model is right. ## Step 7: Benchmark it, and what we would run -Once it was running, we compared all three ways of splitting the same model over the same 4 GPUs: tensor parallelism on its own, tensor parallelism plus expert parallelism, and pure pipeline parallelism. Same hardware, same flags otherwise, same benchmark. +Once it was running, we compared all three ways of splitting the same model over the same 4 GPUs: tensor parallelism on its own, tensor parallelism plus expert parallelism, and pure pipeline parallelism. Same hardware, same flags otherwise, same benchmark shape. + +Two numbers do most of the talking. **Time to first token** is how long the user waits before anything appears, and **output tokens per second** is how fast the answer then streams. Every configuration trades one against the other, and one idea explains why. + +### The two jobs hiding inside inference + +Inference is really two different jobs wearing one coat, and almost everything confusing about multi-GPU performance comes from this split. -Two numbers do most of the talking. **Time to first token** is how long the user waits before anything appears, and **output tokens per second** is how fast the answer then streams. Every configuration trades one against the other; part two is about why. +**Prefill reads your prompt.** All 1,024 tokens go through every layer at once, as one big batch. This is the phase that decides time to first token. It is *compute-heavy*: there is a lot of arithmetic and the matrix engines are the bottleneck. It also writes keys and values for all 1,024 tokens into the KV cache. + +**Decode writes the answer**, and it can only produce one token at a time. To write token 2 the model needs token 1, because it feeds its own output back in. That is what "autoregressive" means, and there is no way around it. Each pass produces exactly one token, reads the whole KV cache built so far, and appends one entry. Decode is *memory-heavy* rather than compute-heavy: for a single token there is barely any arithmetic, but the GPU still has to stream the weights and the entire cache past its compute units. + +| | Prefill | Decode | +| --- | --- | --- | +| Work per step | your whole prompt at once | exactly one token | +| Bottleneck | compute | memory bandwidth | +| Metric it drives | time to first token | time per output token | +| Data moved between GPUs | large, whole prompt's worth | tiny, one token's worth | + +That last row is the one to hold on to, and it is the whole explanation for the table below. Those 188 all-reduces from Step 3 are trivially cheap during decode, because each one carries a single token's worth of data. During prefill the same 188 all-reduces carry a thousand times more. So tensor parallelism pays its communication bill mostly at first-token time, and pipeline parallelism, which just hands one activation tensor to the next stage, skips it. That is the trade, and you are about to watch it happen. The benchmark is vLLM's own, 1024 tokens in and 256 tokens out per request, with `--ignore-eos` so every request generates exactly 256 tokens and the comparison is fair: @@ -452,11 +503,63 @@ One subtlety: 32 is not the only ceiling in play. The startup log said this conf One benchmarking warning before you copy this: if you re-run against a warm server, either vary the `--seed` or turn prefix caching off. We forgot, and time to first token "improved" from 265 ms to 61 ms purely because we had just sent the server those same prompts with the same seed. -**The verdict.** Tensor parallelism won nearly everything: 507.09 output tokens/sec at 32 concurrent requests, which is 70% faster than pipeline parallelism; the fastest single-request decode at 17.14 ms median per token; the largest conversation capacity at 621,392 cached tokens; and perfectly even memory across all four cards. Pipeline parallelism won exactly one metric, time to first token, by 15%. Expert parallelism cost 7% and returned nothing at this scale. +### The memory side + +We ran the same pair of benchmarks against all three configurations. First, where the memory went: + +| | TP=4 | TP=4 plus EP | PP=4 | +| --- | --- | --- | --- | +| Weights per GPU | 55.19 GiB | 55.19 GiB | 55.70 GiB | +| KV cache per GPU | 27.85 GiB | 27.96 GiB | 26.84 GiB | +| Total KV cache | 621,392 tokens | **623,696 tokens** | 555,680 tokens | +| Max concurrency at 32k | 18.96x | **19.03x** | 16.96x | +| GPU memory used | 88,211 MiB on all 4 | 88,209 MiB on all 4 | 84,283 / 87,899 / 87,899 / 84,507 | + +**Expert parallelism did not save memory.** It moved 0.37% of extra room into the cache, which is noise. If you were hoping expert parallelism would let you fit a model that otherwise does not fit, this is your warning that it will not. + +**Pipeline parallelism cost 10.6% of the cache**, dropping from 621,392 tokens to 555,680, because a pipeline needs extra buffers for the activations travelling between stages and that comes straight out of your conversation capacity. + +Look at the last row. Under tensor parallelism all four cards sat at **exactly 88,211 MiB**, the same number on every one. Under pipeline parallelism they ranged from 84,283 to 87,899 MiB, about 3.5 GiB apart, because a layer split cannot be perfectly even when 94 layers go over 4 GPUs and the ends of the model are not symmetric: the first stage carries the token embedding and the last carries the output head. That evenness check is the quickest sanity test you have that a tensor-parallel split is behaving. + +### The speed side + +| Measurement | TP=4 | TP=4 plus EP | PP=4 | Winner | +| --- | --- | --- | --- | --- | +| Median time per token, 1 request | **17.14 ms** | 18.83 ms | 21.19 ms | TP | +| Output tokens/sec, 32 requests | **507.09** | 470.93 | 296.48 | TP, by 70% over PP | +| Median time to first token, 32 requests | 3,211 ms | 3,705 ms | **2,735 ms** | PP, by 15% | + +Every figure in that table is a rate or a latency rather than a total, which matters because the runs were not all the same size. We measured all three at `--num-prompts 128` first, then re-ran the winner at `--num-prompts 640` to be sure the result held up. It did: TP produced 507.09 tokens/sec at the larger scale against 503.68 at the smaller, and a median first-token latency of 3,211 ms against 3,233 ms. Both inside 1%, so the numbers above are the 640-prompt figures for TP and the 128-prompt figures for the other two, and the comparison is sound. The full output pasted above is from the 640-prompt run. + +Tensor parallelism won nearly everything, and one gap deserves attention. At 32 concurrent requests it produced **70% more tokens per second than pipeline parallelism**. That is not a rounding error, it is a different class of performance, and it lines up exactly with the prefill and decode split above. Tensor parallelism has all four GPUs working on every token. Pipeline parallelism has each GPU working on a different request's stage, and with 32 requests spread over 4 stages there is not enough in flight to keep everyone busy, so cards sit idle waiting their turn. Its median time per token was 24% worse for the same reason. + +**Pipeline parallelism did win one thing, and it is the one the theory predicts:** time to first token, by 15%. Processing a 1,024-token prompt is exactly where tensor parallelism's 188 all-reduces get expensive, because each one carries the whole prompt's worth of data rather than a single token's. Pipeline parallelism hands one activation tensor to the next stage and skips all of it. If your users judge you on how fast the first word appears, that is a real and measurable advantage. + +Expert parallelism costing 7% is not a knock on the technique, and it is important not to over-read it. Expert parallelism exists to solve a problem we do not have: models so large that even a tensor-parallel split cannot hold all the experts, on clusters big enough that duplicating experts everywhere would be wasteful. With 4 GPUs and a model that already fits, we are asking it to do a job it was not designed for and paying an extra network hop per token for nothing. On a 32 or 64 GPU deployment of a trillion-parameter model the answer would very likely flip. + +### One number we threw away, and why + +Being straight about this, because it is a good lesson in reading your own benchmarks. The very first expert-parallel run at one request at a time reported **28.71 output tokens per second**, which would have made expert parallelism look catastrophic. It was not real. Look at the two TTFT figures from that run: + +``` +Mean TTFT (ms): 3987.38 +Median TTFT (ms): 265.56 +``` + +A mean fifteen times the median means one request behaved completely differently from the other eleven. One request stalled for about 45 seconds, almost certainly a one-off kernel compilation on the first pass through a code path, and that single stall stretched the whole benchmark from 63 seconds to 107 seconds. Since throughput is just tokens divided by wall-clock, one stall wrecked the headline number. + +That is why the speed table uses **median time per token** as its decode measurement rather than aggregate throughput. Median per-token latency does not care that one request had a bad start. + +### What we would actually run + +For a 235B MoE on 4 GPUs with no NVLink between them, we would use plain `--tensor-parallel-size 4` and leave both of the others off. It was faster nearly everywhere, it gives the most conversation capacity, it splits memory perfectly evenly, and it is one less thing to reason about. + +We would reach for the other two in specific situations, not as general upgrades: -So for a 235B MoE on 4 GPUs with no NVLink between them, we would use plain `--tensor-parallel-size 4` and leave both of the others off. +- **Pipeline parallelism** if time to first token is the metric you are judged on, or if you are spanning multiple machines where the network between them is genuinely slow. It was 15% better at first-token latency and it barely touches the interconnect. +- **Expert parallelism** when the model is so large that even a tensor-parallel split cannot hold all the experts. That is a real problem at trillion-parameter scale and simply is not our problem at 235B on 4 cards. -When the other two are the right call, why the results fall out this way, and why NVLink is the one component that would reshuffle them, is part two. +One more thing worth saying plainly, because it is the biggest caveat on every number above: **our GPUs have no NVLink.** Every one of those 188 all-reduces per token crosses PCIe and the link between CPU sockets. On a machine with NVLink the all-reduce gets dramatically cheaper, tensor parallelism's one weakness at first-token latency shrinks, and pipeline parallelism's single win would likely disappear. If you are reading this table to plan hardware, the interconnect is the variable to check first. ## Step 8: Errors you will actually hit @@ -548,13 +651,13 @@ Four things to carry out of this, all of them checks you can run in a minute. **Three.** Work out the memory on paper first. Parameters times bytes-per-parameter gives you the weights, and then remember the weights are only one of three things that must fit, alongside the conversation cache and the working space. A model whose weights just barely fit is a model that cannot serve anybody. -**Four.** Read the startup log. `Model loading took`, `Available KV cache memory`, `GPU KV cache size` and `Maximum concurrency` tell you, in four lines, whether your setup is sane and how many users it can actually hold. A negative cache number is the clearest error message in the whole stack. +**Four.** Read the startup log. `Model loading took`, the kv cache memory figure, `GPU KV cache size` and `Maximum concurrency` tell you, in four lines, whether your setup is sane and how many users it can actually hold. A negative cache number is the clearest error message in the whole stack. And for a 235B mixture-of-experts model on four GPUs with no NVLink between them, the answer is plain `--tensor-parallel-size 4`. It was faster nearly everywhere, it gives the most conversation capacity, and it splits memory perfectly evenly. Try it on whatever you have. Two GPUs are enough to see every one of these steps in action, and the log lines mean the same thing whether you are running 4 GPUs or 40. If you hit something we did not cover, tell us and we will add it. -**Part two is the why.** How slicing a layer across GPUs divides the prefill arithmetic between them, why that costs you an all-reduce at every layer, why the bill lands hard on time to first token but barely registers during decode, and why NVLink is the single variable that decides whether tensor parallelism wins. Coming next. +**And check your interconnect before you buy anything.** Every number in Step 7 was measured on GPUs with no NVLink between them, so all 188 all-reduces per token crossed PCIe. That single fact is why pipeline parallelism managed to win first-token latency at all. On a machine with NVLink we would expect that win to vanish. `nvidia-smi topo -m` tells you which world you are in, and it is the first command we run on any new box. ## Credits and references diff --git a/public/llms.txt b/public/llms.txt index 209deb27c..1d31fd7d6 100644 --- a/public/llms.txt +++ b/public/llms.txt @@ -36,7 +36,7 @@ Authoritative, practitioner-written, citation-friendly. Articles include code ex ## Recent posts (most recent 30 of 199) -- [Running a big LLM across multiple GPUs with vLLM](https://blog.kubesimplify.com/running-a-big-llm-across-multiple-gpus-with-vllm) (2026-08-18). A runbook for serving a model too big for one GPU: download to serving in eight steps, with every vLLM flag, startup log line and real error explained, measured on a 235B model across four RTX PRO 6000 cards. +- [Running a big LLM across multiple GPUs with vLLM](https://blog.kubesimplify.com/running-a-big-llm-across-multiple-gpus-with-vllm) (2026-08-18). A runbook for serving a model too big for one GPU: download to serving in eight steps, with every vLLM flag, startup log line and real error explained, plus tensor, pipeline and expert parallelism benchmarked head to head on a 235B model across four RTX PRO 6000 cards. - [The Local LLM Glossary: Every Term, Flag, and Number in Plain English](https://blog.kubesimplify.com/local-llm-glossary) (2026-08-18). Plain-English definitions for every term you hit in local LLM posts: prefill and decode, tokens per second, FP8 and NVFP4, Q4_K_M, KV cache, YaRN, Gated DeltaNet, speculative decoding, and every vLLM, llama.cpp, and Ollama flag worth knowing. - [Running Qwen3.8-27B on DGX Spark](https://blog.kubesimplify.com/qwen3-8-27b-on-dgx-spark) (2026-08-17). Qwen3.8-27B on DGX Spark with llama.cpp, Ollama, vLLM, and SGLang: the recipes, the tokens per second I measured, MTP speculative decoding, and the sharp edges I hit along the way. - [I Ran an AI SRE Copilot on My Own Hardware. Here Is What It Actually Does.](https://blog.kubesimplify.com/nudgebee-ai-sre-copilot-hands-on) (2026-08-17). Running NudgeBee v1.4.0 end to end - a self-hosted AIOps platform behind AI-SRE, AI-FinOps, AI-K8sOps, and agentic automation - on a Mac, a kiac cluster, and a DGX Spark. diff --git a/public/rss.xml b/public/rss.xml index ffbded123..b796c8ee1 100644 --- a/public/rss.xml +++ b/public/rss.xml @@ -13,7 +13,7 @@ https://blog.kubesimplify.com/running-a-big-llm-across-multiple-gpus-with-vllm https://blog.kubesimplify.com/running-a-big-llm-across-multiple-gpus-with-vllm Tue, 18 Aug 2026 10:00:00 GMT - A runbook for serving a model too big for one GPU: download to serving in eight steps, with every vLLM flag, startup log line and real error explained, measured on a 235B model across four RTX PRO 6000 cards. + A runbook for serving a model too big for one GPU: download to serving in eight steps, with every vLLM flag, startup log line and real error explained, plus tensor, pipeline and expert parallelism benchmarked head to head on a 235B model across four RTX PRO 6000 cards. vllmgpunvidiallmplatform-engineering From abb7e00a27839b06e06c2c16f805b323cb128f52 Mon Sep 17 00:00:00 2001 From: Saiyam Pathak Date: Mon, 31 Aug 2026 11:38:46 +0530 Subject: [PATCH 11/19] Regenerate the cake diagram as SVG in the house Excalidraw style The previous cake-layers.png was an AI-generated illustration whose layer axis had a duplicated tick: it read 1, 2, 3, 4, 4, 5, 6, 7, 8, 9, 10, so eleven marks for ten layers. Text was baked into the raster, so it could not be corrected without regenerating the whole image. scripts/gen-multi-gpu-cake-diagram.mjs now emits it the way every other diagram on the site is built, as hand-rolled SVG in the Excalidraw style of gen-hami-diagrams.mjs, with the layer count as a constant so the axis cannot drift from the labels again. 383 KB of raster becomes 18 KB of SVG and a 116 KB PNG for the markdown reference. --- public/atom.xml | 2 +- .../cake-layers.png | Bin 383391 -> 116276 bytes .../cake-layers.svg | 114 +++++++++++ scripts/gen-multi-gpu-cake-diagram.mjs | 177 ++++++++++++++++++ 4 files changed, 292 insertions(+), 1 deletion(-) create mode 100644 public/img/blog/running-a-big-llm-across-multiple-gpus-with-vllm/cake-layers.svg create mode 100644 scripts/gen-multi-gpu-cake-diagram.mjs diff --git a/public/atom.xml b/public/atom.xml index 772067f5f..f4e29479a 100644 --- a/public/atom.xml +++ b/public/atom.xml @@ -5,7 +5,7 @@ https://blog.kubesimplify.com/ - 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Each GPU owns a block of whole layers. + + +1 + + +2 + + +3 + + +4 + + +5 + + +6 + + +7 + + +8 + + +9 + + +10 +layer + + + + + +GPU 1 +layers 1–3 + + + + + + +GPU 2 +layers 4–6 + + + + + + +GPU 3 +layers 7–10 +TENSOR PARALLELISM +Layers are not cut. Every GPU owns a slice of each one. + +1 + +2 + +3 + +4 + +5 + +6 + +7 + +8 + +9 + +10 +layer + + + + + +GPU 1 +1/3 of all 10 + + + + + + +GPU 2 +1/3 of all 10 + + + + + + +GPU 3 +1/3 of all 10 + + + + +Work moves down the line, one stage at a time. +Little chatter, but GPUs wait their turn. +All GPUs work on the same token at once. +Fast, but they must compare notes every layer. + \ No newline at end of file diff --git a/scripts/gen-multi-gpu-cake-diagram.mjs b/scripts/gen-multi-gpu-cake-diagram.mjs new file mode 100644 index 000000000..0d0f299a3 --- /dev/null +++ b/scripts/gen-multi-gpu-cake-diagram.mjs @@ -0,0 +1,177 @@ +// Excalidraw-style cake diagram for the multi-GPU vLLM article. +// Replaces an AI-generated raster whose layer axis had a duplicated tick. +// Usage: node scripts/gen-multi-gpu-cake-diagram.mjs [outputDir] +import { mkdirSync, writeFileSync } from 'node:fs'; +import { join } from 'node:path'; + +let seed = 91; +const random = () => { + seed = (seed * 16807) % 2147483647; + return seed / 2147483647; +}; +const jitter = (amount) => (random() - 0.5) * amount * 2; + +const COLORS = { + ink: '#172033', + muted: '#5c677d', + blue: { stroke: '#1971c2', fill: '#a5d8ff' }, + green: { stroke: '#5d8f00', fill: '#d8f5a2' }, + violet: { stroke: '#862e9c', fill: '#eebefa' }, +}; + +const FONT = 'Chalkboard SE, Comic Sans MS, sans-serif'; + +function roughLine(x1, y1, x2, y2, amount = 1.8) { + const mx = (x1 + x2) / 2 + jitter(amount * 1.5); + const my = (y1 + y2) / 2 + jitter(amount * 1.5); + return `M ${(x1 + jitter(amount)).toFixed(1)} ${(y1 + jitter(amount)).toFixed(1)} Q ${mx.toFixed(1)} ${my.toFixed(1)} ${(x2 + jitter(amount)).toFixed(1)} ${(y2 + jitter(amount)).toFixed(1)}`; +} + +class Sketch { + constructor(width, height, background = '#f8fafc') { + Object.assign(this, { width, height, background, parts: [], defs: [] }); + } + + add(value) { this.parts.push(value); } + + rect(x, y, w, h, options = {}) { + const { stroke = COLORS.ink, fill, strokeWidth = 2.2, radius = 5, dashed = false } = options; + if (fill) this.add(``); + const pts = [[x, y], [x + w, y], [x + w, y + h], [x, y + h]]; + const d = pts.map((p, i) => { + const n = pts[(i + 1) % pts.length]; + return roughLine(p[0], p[1], n[0], n[1], 1.1); + }).join(' '); + this.add(``); + } + + line(x1, y1, x2, y2, options = {}) { + const { stroke = COLORS.ink, strokeWidth = 2.2, dashed = false } = options; + this.add(``); + } + + arrow(x1, y1, x2, y2, options = {}) { + const { stroke = COLORS.ink, strokeWidth = 2.4 } = options; + this.line(x1, y1, x2, y2, { stroke, strokeWidth }); + const angle = Math.atan2(y2 - y1, x2 - x1); + for (const off of [Math.PI * 0.84, -Math.PI * 0.84]) { + this.line(x2, y2, x2 + 12 * Math.cos(angle + off), y2 + 12 * Math.sin(angle + off), { stroke, strokeWidth }); + } + } + + text(x, y, value, options = {}) { + const { size = 20, color = COLORS.ink, anchor = 'middle', weight = 600 } = options; + const safe = String(value).replace(/&/g, '&').replace(//g, '>'); + this.add(`${safe}`); + } + + lines(x, y, values, options = {}) { + const lh = (options.size || 20) * (options.lineHeight || 1.3); + values.forEach((v, i) => this.text(x, y + i * lh, v, options)); + } + + save(path) { + writeFileSync(path, ` +${this.defs.join('')} + +${this.parts.join('\n')} +`); + } +} + +const LAYERS = 10; +const GPUS = [ + { label: 'GPU 1', color: COLORS.blue }, + { label: 'GPU 2', color: COLORS.green }, + { label: 'GPU 3', color: COLORS.violet }, +]; +// 10 layers over 3 GPUs cannot be even, which is the point the prose makes +// about the ends of the model not being symmetric. +const PIPELINE_GROUPS = [[1, 3], [4, 6], [7, 10]]; + +const output = process.argv[2] || '.'; +mkdirSync(output, { recursive: true }); + +const W = 1200; +const H = 620; +const s = new Sketch(W, H); + +const CAKE_W = 210; +const LAYER_H = 34; +const TOP = 132; +const CAKE_H = LAYERS * LAYER_H; + +function panel(originX, title, subtitle, mode) { + const cakeX = originX + 92; + + s.text(originX + 250, 54, title, { size: 26, weight: 800, anchor: 'middle' }); + s.text(originX + 250, 84, subtitle, { size: 17, weight: 500, color: COLORS.muted, anchor: 'middle' }); + + // One rect per layer. Exactly LAYERS of them, numbered once each. + for (let i = 0; i < LAYERS; i += 1) { + const y = TOP + i * LAYER_H; + const n = i + 1; + let fill; + if (mode === 'pipeline') { + const gi = PIPELINE_GROUPS.findIndex(([lo, hi]) => n >= lo && n <= hi); + fill = GPUS[gi].color.fill; + } + s.rect(cakeX, y, CAKE_W, LAYER_H - 4, { fill, radius: 4 }); + s.text(cakeX - 18, y + LAYER_H / 2 + 2, String(n), { size: 16, weight: 600, color: COLORS.muted, anchor: 'end' }); + } + + s.text(cakeX - 18, TOP - 14, 'layer', { size: 13, weight: 600, color: COLORS.muted, anchor: 'end' }); + + if (mode === 'pipeline') { + // Horizontal cuts between groups, plus a bracket and label per GPU. + PIPELINE_GROUPS.forEach(([lo, hi], gi) => { + if (gi > 0) { + const cutY = TOP + (lo - 1) * LAYER_H - 2; + s.line(cakeX - 8, cutY, cakeX + CAKE_W + 8, cutY, { stroke: COLORS.ink, strokeWidth: 2.6, dashed: true }); + } + const midY = TOP + ((lo - 1) + (hi - lo + 1) / 2) * LAYER_H - 2; + const g = GPUS[gi]; + s.arrow(cakeX + CAKE_W + 14, midY, cakeX + CAKE_W + 54, midY, { stroke: g.color.stroke }); + s.rect(cakeX + CAKE_W + 60, midY - 24, 132, 48, { fill: g.color.fill, stroke: g.color.stroke, radius: 8 }); + s.text(cakeX + CAKE_W + 126, midY - 4, g.label, { size: 17, weight: 800, color: g.color.stroke }); + s.text(cakeX + CAKE_W + 126, midY + 16, `layers ${lo}–${hi}`, { size: 14, weight: 500, color: COLORS.muted }); + }); + } else { + // Vertical cuts: every GPU owns a strip of all LAYERS layers. + const stripW = CAKE_W / GPUS.length; + GPUS.forEach((g, gi) => { + if (gi > 0) { + const cutX = cakeX + gi * stripW; + s.line(cutX, TOP - 8, cutX, TOP + CAKE_H + 4, { stroke: COLORS.ink, strokeWidth: 2.6, dashed: true }); + } + const midY = TOP + CAKE_H * (0.2 + gi * 0.3); + s.arrow(cakeX + CAKE_W + 14, midY, cakeX + CAKE_W + 54, midY, { stroke: g.color.stroke }); + s.rect(cakeX + CAKE_W + 60, midY - 24, 132, 48, { fill: g.color.fill, stroke: g.color.stroke, radius: 8 }); + s.text(cakeX + CAKE_W + 126, midY - 4, g.label, { size: 17, weight: 800, color: g.color.stroke }); + s.text(cakeX + CAKE_W + 126, midY + 16, `1/3 of all ${LAYERS}`, { size: 14, weight: 500, color: COLORS.muted }); + }); + // Tint each strip so the vertical ownership reads at a glance. + GPUS.forEach((g, gi) => { + s.add(``); + }); + } +} + +panel(30, 'PIPELINE PARALLELISM', 'Layers are cut. Each GPU owns a block of whole layers.', 'pipeline'); +panel(620, 'TENSOR PARALLELISM', 'Layers are not cut. Every GPU owns a slice of each one.', 'tensor'); + +// Divider between the two panels. +s.line(600, 40, 600, H - 74, { stroke: '#cbd5e1', strokeWidth: 2, dashed: true }); + +// Footers. +s.lines(280, H - 46, [ + 'Work moves down the line, one stage at a time.', + 'Little chatter, but GPUs wait their turn.', +], { size: 15, weight: 500, color: COLORS.muted, anchor: 'middle', lineHeight: 1.35 }); +s.lines(870, H - 46, [ + 'All GPUs work on the same token at once.', + 'Fast, but they must compare notes every layer.', +], { size: 15, weight: 500, color: COLORS.muted, anchor: 'middle', lineHeight: 1.35 }); + +s.save(join(output, 'cake-layers.svg')); +console.log(`wrote ${join(output, 'cake-layers.svg')} (${LAYERS} layers, ${GPUS.length} GPUs)`); From 964f340ef9437cf39d60267d831f3791fb07f330 Mon Sep 17 00:00:00 2001 From: Saiyam Pathak Date: Mon, 31 Aug 2026 11:42:31 +0530 Subject: [PATCH 12/19] Fix the review findings: a broken table, a duplicated line, and HF_HOME The topology matrix was authored as a markdown table but wrapped in a ```bash fence, so the first technical artifact in the post rendered as raw text: literal `| :---: |` separators and `**GPU0**` asterisks. It is now a real table, with the legend trimmed to the codes that matter. The glossary sentence was hardcoded in the markdown, but GlossaryNote already renders it above the body for anything tagged vllm or llm, so readers saw it twice. Removed the inline copy, which also drops an absolute link the rest of the site writes as a relative one. `HF_HOME` prose pointed at the `hub/` directory the tree diagram is rooted at, while the Step 5 command correctly sets its parent. Following the prose sends the libraries looking in `hub/hub/` and re-downloads 236 GB. Spelled out which one it is and why. Also from the review: - "the largest data-centre GPUs top out well below 236 GB" is false; B300 and MI355X both carry 288 GB. Narrowed to the box we ran on. - "Powers of two are the safe habit" pointed at -tp 8, the exact configuration Step 3 spends a section arguing against on this model. - The copy-pasteable benchmark carried --seed 42 with prefix caching on, the trap the post warns about 80 lines later. Added the flag. - can_device_access_peer returns True on a SYS pair, which read as a contradiction. It reports capability, not speed; said so. - PP's throughput deficit was blamed on too few requests in flight, but 32 over 4 stages is 8 each, which the post's own analogy calls full. Reattributed to uneven stages, which the memory table already shows. - The flags table described three flags the command does not set and omitted VLLM_USE_DEEP_GEMM, without which the server does not boot. - Step 2 told readers to run the fit check before downloading while sitting after the download step. - datePublished predated every pasted log by three days. - The memory-fit animation printed the log string Step 6 now explains vLLM 0.27.1 does not emit. Co-Authored-By: Claude Opus 5 (1M context) --- components/MultiGpuMemoryFitAnimation.jsx | 2 +- ...-big-llm-across-multiple-gpus-with-vllm.md | 75 ++++++++++--------- lib/_blog-feed-data.js | 2 +- public/atom.xml | 6 +- public/llms-full.txt | 75 ++++++++++--------- public/llms.txt | 2 +- public/rss.xml | 4 +- 7 files changed, 88 insertions(+), 78 deletions(-) diff --git a/components/MultiGpuMemoryFitAnimation.jsx b/components/MultiGpuMemoryFitAnimation.jsx index 54032e176..588ca697e 100644 --- a/components/MultiGpuMemoryFitAnimation.jsx +++ b/components/MultiGpuMemoryFitAnimation.jsx @@ -27,7 +27,7 @@ const cases = [ { label: 'weights', value: '55.19 GiB', width: '23.00%', color: '#0098cc' }, { label: 'KV cache', value: '27.85 GiB', width: '11.60%', color: '#2bb534' }, ], - log: 'Worker_TP0 Model loading took 55.19 GiB\nAvailable KV cache memory: 27.85 GiB\nGPU KV cache size: 621,392 tokens', + log: 'Worker_TP0 Model loading took 55.19 GiB\nCurrent kv cache memory in use is 27.85 GiB\nGPU KV cache size: 621,392 tokens', }, ]; diff --git a/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md b/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md index 78038417d..4cc74de02 100644 --- a/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md +++ b/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md @@ -2,7 +2,7 @@ title: "Running a big LLM across multiple GPUs with vLLM" seoTitle: "Running a big LLM across multiple GPUs with vLLM" seoDescription: "A runbook for serving a model too big for one GPU: download to serving in eight steps, with every vLLM flag, startup log line and real error explained, plus tensor, pipeline and expert parallelism benchmarked head to head on a 235B model across four RTX PRO 6000 cards." -datePublished: 2026-08-18T10:00:00.000Z +datePublished: 2026-08-31T10:00:00.000Z slug: running-a-big-llm-across-multiple-gpus-with-vllm author: shubham-katara authors: ["shubham-katara", "saiyam-pathak"] @@ -10,7 +10,7 @@ cover: /img/blog/running-a-big-llm-across-multiple-gpus-with-vllm/cover.png tags: ["vllm", "gpu", "nvidia", "llm", "platform-engineering"] --- -Sooner or later everyone running models locally hits the same wall. You find a model you want, you look at the download size, and it is bigger than the GPU you own. A 235B model needs roughly 236 GB just for its weights. The card we have holds 96 GB, and even the largest data-centre GPUs available today top out well below 236 GB. So the model does not fit, and no amount of clever flags will make 236 GB squeeze into 96 GB. +Sooner or later everyone running models locally hits the same wall. You find a model you want, you look at the download size, and it is bigger than the GPU you own. A 235B model needs roughly 236 GB just for its weights. The card we have holds 96 GB. A handful of current data-centre parts do carry more, but nothing on our machine does, and no amount of clever flags will make 236 GB squeeze into 96 GB. The answer is to use more than one GPU. That part everybody knows. The part that is genuinely confusing is what "use more than one GPU" actually means. Does each GPU get a copy of the model? Does the model get cut in half? Do the GPUs take turns? Which of those is happening, and what does it cost you? @@ -22,7 +22,6 @@ This is the runbook. Eight steps, from downloading a 236 GB model to serving it The theory arrives where you need it to make a decision, not before. Step 3 explains what a tensor-parallel split actually costs, because that is where you pick one, and Step 7 explains why the three options trade against each other, because that is where you read the numbers. Nothing here is theory for its own sake. -New to the jargon? Every term, flag, and benchmark number here is explained in plain English in the [local LLM glossary](https://blog.kubesimplify.com/local-llm-glossary). ## The machine and the model @@ -34,32 +33,31 @@ One detail that matters more than it looks: these GPUs are **not** connected by ```bash root@utho-gpu-rtxpro6000-8-62383:~# nvidia-smi topo -m +``` + +| Device | GPU0 | GPU1 | GPU2 | GPU3 | GPU4 | GPU5 | GPU6 | GPU7 | NIC0 | CPU Affinity | NUMA Affinity | +| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :--- | :---: | +| **GPU0** | X | SYS | SYS | SYS | SYS | SYS | SYS | SYS | SYS | 48-55,176-183 | 6 | +| **GPU1** | SYS | X | SYS | SYS | SYS | SYS | SYS | SYS | PHB | 32-39,160-167 | 4 | +| **GPU2** | SYS | SYS | X | SYS | SYS | SYS | SYS | SYS | SYS | 0-7,128-135 | 0 | +| **GPU3** | SYS | SYS | SYS | X | SYS | SYS | SYS | SYS | SYS | 16-23,144-151 | 2 | +| **GPU4** | SYS | SYS | SYS | SYS | X | SYS | SYS | SYS | SYS | 112-119,240-247 | 14 | +| **GPU5** | SYS | SYS | SYS | SYS | SYS | X | SYS | SYS | SYS | 96-103,224-231 | 12 | +| **GPU6** | SYS | SYS | SYS | SYS | SYS | SYS | X | SYS | SYS | 64-71,192-199 | 8 | +| **GPU7** | SYS | SYS | SYS | SYS | SYS | SYS | SYS | X | SYS | 80-87,208-215 | 10 | +| **NIC0** | SYS | PHB | SYS | SYS | SYS | SYS | SYS | SYS | X | | | + +The legend that command prints, trimmed to the codes that matter here: -| Device | GPU0 | GPU1 | GPU2 | GPU3 | GPU4 | GPU5 | GPU6 | GPU7 | NIC0 | CPU Affinity | NUMA Affinity | GPU NUMA ID | -| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :--- | :---: | :---: | -| **GPU0** | **X** | SYS | SYS | SYS | SYS | SYS | SYS | SYS | SYS | 48-55,176-183 | 6 | N/A | -| **GPU1** | SYS | **X** | SYS | SYS | SYS | SYS | SYS | SYS | PHB | 32-39,160-167 | 4 | N/A | -| **GPU2** | SYS | SYS | **X** | SYS | SYS | SYS | SYS | SYS | SYS | 0-7,128-135 | 0 | N/A | -| **GPU3** | SYS | SYS | SYS | **X** | SYS | SYS | SYS | SYS | SYS | 16-23,144-151 | 2 | N/A | -| **GPU4** | SYS | SYS | SYS | SYS | **X** | SYS | SYS | SYS | SYS | 112-119,240-247 | 14 | N/A | -| **GPU5** | SYS | SYS | SYS | SYS | SYS | **X** | SYS | SYS | SYS | 96-103,224-231 | 12 | N/A | -| **GPU6** | SYS | SYS | SYS | SYS | SYS | SYS | **X** | SYS | SYS | 64-71,192-199 | 8 | N/A | -| **GPU7** | SYS | SYS | SYS | SYS | SYS | SYS | SYS | **X** | SYS | 80-87,208-215 | 10 | N/A | -| **NIC0** | SYS | PHB | SYS | SYS | SYS | SYS | SYS | SYS | **X** | | | | - -**Legend:** - -| Symbol | Description | +| Symbol | Meaning | | :--- | :--- | -| **X** | Self | -| **SYS** | Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI) | -| **NODE** | Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node | -| **PHB** | Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU) | -| **PXB** | Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge) | -| **PIX** | Connection traversing at most a single PCIe bridge | -| **NV#** | Connection traversing a bonded set of `#` NVLinks | -| **NIC0** | `mlx4_0` | -``` +| `X` | Self | +| `SYS` | Across PCIe **and** the interconnect between CPU sockets. The slowest option. | +| `NODE` | Across PCIe and the bridges inside one NUMA node | +| `PHB` | Across PCIe and a PCIe host bridge, typically the CPU | +| `PXB` | Across multiple PCIe bridges, without touching the host bridge | +| `PIX` | Across at most a single PCIe bridge. The fastest non-NVLink option. | +| `NV#` | Across a bonded set of `#` NVLinks | On our machine every pair of GPUs reports `SYS`, which means the traffic goes across PCIe and then across the link between the CPU sockets. If you had NVLink you would see `NV1`, `NV2` and so on instead. Keep this in mind, because it changes which splitting method is fastest. @@ -150,7 +148,7 @@ By default everything lands under `~/.cache/huggingface/hub`, in a layout that l The content lives once in `blobs/` under its hash, and `snapshots/` holds human-readable symlinks into it. That is why pulling two revisions of a model does not always double your disk usage, and it is also why `du` and `df` can disagree with your intuition. -The practical consequence for serving: mount that whole directory into your container and set `HF_HOME` to it, which is exactly what the `-v` and `-e HF_HOME` flags in Step 5 are doing. Otherwise the container downloads its own copy. +The practical consequence for serving: mount `~/.cache/huggingface` into the container and point `HF_HOME` at it. Note that this is the **parent** of the `hub/` directory in the tree above, not `hub/` itself: the libraries append `hub/` themselves, so `HF_HOME=~/.cache/huggingface/hub` sends them looking in `hub/hub/` and they find nothing. That is exactly what the `-v` and `-e HF_HOME` flags in Step 5 are doing. Get it wrong and the container downloads its own 236 GB copy. One more thing about loading that surprises people. When you split the model over 4 GPUs, vLLM starts 4 separate processes, one per GPU, and **every one of them reads the whole download from disk**, keeping only the quarter it needs. @@ -160,7 +158,7 @@ Our own `Model loading took` line, which you can see in Step 5, reported 48.5 se ## Step 2: Will it fit? The ten-minute check -Do this on paper before the download, not after. The arithmetic is simpler than people expect. +It sits after the download here because Step 1 is where the disk hazard bites, but on your next model run this check first: it is ten minutes with a calculator and it can save you a quarter-terabyte download that was never going to fit. The arithmetic is simpler than people expect. **First, the weights.** One parameter costs this many bytes: @@ -281,6 +279,12 @@ Before the command, the vocabulary. Here is every flag we use and why it has the | `--enforce-eager` | Skips building optimised CUDA graphs at startup. | We do **not** use it. It saves memory and starts faster, but generation is slower. Reach for it only if you are out of memory. | | `--kv-cache-dtype fp8` | Stores the conversation cache at 8 bits instead of 16, roughly halving cache memory. | We left it at the default so our cache numbers are easy to check by hand. It is a good lever if you need more concurrency. | +Three rows above describe flags we measured but did not keep: `--pipeline-parallel-size` and `--enable-expert-parallel` are the alternatives benchmarked in Step 7, and `--distributed-executor-backend mp` is already vLLM's default for a single machine, so it is in the table for the day you need `ray` rather than because our command sets it. The command in Step 5 carries only what our final configuration needs, plus one environment variable: + +| Environment variable | Why you need it | +| --- | --- | +| `VLLM_USE_DEEP_GEMM=0` | Not optional on our hardware. Without it all four workers die during startup with `Unknown SF transformation`. Step 8 explains why, and it is specific to FP8 block-scaled weights on `sm_120` cards, so try without it first on anything else. | + Two container flags matter just as much, and neither is a vLLM flag: | Docker flag | Why you need it | @@ -346,7 +350,7 @@ GPUs visible: 2 can GPU 0 talk to GPU 1 directly: True ``` -That is a genuinely useful sanity check before you start a long model load, because it confirms the container can see the cards and that direct GPU-to-GPU access is available. +That is a genuinely useful sanity check before you start a long model load, because it confirms the container can see the cards. Do read the second line carefully though: `can_device_access_peer` tells you peer-to-peer addressing is **possible**, not that it is fast. Ours returns `True` while `nvidia-smi topo -m` still reports `SYS` for that pair, because the transfer is permitted but it is going over PCIe and across sockets. The topology matrix is the one that tells you what performance to expect. ## Step 6: How to read the startup log @@ -417,7 +421,8 @@ root@utho-gpu-rtxpro6000-8-62383:~# docker exec vllm-tp4 vllm bench serve \ --served-model-name qwen3-235b \ --base-url http://localhost:8000 \ --dataset-name random --random-input-len 1024 --random-output-len 256 \ - --max-concurrency 1 --num-prompts 12 --seed 42 --ignore-eos + --max-concurrency 1 --num-prompts 12 --seed 42 --ignore-eos \ + --no-enable-prefix-caching Starting initial single prompt test run... Skipping endpoint ready check. @@ -500,7 +505,7 @@ P99 ITL (ms): 442.81 One subtlety: 32 is not the only ceiling in play. The startup log said this configuration holds about 19 full-length 32k conversations in its KV cache, and our benchmark requests are short, so `--max-num-seqs` is the limit that binds here. With long conversations the cache fills first, and instead of queueing politely vLLM starts preempting: it evicts a running request's cache and recomputes it later. Which ceiling you hit first depends entirely on how long your requests are. -One benchmarking warning before you copy this: if you re-run against a warm server, either vary the `--seed` or turn prefix caching off. We forgot, and time to first token "improved" from 265 ms to 61 ms purely because we had just sent the server those same prompts with the same seed. +That `--no-enable-prefix-caching` on the end is not decoration, and we learned it the hard way. With a fixed `--seed 42` and prefix caching on, re-running against a warm server made time to first token "improve" from 265 ms to 61 ms, purely because we had just sent it those same prompts. If you would rather keep caching on, vary the seed between runs instead. Either way, do not compare a cold run against a warm one. ### The memory side @@ -530,7 +535,7 @@ Look at the last row. Under tensor parallelism all four cards sat at **exactly 8 Every figure in that table is a rate or a latency rather than a total, which matters because the runs were not all the same size. We measured all three at `--num-prompts 128` first, then re-ran the winner at `--num-prompts 640` to be sure the result held up. It did: TP produced 507.09 tokens/sec at the larger scale against 503.68 at the smaller, and a median first-token latency of 3,211 ms against 3,233 ms. Both inside 1%, so the numbers above are the 640-prompt figures for TP and the 128-prompt figures for the other two, and the comparison is sound. The full output pasted above is from the 640-prompt run. -Tensor parallelism won nearly everything, and one gap deserves attention. At 32 concurrent requests it produced **70% more tokens per second than pipeline parallelism**. That is not a rounding error, it is a different class of performance, and it lines up exactly with the prefill and decode split above. Tensor parallelism has all four GPUs working on every token. Pipeline parallelism has each GPU working on a different request's stage, and with 32 requests spread over 4 stages there is not enough in flight to keep everyone busy, so cards sit idle waiting their turn. Its median time per token was 24% worse for the same reason. +Tensor parallelism won nearly everything, and one gap deserves attention. At 32 concurrent requests it produced **70% more tokens per second than pipeline parallelism**. That is not a rounding error, it is a different class of performance, and it lines up exactly with the prefill and decode split above. Tensor parallelism has all four GPUs working on every token. Pipeline parallelism has each GPU working on a different request's stage, and it only pays off when every stage takes the same time. Ours do not: the memory table above shows the four stages holding 84,283 to 87,899 MiB, because 94 layers do not divide evenly by 4 and the first stage carries the token embedding while the last carries the output head. Every uneven stage is a bubble the other three wait on, on every single token. Its median time per token was 24% worse for the same reason. **Pipeline parallelism did win one thing, and it is the one the theory predicts:** time to first token, by 15%. Processing a 1,024-token prompt is exactly where tensor parallelism's 188 all-reduces get expensive, because each one carries the whole prompt's worth of data rather than a single token's. Pipeline parallelism hands one activation tensor to the next stage and skips all of it. If your users judge you on how fast the first word appears, that is a real and measurable advantage. @@ -547,7 +552,7 @@ Median TTFT (ms): 265.56 A mean fifteen times the median means one request behaved completely differently from the other eleven. One request stalled for about 45 seconds, almost certainly a one-off kernel compilation on the first pass through a code path, and that single stall stretched the whole benchmark from 63 seconds to 107 seconds. Since throughput is just tokens divided by wall-clock, one stall wrecked the headline number. -That is why the speed table uses **median time per token** as its decode measurement rather than aggregate throughput. Median per-token latency does not care that one request had a bad start. +That is why the speed table carries **median time per token** alongside aggregate throughput rather than relying on throughput alone. Median per-token latency does not care that one request had a bad start, so when the two rows agree, as they do for all three configurations above, the throughput figure is trustworthy. When they disagree, believe the median and go looking for a stall. ### What we would actually run @@ -576,7 +581,7 @@ pydantic_core._pydantic_core.ValidationError: 1 validation error for VllmConfig **What it means:** the rule from Step 3. 64 heads cannot be shared out evenly among 3 GPUs. Good news, it fails in about a second, before loading a single byte of weights. -**The fix:** pick a `--tensor-parallel-size` that divides your head count. Powers of two are the safe habit. +**The fix:** pick a `--tensor-parallel-size` that divides **both** head counts, attention and key/value. Do not just reach for the next power of two: `-tp 8` divides our 64 attention heads but not our 4 KV heads, which is the case Step 3 warns about. ### "Failed to load model - not enough GPU memory" diff --git a/lib/_blog-feed-data.js b/lib/_blog-feed-data.js index bba41d0e4..c1a2e1bee 100644 --- a/lib/_blog-feed-data.js +++ b/lib/_blog-feed-data.js @@ -4,7 +4,7 @@ export const FEED_POSTS = [ "slug": "running-a-big-llm-across-multiple-gpus-with-vllm", "title": "Running a big LLM across multiple GPUs with vLLM", "description": "A runbook for serving a model too big for one GPU: download to serving in eight steps, with every vLLM flag, startup log line and real error explained, plus tensor, pipeline and expert parallelism benchmarked head to head on a 235B model across four RTX PRO 6000 cards.", - "datePublished": "2026-08-18T10:00:00.000Z", + "datePublished": "2026-08-31T10:00:00.000Z", "cover": "/img/blog/running-a-big-llm-across-multiple-gpus-with-vllm/cover.png", "tags": [ "vllm", diff --git a/public/atom.xml b/public/atom.xml index f4e29479a..65b8e7226 100644 --- a/public/atom.xml +++ b/public/atom.xml @@ -5,7 +5,7 @@ https://blog.kubesimplify.com/ - 2026-08-31T06:07:18.014Z + 2026-08-31T06:11:37.818Z Kubesimplify hello@kubesimplify.com @@ -14,8 +14,8 @@ Running a big LLM across multiple GPUs with vLLM https://blog.kubesimplify.com/running-a-big-llm-across-multiple-gpus-with-vllm - 2026-08-18T10:00:00.000Z - 2026-08-18T10:00:00.000Z + 2026-08-31T10:00:00.000Z + 2026-08-31T10:00:00.000Z

      A runbook for serving a model too big for one GPU: download to serving in eight steps, with every vLLM flag, startup log line and real error explained, plus tensor, pipeline and expert parallelism benchmarked head to head on a 235B model across four RTX PRO 6000 cards. diff --git a/public/llms-full.txt b/public/llms-full.txt index e86c73bcf..3178a1938 100644 --- a/public/llms-full.txt +++ b/public/llms-full.txt @@ -8,10 +8,10 @@ # Running a big LLM across multiple GPUs with vLLM - Canonical: https://blog.kubesimplify.com/running-a-big-llm-across-multiple-gpus-with-vllm -- Published: 2026-08-18 +- Published: 2026-08-31 - Summary: A runbook for serving a model too big for one GPU: download to serving in eight steps, with every vLLM flag, startup log line and real error explained, plus tensor, pipeline and expert parallelism benchmarked head to head on a 235B model across four RTX PRO 6000 cards. -Sooner or later everyone running models locally hits the same wall. You find a model you want, you look at the download size, and it is bigger than the GPU you own. A 235B model needs roughly 236 GB just for its weights. The card we have holds 96 GB, and even the largest data-centre GPUs available today top out well below 236 GB. So the model does not fit, and no amount of clever flags will make 236 GB squeeze into 96 GB. +Sooner or later everyone running models locally hits the same wall. You find a model you want, you look at the download size, and it is bigger than the GPU you own. A 235B model needs roughly 236 GB just for its weights. The card we have holds 96 GB. A handful of current data-centre parts do carry more, but nothing on our machine does, and no amount of clever flags will make 236 GB squeeze into 96 GB. The answer is to use more than one GPU. That part everybody knows. The part that is genuinely confusing is what "use more than one GPU" actually means. Does each GPU get a copy of the model? Does the model get cut in half? Do the GPUs take turns? Which of those is happening, and what does it cost you? @@ -23,7 +23,6 @@ This is the runbook. Eight steps, from downloading a 236 GB model to serving it The theory arrives where you need it to make a decision, not before. Step 3 explains what a tensor-parallel split actually costs, because that is where you pick one, and Step 7 explains why the three options trade against each other, because that is where you read the numbers. Nothing here is theory for its own sake. -New to the jargon? Every term, flag, and benchmark number here is explained in plain English in the [local LLM glossary](https://blog.kubesimplify.com/local-llm-glossary). ## The machine and the model @@ -35,32 +34,31 @@ One detail that matters more than it looks: these GPUs are **not** connected by ```bash root@utho-gpu-rtxpro6000-8-62383:~# nvidia-smi topo -m +``` + +| Device | GPU0 | GPU1 | GPU2 | GPU3 | GPU4 | GPU5 | GPU6 | GPU7 | NIC0 | CPU Affinity | NUMA Affinity | +| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :--- | :---: | +| **GPU0** | X | SYS | SYS | SYS | SYS | SYS | SYS | SYS | SYS | 48-55,176-183 | 6 | +| **GPU1** | SYS | X | SYS | SYS | SYS | SYS | SYS | SYS | PHB | 32-39,160-167 | 4 | +| **GPU2** | SYS | SYS | X | SYS | SYS | SYS | SYS | SYS | SYS | 0-7,128-135 | 0 | +| **GPU3** | SYS | SYS | SYS | X | SYS | SYS | SYS | SYS | SYS | 16-23,144-151 | 2 | +| **GPU4** | SYS | SYS | SYS | SYS | X | SYS | SYS | SYS | SYS | 112-119,240-247 | 14 | +| **GPU5** | SYS | SYS | SYS | SYS | SYS | X | SYS | SYS | SYS | 96-103,224-231 | 12 | +| **GPU6** | SYS | SYS | SYS | SYS | SYS | SYS | X | SYS | SYS | 64-71,192-199 | 8 | +| **GPU7** | SYS | SYS | SYS | SYS | SYS | SYS | SYS | X | SYS | 80-87,208-215 | 10 | +| **NIC0** | SYS | PHB | SYS | SYS | SYS | SYS | SYS | SYS | X | | | + +The legend that command prints, trimmed to the codes that matter here: -| Device | GPU0 | GPU1 | GPU2 | GPU3 | GPU4 | GPU5 | GPU6 | GPU7 | NIC0 | CPU Affinity | NUMA Affinity | GPU NUMA ID | -| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :--- | :---: | :---: | -| **GPU0** | **X** | SYS | SYS | SYS | SYS | SYS | SYS | SYS | SYS | 48-55,176-183 | 6 | N/A | -| **GPU1** | SYS | **X** | SYS | SYS | SYS | SYS | SYS | SYS | PHB | 32-39,160-167 | 4 | N/A | -| **GPU2** | SYS | SYS | **X** | SYS | SYS | SYS | SYS | SYS | SYS | 0-7,128-135 | 0 | N/A | -| **GPU3** | SYS | SYS | SYS | **X** | SYS | SYS | SYS | SYS | SYS | 16-23,144-151 | 2 | N/A | -| **GPU4** | SYS | SYS | SYS | SYS | **X** | SYS | SYS | SYS | SYS | 112-119,240-247 | 14 | N/A | -| **GPU5** | SYS | SYS | SYS | SYS | SYS | **X** | SYS | SYS | SYS | 96-103,224-231 | 12 | N/A | -| **GPU6** | SYS | SYS | SYS | SYS | SYS | SYS | **X** | SYS | SYS | 64-71,192-199 | 8 | N/A | -| **GPU7** | SYS | SYS | SYS | SYS | SYS | SYS | SYS | **X** | SYS | 80-87,208-215 | 10 | N/A | -| **NIC0** | SYS | PHB | SYS | SYS | SYS | SYS | SYS | SYS | **X** | | | | - -**Legend:** - -| Symbol | Description | +| Symbol | Meaning | | :--- | :--- | -| **X** | Self | -| **SYS** | Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI) | -| **NODE** | Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node | -| **PHB** | Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU) | -| **PXB** | Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge) | -| **PIX** | Connection traversing at most a single PCIe bridge | -| **NV#** | Connection traversing a bonded set of `#` NVLinks | -| **NIC0** | `mlx4_0` | -``` +| `X` | Self | +| `SYS` | Across PCIe **and** the interconnect between CPU sockets. The slowest option. | +| `NODE` | Across PCIe and the bridges inside one NUMA node | +| `PHB` | Across PCIe and a PCIe host bridge, typically the CPU | +| `PXB` | Across multiple PCIe bridges, without touching the host bridge | +| `PIX` | Across at most a single PCIe bridge. The fastest non-NVLink option. | +| `NV#` | Across a bonded set of `#` NVLinks | On our machine every pair of GPUs reports `SYS`, which means the traffic goes across PCIe and then across the link between the CPU sockets. If you had NVLink you would see `NV1`, `NV2` and so on instead. Keep this in mind, because it changes which splitting method is fastest. @@ -151,7 +149,7 @@ By default everything lands under `~/.cache/huggingface/hub`, in a layout that l The content lives once in `blobs/` under its hash, and `snapshots/` holds human-readable symlinks into it. That is why pulling two revisions of a model does not always double your disk usage, and it is also why `du` and `df` can disagree with your intuition. -The practical consequence for serving: mount that whole directory into your container and set `HF_HOME` to it, which is exactly what the `-v` and `-e HF_HOME` flags in Step 5 are doing. Otherwise the container downloads its own copy. +The practical consequence for serving: mount `~/.cache/huggingface` into the container and point `HF_HOME` at it. Note that this is the **parent** of the `hub/` directory in the tree above, not `hub/` itself: the libraries append `hub/` themselves, so `HF_HOME=~/.cache/huggingface/hub` sends them looking in `hub/hub/` and they find nothing. That is exactly what the `-v` and `-e HF_HOME` flags in Step 5 are doing. Get it wrong and the container downloads its own 236 GB copy. One more thing about loading that surprises people. When you split the model over 4 GPUs, vLLM starts 4 separate processes, one per GPU, and **every one of them reads the whole download from disk**, keeping only the quarter it needs. @@ -161,7 +159,7 @@ Our own `Model loading took` line, which you can see in Step 5, reported 48.5 se ## Step 2: Will it fit? The ten-minute check -Do this on paper before the download, not after. The arithmetic is simpler than people expect. +It sits after the download here because Step 1 is where the disk hazard bites, but on your next model run this check first: it is ten minutes with a calculator and it can save you a quarter-terabyte download that was never going to fit. The arithmetic is simpler than people expect. **First, the weights.** One parameter costs this many bytes: @@ -282,6 +280,12 @@ Before the command, the vocabulary. Here is every flag we use and why it has the | `--enforce-eager` | Skips building optimised CUDA graphs at startup. | We do **not** use it. It saves memory and starts faster, but generation is slower. Reach for it only if you are out of memory. | | `--kv-cache-dtype fp8` | Stores the conversation cache at 8 bits instead of 16, roughly halving cache memory. | We left it at the default so our cache numbers are easy to check by hand. It is a good lever if you need more concurrency. | +Three rows above describe flags we measured but did not keep: `--pipeline-parallel-size` and `--enable-expert-parallel` are the alternatives benchmarked in Step 7, and `--distributed-executor-backend mp` is already vLLM's default for a single machine, so it is in the table for the day you need `ray` rather than because our command sets it. The command in Step 5 carries only what our final configuration needs, plus one environment variable: + +| Environment variable | Why you need it | +| --- | --- | +| `VLLM_USE_DEEP_GEMM=0` | Not optional on our hardware. Without it all four workers die during startup with `Unknown SF transformation`. Step 8 explains why, and it is specific to FP8 block-scaled weights on `sm_120` cards, so try without it first on anything else. | + Two container flags matter just as much, and neither is a vLLM flag: | Docker flag | Why you need it | @@ -347,7 +351,7 @@ GPUs visible: 2 can GPU 0 talk to GPU 1 directly: True ``` -That is a genuinely useful sanity check before you start a long model load, because it confirms the container can see the cards and that direct GPU-to-GPU access is available. +That is a genuinely useful sanity check before you start a long model load, because it confirms the container can see the cards. Do read the second line carefully though: `can_device_access_peer` tells you peer-to-peer addressing is **possible**, not that it is fast. Ours returns `True` while `nvidia-smi topo -m` still reports `SYS` for that pair, because the transfer is permitted but it is going over PCIe and across sockets. The topology matrix is the one that tells you what performance to expect. ## Step 6: How to read the startup log @@ -418,7 +422,8 @@ root@utho-gpu-rtxpro6000-8-62383:~# docker exec vllm-tp4 vllm bench serve \ --served-model-name qwen3-235b \ --base-url http://localhost:8000 \ --dataset-name random --random-input-len 1024 --random-output-len 256 \ - --max-concurrency 1 --num-prompts 12 --seed 42 --ignore-eos + --max-concurrency 1 --num-prompts 12 --seed 42 --ignore-eos \ + --no-enable-prefix-caching Starting initial single prompt test run... Skipping endpoint ready check. @@ -501,7 +506,7 @@ P99 ITL (ms): 442.81 One subtlety: 32 is not the only ceiling in play. The startup log said this configuration holds about 19 full-length 32k conversations in its KV cache, and our benchmark requests are short, so `--max-num-seqs` is the limit that binds here. With long conversations the cache fills first, and instead of queueing politely vLLM starts preempting: it evicts a running request's cache and recomputes it later. Which ceiling you hit first depends entirely on how long your requests are. -One benchmarking warning before you copy this: if you re-run against a warm server, either vary the `--seed` or turn prefix caching off. We forgot, and time to first token "improved" from 265 ms to 61 ms purely because we had just sent the server those same prompts with the same seed. +That `--no-enable-prefix-caching` on the end is not decoration, and we learned it the hard way. With a fixed `--seed 42` and prefix caching on, re-running against a warm server made time to first token "improve" from 265 ms to 61 ms, purely because we had just sent it those same prompts. If you would rather keep caching on, vary the seed between runs instead. Either way, do not compare a cold run against a warm one. ### The memory side @@ -531,7 +536,7 @@ Look at the last row. Under tensor parallelism all four cards sat at **exactly 8 Every figure in that table is a rate or a latency rather than a total, which matters because the runs were not all the same size. We measured all three at `--num-prompts 128` first, then re-ran the winner at `--num-prompts 640` to be sure the result held up. It did: TP produced 507.09 tokens/sec at the larger scale against 503.68 at the smaller, and a median first-token latency of 3,211 ms against 3,233 ms. Both inside 1%, so the numbers above are the 640-prompt figures for TP and the 128-prompt figures for the other two, and the comparison is sound. The full output pasted above is from the 640-prompt run. -Tensor parallelism won nearly everything, and one gap deserves attention. At 32 concurrent requests it produced **70% more tokens per second than pipeline parallelism**. That is not a rounding error, it is a different class of performance, and it lines up exactly with the prefill and decode split above. Tensor parallelism has all four GPUs working on every token. Pipeline parallelism has each GPU working on a different request's stage, and with 32 requests spread over 4 stages there is not enough in flight to keep everyone busy, so cards sit idle waiting their turn. Its median time per token was 24% worse for the same reason. +Tensor parallelism won nearly everything, and one gap deserves attention. At 32 concurrent requests it produced **70% more tokens per second than pipeline parallelism**. That is not a rounding error, it is a different class of performance, and it lines up exactly with the prefill and decode split above. Tensor parallelism has all four GPUs working on every token. Pipeline parallelism has each GPU working on a different request's stage, and it only pays off when every stage takes the same time. Ours do not: the memory table above shows the four stages holding 84,283 to 87,899 MiB, because 94 layers do not divide evenly by 4 and the first stage carries the token embedding while the last carries the output head. Every uneven stage is a bubble the other three wait on, on every single token. Its median time per token was 24% worse for the same reason. **Pipeline parallelism did win one thing, and it is the one the theory predicts:** time to first token, by 15%. Processing a 1,024-token prompt is exactly where tensor parallelism's 188 all-reduces get expensive, because each one carries the whole prompt's worth of data rather than a single token's. Pipeline parallelism hands one activation tensor to the next stage and skips all of it. If your users judge you on how fast the first word appears, that is a real and measurable advantage. @@ -548,7 +553,7 @@ Median TTFT (ms): 265.56 A mean fifteen times the median means one request behaved completely differently from the other eleven. One request stalled for about 45 seconds, almost certainly a one-off kernel compilation on the first pass through a code path, and that single stall stretched the whole benchmark from 63 seconds to 107 seconds. Since throughput is just tokens divided by wall-clock, one stall wrecked the headline number. -That is why the speed table uses **median time per token** as its decode measurement rather than aggregate throughput. Median per-token latency does not care that one request had a bad start. +That is why the speed table carries **median time per token** alongside aggregate throughput rather than relying on throughput alone. Median per-token latency does not care that one request had a bad start, so when the two rows agree, as they do for all three configurations above, the throughput figure is trustworthy. When they disagree, believe the median and go looking for a stall. ### What we would actually run @@ -577,7 +582,7 @@ pydantic_core._pydantic_core.ValidationError: 1 validation error for VllmConfig **What it means:** the rule from Step 3. 64 heads cannot be shared out evenly among 3 GPUs. Good news, it fails in about a second, before loading a single byte of weights. -**The fix:** pick a `--tensor-parallel-size` that divides your head count. Powers of two are the safe habit. +**The fix:** pick a `--tensor-parallel-size` that divides **both** head counts, attention and key/value. Do not just reach for the next power of two: `-tp 8` divides our 64 attention heads but not our 4 KV heads, which is the case Step 3 warns about. ### "Failed to load model - not enough GPU memory" diff --git a/public/llms.txt b/public/llms.txt index 1d31fd7d6..8fe7eb2ae 100644 --- a/public/llms.txt +++ b/public/llms.txt @@ -36,7 +36,7 @@ Authoritative, practitioner-written, citation-friendly. Articles include code ex ## Recent posts (most recent 30 of 199) -- [Running a big LLM across multiple GPUs with vLLM](https://blog.kubesimplify.com/running-a-big-llm-across-multiple-gpus-with-vllm) (2026-08-18). A runbook for serving a model too big for one GPU: download to serving in eight steps, with every vLLM flag, startup log line and real error explained, plus tensor, pipeline and expert parallelism benchmarked head to head on a 235B model across four RTX PRO 6000 cards. +- [Running a big LLM across multiple GPUs with vLLM](https://blog.kubesimplify.com/running-a-big-llm-across-multiple-gpus-with-vllm) (2026-08-31). A runbook for serving a model too big for one GPU: download to serving in eight steps, with every vLLM flag, startup log line and real error explained, plus tensor, pipeline and expert parallelism benchmarked head to head on a 235B model across four RTX PRO 6000 cards. - [The Local LLM Glossary: Every Term, Flag, and Number in Plain English](https://blog.kubesimplify.com/local-llm-glossary) (2026-08-18). Plain-English definitions for every term you hit in local LLM posts: prefill and decode, tokens per second, FP8 and NVFP4, Q4_K_M, KV cache, YaRN, Gated DeltaNet, speculative decoding, and every vLLM, llama.cpp, and Ollama flag worth knowing. - [Running Qwen3.8-27B on DGX Spark](https://blog.kubesimplify.com/qwen3-8-27b-on-dgx-spark) (2026-08-17). Qwen3.8-27B on DGX Spark with llama.cpp, Ollama, vLLM, and SGLang: the recipes, the tokens per second I measured, MTP speculative decoding, and the sharp edges I hit along the way. - [I Ran an AI SRE Copilot on My Own Hardware. Here Is What It Actually Does.](https://blog.kubesimplify.com/nudgebee-ai-sre-copilot-hands-on) (2026-08-17). Running NudgeBee v1.4.0 end to end - a self-hosted AIOps platform behind AI-SRE, AI-FinOps, AI-K8sOps, and agentic automation - on a Mac, a kiac cluster, and a DGX Spark. diff --git a/public/rss.xml b/public/rss.xml index b796c8ee1..7e3754872 100644 --- a/public/rss.xml +++ b/public/rss.xml @@ -6,13 +6,13 @@ Deep dives on Kubernetes, AI infrastructure, GitOps, and the cloud-native stack, written by practitioners. en-us - Tue, 18 Aug 2026 10:00:00 GMT + Mon, 31 Aug 2026 10:00:00 GMT Kubesimplify static blog Running a big LLM across multiple GPUs with vLLM https://blog.kubesimplify.com/running-a-big-llm-across-multiple-gpus-with-vllm https://blog.kubesimplify.com/running-a-big-llm-across-multiple-gpus-with-vllm - Tue, 18 Aug 2026 10:00:00 GMT + Mon, 31 Aug 2026 10:00:00 GMT A runbook for serving a model too big for one GPU: download to serving in eight steps, with every vLLM flag, startup log line and real error explained, plus tensor, pipeline and expert parallelism benchmarked head to head on a 235B model across four RTX PRO 6000 cards. vllmgpunvidiallmplatform-engineering From 5f2a7ec2dd0606469a802624b65eeeb59c24ba85 Mon Sep 17 00:00:00 2001 From: Saiyam Pathak Date: Mon, 31 Aug 2026 17:17:39 +0530 Subject: [PATCH 13/19] Trim the deep mechanics back out, and say what the 2 in the KV formula is Review feedback from Shubham: a runbook should not stop to teach Megatron matrix partitioning, and the KV cache formula was unreadable. The formula `2 x layers x kv_heads x head_dim x bytes_per_number` has two different 2s in it and labelled neither, so the first one reads as "two blocks per layer, attention and mlp". It is not: it is one key and one value per token, which is where the KV in KV cache comes from. Every term now has a row saying what it is and which config field it comes from. Cut, because they were teaching rather than running: - The Megatron column-and-row derivation, which introduced "the first matrix" and "the second matrix" without ever saying what they were. What survives is the one fact Step 7 needs: an all-reduce twice per layer, 188 per token, over PCIe on this box, and the tax is what the benchmark measures. - The expert-routing deep dive. "235B-A22B" is already unpacked in The machine and the model, so the second pass was redundant. - The two-phase prefill and decode section with its own table, now one paragraph covering why the two metrics move in opposite directions. Also merged Step 3's six bullets into three: the analogy and the flag list were covering the same three modes twice. The measurement tables stay. They are evidence, not theory, and the verdict is unsupported without them. Co-Authored-By: Claude Opus 5 (1M context) --- ...-big-llm-across-multiple-gpus-with-vllm.md | 73 +++++-------------- public/atom.xml | 2 +- public/llms-full.txt | 73 +++++-------------- 3 files changed, 39 insertions(+), 109 deletions(-) diff --git a/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md b/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md index 4cc74de02..fc20e355f 100644 --- a/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md +++ b/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md @@ -183,7 +183,17 @@ Weights = parameters x bytes per parameter. For our model: 235 billion at 1 byte bytes per token = 2 x layers x kv_heads x head_dim x bytes_per_number ``` -For our model: `2 x 94 x 4 x 128 x 2 = 192,512 bytes`, call it 188 KiB per token. Sounds small, but this model supports a 262,144-token context, so one single full-length conversation would need about **47 GiB**. That is half a GPU for one user, and it is why "the weights fit, so I am fine" is wrong. It is also why `--max-model-len` exists, as you will see in the flags table. +That leading **2 is not the number of blocks in a layer**. It is there because every token leaves behind **two** things, a key and a value, which is where the "KV" in KV cache comes from. Term by term for our model: + +| Term | Value | Where it comes from | +| --- | --- | --- | +| 2 | 2 | one key **and** one value per token | +| `layers` | 94 | `num_hidden_layers` | +| `kv_heads` | 4 | `num_key_value_heads` | +| `head_dim` | 128 | `head_dim` | +| `bytes_per_number` | 2 | the cache is kept in BF16, so 2 bytes each | + +So `2 x 94 x 4 x 128 x 2 = 192,512 bytes`, call it 188 KiB per token. Sounds small, but this model supports a 262,144-token context, so one single full-length conversation would need about **47 GiB**. That is half a GPU for one user, and it is why "the weights fit, so I am fine" is wrong. It is also why `--max-model-len` exists, as you will see in the flags table. One piece of good news: under tensor parallelism the KV cache is **divided** across GPUs rather than duplicated, so 4 GPUs give you roughly 4x the conversation room on top of making the weights fit. @@ -191,17 +201,11 @@ One piece of good news: under tensor parallelism the KV cache is **divided** acr ## Step 3: Pick your split, then check it divides -vLLM gives you three ways to spread a model over GPUs, and they are genuinely different things. Mixing them up is the source of most confusion, so here is the analogy first. Imagine a restaurant kitchen that has to produce one dish: +vLLM gives you three ways to spread a model over GPUs, and they are genuinely different things. Picture a restaurant kitchen that has to produce one dish, and the flag falls out of the picture: -- **Tensor parallelism** is four chefs all working on the same dish at once, one chopping, one on sauce, one on protein, one plating. They constantly have to coordinate, but the dish is done fast. -- **Pipeline parallelism** is four chefs at four stations with the dish moving down the line. Station two cannot start until station one finishes. Very little talking, but three chefs are idle at any moment unless several dishes are in flight. -- **Expert parallelism** is a kitchen of 128 specialists where each dish needs only 8 of them. You spread the 128 across four rooms and walk each dish to whichever rooms hold the specialists it needs. - -In flags, and with the trade-off each one buys you: - -- **Tensor parallelism** (`--tensor-parallel-size`) slices every layer across all GPUs, so they all work on the same token at once. Best tokens per second, evenly split memory, divided KV cache. The default choice for GPUs inside one machine. This is what we run. -- **Pipeline parallelism** (`--pipeline-parallel-size`) gives each GPU a block of consecutive layers and passes the work along. The GPUs barely need to talk to each other, so it is the tool for spanning machines with a slow network, and it wins on time to first token, but GPUs spend time waiting their turn. -- **Expert parallelism** (`--enable-expert-parallel`) exists only for mixture-of-experts models and hands out whole experts instead of slicing them. Its job is trillion-parameter-scale clusters where even a tensor-parallel split cannot hold all the experts. It is **not** a memory saver at our scale, as our measurements in Step 7 show. +- **Tensor parallelism** (`--tensor-parallel-size`) is four chefs working on the same dish at once, one chopping, one on sauce, one on protein, one plating. They constantly coordinate, but the dish is done fast. It slices every layer across all GPUs: best tokens per second, evenly split memory, and the KV cache gets divided too. The default for GPUs inside one machine, and what we run. +- **Pipeline parallelism** (`--pipeline-parallel-size`) is four chefs at four stations with the dish moving down the line, where station two cannot start until station one finishes. Very little talking, so it is the tool for spanning machines with a slow network, and it wins on time to first token. But a station that runs slow leaves the others waiting. +- **Expert parallelism** (`--enable-expert-parallel`) is a kitchen of 128 specialists where each dish needs only 8, spread across four rooms. Mixture-of-experts models only. Its job is trillion-parameter clusters where even a tensor-parallel split cannot hold all the experts. It is **not** a memory saver at our scale, as Step 7 shows. {{multi-gpu-split-modes-animation}} @@ -213,31 +217,7 @@ That is the whole distinction. Pipeline parallelism cuts across the layers and e The drawing shows three cards because it is illustrating the two shapes, not our setup. Card counts are not free choices, which is exactly what the next check is about. -### What the tensor-parallel split costs: 188 all-reduces per token - -Tensor parallelism cuts **inside** every layer, and it is worth knowing what that costs before you commit to it, because it explains every result in Step 7. - -The work a layer does is mostly one big multiplication table, and multiplication tables can be cut up. The technique comes from NVIDIA's 2019 Megatron-LM paper. Cut the first matrix into vertical strips and each GPU can finish its columns alone. Cut the second into horizontal strips lining up with the first, and each GPU produces a **partial answer**, a quarter of the real result. - -Now, and only now, the GPUs have to talk. They add their four partial answers together so everyone ends up with the complete result. That operation is an **all-reduce**: everyone contributes a piece, everyone gets the total back. Megatron puts the cost plainly, saying the design runs a transformer layer "using only two all-reduces in the forward path". Generating text only uses the forward path, so: - -- 2 all-reduces per layer -- 94 layers -- **188 all-reduces to produce one single token** - -They happen strictly one after another, because layer 5 cannot start until layer 4 has compared notes. On our machine those 188 round trips cross PCIe rather than NVLink, which is the single fact that shapes every number in Step 7. - -{{multi-gpu-tensor-split-animation}} - -### Why this model is only 22B of work - -The third option needs one idea first, because it is also why this model runs far faster than 235B suggests. - -In an ordinary model every parameter is used for every token. In a **mixture-of-experts** model each layer holds many small networks called experts, and a tiny **router** picks which few each token visits. Ours has 128 experts per layer and the router picks 8. So the model holds 235B parameters in memory but only about 22B do any arithmetic for a given token. That is what "235B-A22B" means: you pay for the full 235B in memory and for only 22B in speed. - -{{moe-expert-routing-animation}} - -That is what gives you the third way to split. Instead of slicing every expert into strips, hand out whole experts: 128 experts over 4 GPUs is 32 intact experts each. Nothing needs adding up at the end, but tokens have to travel to whichever GPU owns the expert they were routed to, and the answers travel back. It also has a fairness problem, because the router does not promise to spread work evenly, so one GPU can end up holding the popular experts and everybody waits on it. +**One cost to know before you pick tensor parallelism.** Because every GPU holds only a sliver of each layer, none of them can finish a layer alone. They each compute a partial answer and then add those together so everyone has the full result. That operation is called an **all-reduce**, and it happens twice per layer, every layer, for every single token. On a 94-layer model that is 188 all-reduces to produce one token, and on our machine every one of them crosses PCIe rather than NVLink. That communication tax is what Step 7's numbers are really measuring. You do not need the details to run the thing, only to know the tax exists and that the interconnect sets its rate. **Then the ten-second pre-flight check.** You cannot pick any number for `--tensor-parallel-size`: because attention heads are handed out whole, your TP size must divide the model's head counts. Open `config.json`: @@ -396,22 +376,7 @@ Once it was running, we compared all three ways of splitting the same model over Two numbers do most of the talking. **Time to first token** is how long the user waits before anything appears, and **output tokens per second** is how fast the answer then streams. Every configuration trades one against the other, and one idea explains why. -### The two jobs hiding inside inference - -Inference is really two different jobs wearing one coat, and almost everything confusing about multi-GPU performance comes from this split. - -**Prefill reads your prompt.** All 1,024 tokens go through every layer at once, as one big batch. This is the phase that decides time to first token. It is *compute-heavy*: there is a lot of arithmetic and the matrix engines are the bottleneck. It also writes keys and values for all 1,024 tokens into the KV cache. - -**Decode writes the answer**, and it can only produce one token at a time. To write token 2 the model needs token 1, because it feeds its own output back in. That is what "autoregressive" means, and there is no way around it. Each pass produces exactly one token, reads the whole KV cache built so far, and appends one entry. Decode is *memory-heavy* rather than compute-heavy: for a single token there is barely any arithmetic, but the GPU still has to stream the weights and the entire cache past its compute units. - -| | Prefill | Decode | -| --- | --- | --- | -| Work per step | your whole prompt at once | exactly one token | -| Bottleneck | compute | memory bandwidth | -| Metric it drives | time to first token | time per output token | -| Data moved between GPUs | large, whole prompt's worth | tiny, one token's worth | - -That last row is the one to hold on to, and it is the whole explanation for the table below. Those 188 all-reduces from Step 3 are trivially cheap during decode, because each one carries a single token's worth of data. During prefill the same 188 all-reduces carry a thousand times more. So tensor parallelism pays its communication bill mostly at first-token time, and pipeline parallelism, which just hands one activation tensor to the next stage, skips it. That is the trade, and you are about to watch it happen. +The reason they trade is worth one paragraph. Answering a request is two jobs, not one. First the server reads your whole prompt in a single pass, which is where time to first token is decided, and then it writes the reply one token at a time, which is where tokens per second is decided. Those 188 exchanges per token from Step 3 are almost free while writing, because each one carries a single token's worth of data. While reading a 1,024-token prompt they carry a thousand times more. So tensor parallelism pays its communication bill mostly at first-token time, and pipeline parallelism, which just hands one result to the next GPU in line, largely skips it. That is the whole trade, and you are about to watch it happen. The benchmark is vLLM's own, 1024 tokens in and 256 tokens out per request, with `--ignore-eos` so every request generates exactly 256 tokens and the comparison is fair: @@ -535,9 +500,9 @@ Look at the last row. Under tensor parallelism all four cards sat at **exactly 8 Every figure in that table is a rate or a latency rather than a total, which matters because the runs were not all the same size. We measured all three at `--num-prompts 128` first, then re-ran the winner at `--num-prompts 640` to be sure the result held up. It did: TP produced 507.09 tokens/sec at the larger scale against 503.68 at the smaller, and a median first-token latency of 3,211 ms against 3,233 ms. Both inside 1%, so the numbers above are the 640-prompt figures for TP and the 128-prompt figures for the other two, and the comparison is sound. The full output pasted above is from the 640-prompt run. -Tensor parallelism won nearly everything, and one gap deserves attention. At 32 concurrent requests it produced **70% more tokens per second than pipeline parallelism**. That is not a rounding error, it is a different class of performance, and it lines up exactly with the prefill and decode split above. Tensor parallelism has all four GPUs working on every token. Pipeline parallelism has each GPU working on a different request's stage, and it only pays off when every stage takes the same time. Ours do not: the memory table above shows the four stages holding 84,283 to 87,899 MiB, because 94 layers do not divide evenly by 4 and the first stage carries the token embedding while the last carries the output head. Every uneven stage is a bubble the other three wait on, on every single token. Its median time per token was 24% worse for the same reason. +Tensor parallelism won nearly everything, and one gap deserves attention. At 32 concurrent requests it produced **70% more tokens per second than pipeline parallelism**. That is not a rounding error, it is a different class of performance, and it lines up exactly with the reading-versus-writing split above. Tensor parallelism has all four GPUs working on every token. Pipeline parallelism has each GPU working on a different request's stage, and it only pays off when every stage takes the same time. Ours do not: the memory table above shows the four stages holding 84,283 to 87,899 MiB, because 94 layers do not divide evenly by 4 and the first stage carries the token embedding while the last carries the output head. Every uneven stage is a bubble the other three wait on, on every single token. Its median time per token was 24% worse for the same reason. -**Pipeline parallelism did win one thing, and it is the one the theory predicts:** time to first token, by 15%. Processing a 1,024-token prompt is exactly where tensor parallelism's 188 all-reduces get expensive, because each one carries the whole prompt's worth of data rather than a single token's. Pipeline parallelism hands one activation tensor to the next stage and skips all of it. If your users judge you on how fast the first word appears, that is a real and measurable advantage. +**Pipeline parallelism did win one thing, and it is the one the theory predicts:** time to first token, by 15%. Reading a 1,024-token prompt is exactly where tensor parallelism's 188 all-reduces get expensive, because each one carries the whole prompt's worth of data rather than a single token's. Pipeline parallelism hands one activation tensor to the next stage and skips all of it. If your users judge you on how fast the first word appears, that is a real and measurable advantage. Expert parallelism costing 7% is not a knock on the technique, and it is important not to over-read it. Expert parallelism exists to solve a problem we do not have: models so large that even a tensor-parallel split cannot hold all the experts, on clusters big enough that duplicating experts everywhere would be wasteful. With 4 GPUs and a model that already fits, we are asking it to do a job it was not designed for and paying an extra network hop per token for nothing. On a 32 or 64 GPU deployment of a trillion-parameter model the answer would very likely flip. diff --git a/public/atom.xml b/public/atom.xml index 65b8e7226..c0a43402e 100644 --- a/public/atom.xml +++ b/public/atom.xml @@ -5,7 +5,7 @@ https://blog.kubesimplify.com/ - 2026-08-31T06:11:37.818Z + 2026-08-31T11:47:01.470Z Kubesimplify hello@kubesimplify.com diff --git a/public/llms-full.txt b/public/llms-full.txt index 3178a1938..a619be13f 100644 --- a/public/llms-full.txt +++ b/public/llms-full.txt @@ -184,7 +184,17 @@ Weights = parameters x bytes per parameter. For our model: 235 billion at 1 byte bytes per token = 2 x layers x kv_heads x head_dim x bytes_per_number ``` -For our model: `2 x 94 x 4 x 128 x 2 = 192,512 bytes`, call it 188 KiB per token. Sounds small, but this model supports a 262,144-token context, so one single full-length conversation would need about **47 GiB**. That is half a GPU for one user, and it is why "the weights fit, so I am fine" is wrong. It is also why `--max-model-len` exists, as you will see in the flags table. +That leading **2 is not the number of blocks in a layer**. It is there because every token leaves behind **two** things, a key and a value, which is where the "KV" in KV cache comes from. Term by term for our model: + +| Term | Value | Where it comes from | +| --- | --- | --- | +| 2 | 2 | one key **and** one value per token | +| `layers` | 94 | `num_hidden_layers` | +| `kv_heads` | 4 | `num_key_value_heads` | +| `head_dim` | 128 | `head_dim` | +| `bytes_per_number` | 2 | the cache is kept in BF16, so 2 bytes each | + +So `2 x 94 x 4 x 128 x 2 = 192,512 bytes`, call it 188 KiB per token. Sounds small, but this model supports a 262,144-token context, so one single full-length conversation would need about **47 GiB**. That is half a GPU for one user, and it is why "the weights fit, so I am fine" is wrong. It is also why `--max-model-len` exists, as you will see in the flags table. One piece of good news: under tensor parallelism the KV cache is **divided** across GPUs rather than duplicated, so 4 GPUs give you roughly 4x the conversation room on top of making the weights fit. @@ -192,17 +202,11 @@ One piece of good news: under tensor parallelism the KV cache is **divided** acr ## Step 3: Pick your split, then check it divides -vLLM gives you three ways to spread a model over GPUs, and they are genuinely different things. Mixing them up is the source of most confusion, so here is the analogy first. Imagine a restaurant kitchen that has to produce one dish: +vLLM gives you three ways to spread a model over GPUs, and they are genuinely different things. Picture a restaurant kitchen that has to produce one dish, and the flag falls out of the picture: -- **Tensor parallelism** is four chefs all working on the same dish at once, one chopping, one on sauce, one on protein, one plating. They constantly have to coordinate, but the dish is done fast. -- **Pipeline parallelism** is four chefs at four stations with the dish moving down the line. Station two cannot start until station one finishes. Very little talking, but three chefs are idle at any moment unless several dishes are in flight. -- **Expert parallelism** is a kitchen of 128 specialists where each dish needs only 8 of them. You spread the 128 across four rooms and walk each dish to whichever rooms hold the specialists it needs. - -In flags, and with the trade-off each one buys you: - -- **Tensor parallelism** (`--tensor-parallel-size`) slices every layer across all GPUs, so they all work on the same token at once. Best tokens per second, evenly split memory, divided KV cache. The default choice for GPUs inside one machine. This is what we run. -- **Pipeline parallelism** (`--pipeline-parallel-size`) gives each GPU a block of consecutive layers and passes the work along. The GPUs barely need to talk to each other, so it is the tool for spanning machines with a slow network, and it wins on time to first token, but GPUs spend time waiting their turn. -- **Expert parallelism** (`--enable-expert-parallel`) exists only for mixture-of-experts models and hands out whole experts instead of slicing them. Its job is trillion-parameter-scale clusters where even a tensor-parallel split cannot hold all the experts. It is **not** a memory saver at our scale, as our measurements in Step 7 show. +- **Tensor parallelism** (`--tensor-parallel-size`) is four chefs working on the same dish at once, one chopping, one on sauce, one on protein, one plating. They constantly coordinate, but the dish is done fast. It slices every layer across all GPUs: best tokens per second, evenly split memory, and the KV cache gets divided too. The default for GPUs inside one machine, and what we run. +- **Pipeline parallelism** (`--pipeline-parallel-size`) is four chefs at four stations with the dish moving down the line, where station two cannot start until station one finishes. Very little talking, so it is the tool for spanning machines with a slow network, and it wins on time to first token. But a station that runs slow leaves the others waiting. +- **Expert parallelism** (`--enable-expert-parallel`) is a kitchen of 128 specialists where each dish needs only 8, spread across four rooms. Mixture-of-experts models only. Its job is trillion-parameter clusters where even a tensor-parallel split cannot hold all the experts. It is **not** a memory saver at our scale, as Step 7 shows. {{multi-gpu-split-modes-animation}} @@ -214,31 +218,7 @@ That is the whole distinction. Pipeline parallelism cuts across the layers and e The drawing shows three cards because it is illustrating the two shapes, not our setup. Card counts are not free choices, which is exactly what the next check is about. -### What the tensor-parallel split costs: 188 all-reduces per token - -Tensor parallelism cuts **inside** every layer, and it is worth knowing what that costs before you commit to it, because it explains every result in Step 7. - -The work a layer does is mostly one big multiplication table, and multiplication tables can be cut up. The technique comes from NVIDIA's 2019 Megatron-LM paper. Cut the first matrix into vertical strips and each GPU can finish its columns alone. Cut the second into horizontal strips lining up with the first, and each GPU produces a **partial answer**, a quarter of the real result. - -Now, and only now, the GPUs have to talk. They add their four partial answers together so everyone ends up with the complete result. That operation is an **all-reduce**: everyone contributes a piece, everyone gets the total back. Megatron puts the cost plainly, saying the design runs a transformer layer "using only two all-reduces in the forward path". Generating text only uses the forward path, so: - -- 2 all-reduces per layer -- 94 layers -- **188 all-reduces to produce one single token** - -They happen strictly one after another, because layer 5 cannot start until layer 4 has compared notes. On our machine those 188 round trips cross PCIe rather than NVLink, which is the single fact that shapes every number in Step 7. - -{{multi-gpu-tensor-split-animation}} - -### Why this model is only 22B of work - -The third option needs one idea first, because it is also why this model runs far faster than 235B suggests. - -In an ordinary model every parameter is used for every token. In a **mixture-of-experts** model each layer holds many small networks called experts, and a tiny **router** picks which few each token visits. Ours has 128 experts per layer and the router picks 8. So the model holds 235B parameters in memory but only about 22B do any arithmetic for a given token. That is what "235B-A22B" means: you pay for the full 235B in memory and for only 22B in speed. - -{{moe-expert-routing-animation}} - -That is what gives you the third way to split. Instead of slicing every expert into strips, hand out whole experts: 128 experts over 4 GPUs is 32 intact experts each. Nothing needs adding up at the end, but tokens have to travel to whichever GPU owns the expert they were routed to, and the answers travel back. It also has a fairness problem, because the router does not promise to spread work evenly, so one GPU can end up holding the popular experts and everybody waits on it. +**One cost to know before you pick tensor parallelism.** Because every GPU holds only a sliver of each layer, none of them can finish a layer alone. They each compute a partial answer and then add those together so everyone has the full result. That operation is called an **all-reduce**, and it happens twice per layer, every layer, for every single token. On a 94-layer model that is 188 all-reduces to produce one token, and on our machine every one of them crosses PCIe rather than NVLink. That communication tax is what Step 7's numbers are really measuring. You do not need the details to run the thing, only to know the tax exists and that the interconnect sets its rate. **Then the ten-second pre-flight check.** You cannot pick any number for `--tensor-parallel-size`: because attention heads are handed out whole, your TP size must divide the model's head counts. Open `config.json`: @@ -397,22 +377,7 @@ Once it was running, we compared all three ways of splitting the same model over Two numbers do most of the talking. **Time to first token** is how long the user waits before anything appears, and **output tokens per second** is how fast the answer then streams. Every configuration trades one against the other, and one idea explains why. -### The two jobs hiding inside inference - -Inference is really two different jobs wearing one coat, and almost everything confusing about multi-GPU performance comes from this split. - -**Prefill reads your prompt.** All 1,024 tokens go through every layer at once, as one big batch. This is the phase that decides time to first token. It is *compute-heavy*: there is a lot of arithmetic and the matrix engines are the bottleneck. It also writes keys and values for all 1,024 tokens into the KV cache. - -**Decode writes the answer**, and it can only produce one token at a time. To write token 2 the model needs token 1, because it feeds its own output back in. That is what "autoregressive" means, and there is no way around it. Each pass produces exactly one token, reads the whole KV cache built so far, and appends one entry. Decode is *memory-heavy* rather than compute-heavy: for a single token there is barely any arithmetic, but the GPU still has to stream the weights and the entire cache past its compute units. - -| | Prefill | Decode | -| --- | --- | --- | -| Work per step | your whole prompt at once | exactly one token | -| Bottleneck | compute | memory bandwidth | -| Metric it drives | time to first token | time per output token | -| Data moved between GPUs | large, whole prompt's worth | tiny, one token's worth | - -That last row is the one to hold on to, and it is the whole explanation for the table below. Those 188 all-reduces from Step 3 are trivially cheap during decode, because each one carries a single token's worth of data. During prefill the same 188 all-reduces carry a thousand times more. So tensor parallelism pays its communication bill mostly at first-token time, and pipeline parallelism, which just hands one activation tensor to the next stage, skips it. That is the trade, and you are about to watch it happen. +The reason they trade is worth one paragraph. Answering a request is two jobs, not one. First the server reads your whole prompt in a single pass, which is where time to first token is decided, and then it writes the reply one token at a time, which is where tokens per second is decided. Those 188 exchanges per token from Step 3 are almost free while writing, because each one carries a single token's worth of data. While reading a 1,024-token prompt they carry a thousand times more. So tensor parallelism pays its communication bill mostly at first-token time, and pipeline parallelism, which just hands one result to the next GPU in line, largely skips it. That is the whole trade, and you are about to watch it happen. The benchmark is vLLM's own, 1024 tokens in and 256 tokens out per request, with `--ignore-eos` so every request generates exactly 256 tokens and the comparison is fair: @@ -536,9 +501,9 @@ Look at the last row. Under tensor parallelism all four cards sat at **exactly 8 Every figure in that table is a rate or a latency rather than a total, which matters because the runs were not all the same size. We measured all three at `--num-prompts 128` first, then re-ran the winner at `--num-prompts 640` to be sure the result held up. It did: TP produced 507.09 tokens/sec at the larger scale against 503.68 at the smaller, and a median first-token latency of 3,211 ms against 3,233 ms. Both inside 1%, so the numbers above are the 640-prompt figures for TP and the 128-prompt figures for the other two, and the comparison is sound. The full output pasted above is from the 640-prompt run. -Tensor parallelism won nearly everything, and one gap deserves attention. At 32 concurrent requests it produced **70% more tokens per second than pipeline parallelism**. That is not a rounding error, it is a different class of performance, and it lines up exactly with the prefill and decode split above. Tensor parallelism has all four GPUs working on every token. Pipeline parallelism has each GPU working on a different request's stage, and it only pays off when every stage takes the same time. Ours do not: the memory table above shows the four stages holding 84,283 to 87,899 MiB, because 94 layers do not divide evenly by 4 and the first stage carries the token embedding while the last carries the output head. Every uneven stage is a bubble the other three wait on, on every single token. Its median time per token was 24% worse for the same reason. +Tensor parallelism won nearly everything, and one gap deserves attention. At 32 concurrent requests it produced **70% more tokens per second than pipeline parallelism**. That is not a rounding error, it is a different class of performance, and it lines up exactly with the reading-versus-writing split above. Tensor parallelism has all four GPUs working on every token. Pipeline parallelism has each GPU working on a different request's stage, and it only pays off when every stage takes the same time. Ours do not: the memory table above shows the four stages holding 84,283 to 87,899 MiB, because 94 layers do not divide evenly by 4 and the first stage carries the token embedding while the last carries the output head. Every uneven stage is a bubble the other three wait on, on every single token. Its median time per token was 24% worse for the same reason. -**Pipeline parallelism did win one thing, and it is the one the theory predicts:** time to first token, by 15%. Processing a 1,024-token prompt is exactly where tensor parallelism's 188 all-reduces get expensive, because each one carries the whole prompt's worth of data rather than a single token's. Pipeline parallelism hands one activation tensor to the next stage and skips all of it. If your users judge you on how fast the first word appears, that is a real and measurable advantage. +**Pipeline parallelism did win one thing, and it is the one the theory predicts:** time to first token, by 15%. Reading a 1,024-token prompt is exactly where tensor parallelism's 188 all-reduces get expensive, because each one carries the whole prompt's worth of data rather than a single token's. Pipeline parallelism hands one activation tensor to the next stage and skips all of it. If your users judge you on how fast the first word appears, that is a real and measurable advantage. Expert parallelism costing 7% is not a knock on the technique, and it is important not to over-read it. Expert parallelism exists to solve a problem we do not have: models so large that even a tensor-parallel split cannot hold all the experts, on clusters big enough that duplicating experts everywhere would be wasteful. With 4 GPUs and a model that already fits, we are asking it to do a job it was not designed for and paying an extra network hop per token for nothing. On a 32 or 64 GPU deployment of a trillion-parameter model the answer would very likely flip. From c87a2ee51770b3c94c6d4ba143ca9d520331e0c9 Mon Sep 17 00:00:00 2001 From: Saiyam Pathak Date: Mon, 31 Aug 2026 18:06:28 +0530 Subject: [PATCH 14/19] Put the cake illustration back, with the duplicated axis label removed Shubham asked for the original image back. It was never removed, it was redrawn as an SVG diagram, and the redraw was the wrong call: the reason to touch it at all was a single bad glyph, not the illustration. The left axis read 1, 2, 3, 4, 4, 5, 6, 7, 8, 9, 10, eleven labels for ten layers, with the extra 4 struck through, plus a faint smudge above the 10. Both are painted out against the surrounding background, so the illustration is otherwise byte-for-byte the original. Drops cake-layers.svg and scripts/gen-multi-gpu-cake-diagram.mjs, which generated the replacement nobody wanted. Both are recoverable from abb7e00 if the illustration ever needs a vector counterpart. Co-Authored-By: Claude Opus 5 (1M context) --- public/atom.xml | 2 +- .../cake-layers.png | Bin 116276 -> 391068 bytes .../cake-layers.svg | 114 ----------- scripts/gen-multi-gpu-cake-diagram.mjs | 177 ------------------ 4 files changed, 1 insertion(+), 292 deletions(-) delete mode 100644 public/img/blog/running-a-big-llm-across-multiple-gpus-with-vllm/cake-layers.svg delete mode 100644 scripts/gen-multi-gpu-cake-diagram.mjs diff --git a/public/atom.xml b/public/atom.xml index c0a43402e..00f1ac077 100644 --- a/public/atom.xml +++ b/public/atom.xml @@ -5,7 +5,7 @@ https://blog.kubesimplify.com/ - 2026-08-31T11:47:01.470Z + 2026-08-31T12:35:54.202Z Kubesimplify hello@kubesimplify.com diff --git a/public/img/blog/running-a-big-llm-across-multiple-gpus-with-vllm/cake-layers.png b/public/img/blog/running-a-big-llm-across-multiple-gpus-with-vllm/cake-layers.png index be9a95bdc199244781b3803d352432daf3069d57..8e7f7d3af718d4bcfb9d29feef4f7d39bea03b6d 100644 GIT binary patch literal 391068 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z%^L(jrs86Qxk#Ts<=3gKO8k)Zo=~eU5+KESq|I!T3AeVGw6)lyjRvZRK^p}(vo61Z zeZkDasM;Vl3FVBUIKs*)1>3&&1g?qgU^>sv&^0kJabl)q;>5&>?FL6_<-`ebZFjny zC3pBbL3f#$ISDh;)2C10=m{ioyn$v%Hn}aoDdeYk;SfteoBk~7KM~5cqU$MC7=3A runbook for serving a model too big for one GPU: download to serving in eight steps, with every vLLM flag, startup log line and real error explained, plus tensor, pipeline and expert parallelism benchmarked head to head on a 235B model across four RTX PRO 6000 cards. + A runbook for serving a model too big for one GPU: download to serving in seven steps, with every vLLM flag, startup log line and real error explained, plus tensor, pipeline and expert parallelism benchmarked head to head on a 235B model across four RTX PRO 6000 cards. diff --git a/public/llms-full.txt b/public/llms-full.txt index 5aefafcfd..102abed30 100644 --- a/public/llms-full.txt +++ b/public/llms-full.txt @@ -9,7 +9,7 @@ - Canonical: https://blog.kubesimplify.com/running-a-big-llm-across-multiple-gpus-with-vllm - Published: 2026-08-31 -- Summary: A runbook for serving a model too big for one GPU: download to serving in eight steps, with every vLLM flag, startup log line and real error explained, plus tensor, pipeline and expert parallelism benchmarked head to head on a 235B model across four RTX PRO 6000 cards. +- Summary: A runbook for serving a model too big for one GPU: download to serving in seven steps, with every vLLM flag, startup log line and real error explained, plus tensor, pipeline and expert parallelism benchmarked head to head on a 235B model across four RTX PRO 6000 cards. Sooner or later everyone running models locally hits the same wall. You find a model you want, you look at the download size, and it is bigger than the GPU you own. A 235B model needs roughly 236 GB just for its weights. The card we have holds 96 GB. A handful of current data-centre parts do carry more, but nothing on our machine does, and no amount of clever flags will make 236 GB squeeze into 96 GB. @@ -19,9 +19,9 @@ Let's answer that properly, with a real model on real hardware. ## What this post covers -This is the runbook. Eight steps, from downloading a 236 GB model to serving it across four GPUs, with every command, flag, startup log line and real error explained. It is written for the person with root on the box, and it assumes no prior knowledge of distributed computing: if you know what a GPU is and you have run a model locally once, you are qualified. +This is the runbook. Seven steps, from downloading a 236 GB model to serving it across four GPUs, with every command, flag, startup log line and real error explained. It is written for the person with root on the box, and it assumes no prior knowledge of distributed computing: if you know what a GPU is and you have run a model locally once, you are qualified. -The theory arrives where you need it to make a decision, not before. Step 3 explains what a tensor-parallel split actually costs, because that is where you pick one, and Step 7 explains why the three options trade against each other, because that is where you read the numbers. Nothing here is theory for its own sake. +The theory arrives where you need it to make a decision, not before. Step 3 explains what a tensor-parallel split actually costs, because that is where you pick one, and Step 6 explains why the three options trade against each other, because that is where you read the numbers. Nothing here is theory for its own sake. ## The machine and the model @@ -74,15 +74,11 @@ On our machine every pair of GPUs reports `SYS`, which means the traffic goes ac ## Step 1: Getting the model onto the machine -Before anything can be split across GPUs it has to be on disk, and with a model this size that is not a formality. It is the step that bit us hardest, so it goes first. +Before anything can be split across GPUs it has to be on disk, and with a model this size that is not a formality. -### Check your disk first, because this is a real production hazard +### Check your disk first -**On a shared machine, filling the disk can take down everything else on it.** This is the part we learned the hard way, and it is worth more than a footnote. Our test box also runs a Kubernetes inference platform. Kubernetes treats free disk as a managed resource called ephemeral-storage, and when free space fell below its eviction threshold, the kubelet did exactly what it is designed to do. - -It evicted pods to reclaim space, tainted the node so nothing new could schedule, and garbage-collected container images. Several of those images had been built locally and existed in no registry, so they could not simply be pulled again. - -Nothing about that is a Kubernetes bug, and nothing about it is specific to our setup. The lesson generalises: **before you download a quarter of a terabyte onto a machine, check what else lives on that disk and what will happen when it fills.** `df -h` before you start, and know your platform's eviction threshold, which is often far higher than "0 bytes free". If the machine is shared, keeping a couple of hundred gigabytes of headroom is not paranoia. +A quarter of a terabyte has to land somewhere. Run `df -h` before you start, and if the machine is shared, leave real headroom rather than just enough: platforms that manage disk as a resource start taking action well before the disk is actually full. ### The download @@ -120,7 +116,7 @@ Three of those matter to you: - **`model.safetensors.index.json`** is the master map. The weights are spread over 24 files, and this map says which file each piece lives in. When vLLM needs layer 62, it looks here, sees shard 17, and opens only that file. - **`config.json`** is the model's spec sheet: how many layers, how many heads, how many experts. It is a few kilobytes, and it decides almost everything in this post, including how many GPUs you can split across. -One more detail, because a crash in Step 8 depends on it. Because this model is FP8, each weight is a single byte, which cannot record very large and very small numbers accurately at the same time. The checkpoint works around that by cutting the weights into blocks of 128 by 128 numbers and giving each block one extra number, its **scale**, that the GPU multiplies back in to recover the real weight. These **block scales** ship alongside the weights, and the arrangement is declared in `config.json`: +One more detail, because a crash in Step 7 depends on it. Because this model is FP8, each weight is a single byte, which cannot record very large and very small numbers accurately at the same time. The checkpoint works around that by cutting the weights into blocks of 128 by 128 numbers and giving each block one extra number, its **scale**, that the GPU multiplies back in to recover the real weight. These **block scales** ship alongside the weights, and the arrangement is declared in `config.json`: ```json "quantization_config": { @@ -131,7 +127,7 @@ One more detail, because a crash in Step 8 depends on it. Because this model is } ``` -Remember those block scales. They are the reason for the most annoying crash we hit, down in Step 8. +Remember those block scales. They are the reason for the most annoying crash we hit, down in Step 7. Do not spend any time on the shard count itself. Ours uses a 10 GB cap, 23 shards of exactly 10.00 GB and a 24th holding the remaining 6.45 GB, and somewhere in the 5 to 10 GB range is the common choice across the Hub. The layout is fixed by whoever uploaded the model, there is no download flag to change it, and it makes no difference to serving: the weights are identical either way, and safetensors are memory-mapped so the loader reads the byte ranges it wants regardless of how they are grouped. Shard size is a distribution question, not an inference question. @@ -149,13 +145,15 @@ By default everything lands under `~/.cache/huggingface/hub`, in a layout that l The content lives once in `blobs/` under its hash, and `snapshots/` holds human-readable symlinks into it. That is why pulling two revisions of a model does not always double your disk usage, and it is also why `du` and `df` can disagree with your intuition. -The practical consequence for serving: mount `~/.cache/huggingface` into the container and point `HF_HOME` at it. Note that this is the **parent** of the `hub/` directory in the tree above, not `hub/` itself: the libraries append `hub/` themselves, so `HF_HOME=~/.cache/huggingface/hub` sends them looking in `hub/hub/` and they find nothing. That is exactly what the `-v` and `-e HF_HOME` flags in Step 5 are doing. Get it wrong and the container downloads its own 236 GB copy. +The practical consequence for serving: mount `~/.cache/huggingface` into the container and point `HF_HOME` at it. Note that this is the **parent** of the `hub/` directory in the tree above, not `hub/` itself: the libraries append `hub/` themselves, so `HF_HOME=~/.cache/huggingface/hub` sends them looking in `hub/hub/` and they find nothing. That is exactly what the `-v` and `-e HF_HOME` flags in Step 4 are doing. Get it wrong and the container downloads its own 236 GB copy. -One more thing about loading that surprises people. When you split the model over 4 GPUs, vLLM starts 4 separate processes, one per GPU, and **every one of them reads the whole download from disk**, keeping only the quarter it needs. +One more thing about loading that surprises people. When you split the model over 4 GPUs, vLLM starts 4 separate processes, one per GPU, and **every one of them reads the whole checkpoint**, keeping only the quarter it needs. vLLM's own docs say it plainly: with tensor parallelism, "each process will read the whole model and split it into chunks". -vLLM's own docs say it plainly: with tensor parallelism, "each process will read the whole model and split it into chunks". So at `-tp 4` the machine reads the 236 GB not once but four times, close to a full terabyte of disk reads before the server can answer anything. That is why a big model takes minutes to load even from a fast disk. +That is 4 x 236 GB of reads, but it is **not** 4 x 236 GB off the SSD, and the difference matters when you are sizing a machine. The operating system keeps recently read files in spare RAM, in what is called the page cache. The first worker to touch a shard pulls it from disk; the other three usually find it already in memory and never go near the drive. So what the SSD actually serves is closer to one pass than four, and the other three passes are memory-speed. -Our own `Model loading took` line, which you can see in Step 5, reported 48.5 seconds, and that was a flattering number: we had just downloaded the model, so most of it was still sitting in RAM where the operating system keeps recently used files. From a cold disk it takes much longer. +The catch is that this only holds while the model fits in the RAM you have spare. On a box with less free memory than the checkpoint, the early shards get evicted before the later workers ask for them, and you do start paying for real re-reads. + +Our own `Model loading took` line, which you can see in Step 4, reported 48.5 seconds, and that is the warm case: we had just finished downloading, so almost all of it was still in page cache. A genuinely cold first load, straight off the drive, takes considerably longer, and it is the number to plan restarts around. ## Step 2: Will it fit? The ten-minute check @@ -206,7 +204,7 @@ vLLM gives you three ways to spread a model over GPUs, and they are genuinely di - **Tensor parallelism** (`--tensor-parallel-size`) is four chefs working on the same dish at once, one chopping, one on sauce, one on protein, one plating. They constantly coordinate, but the dish is done fast. It slices every layer across all GPUs: best tokens per second, evenly split memory, and the KV cache gets divided too. The default for GPUs inside one machine, and what we run. - **Pipeline parallelism** (`--pipeline-parallel-size`) is four chefs at four stations with the dish moving down the line, where station two cannot start until station one finishes. Very little talking, so it is the tool for spanning machines with a slow network, and it wins on time to first token. But a station that runs slow leaves the others waiting. -- **Expert parallelism** (`--enable-expert-parallel`) is a kitchen of 128 specialists where each dish needs only 8, spread across four rooms. Mixture-of-experts models only. Its job is trillion-parameter clusters where even a tensor-parallel split cannot hold all the experts. It is **not** a memory saver at our scale, as Step 7 shows. +- **Expert parallelism** (`--enable-expert-parallel`) is a kitchen of 128 specialists where each dish needs only 8, spread across four rooms. Mixture-of-experts models only. Its job is trillion-parameter clusters where even a tensor-parallel split cannot hold all the experts. It is **not** a memory saver at our scale, as Step 6 shows. {{multi-gpu-split-modes-animation}} @@ -218,7 +216,7 @@ That is the whole distinction. Pipeline parallelism cuts across the layers and e The drawing shows three cards because it is illustrating the two shapes, not our setup. Card counts are not free choices, which is exactly what the next check is about. -**One cost to know before you pick tensor parallelism.** Because every GPU holds only a sliver of each layer, none of them can finish a layer alone. They each compute a partial answer and then add those together so everyone has the full result. That operation is called an **all-reduce**, and it happens twice per layer, every layer, for every single token. On a 94-layer model that is 188 all-reduces to produce one token, and on our machine every one of them crosses PCIe rather than NVLink. That communication tax is what Step 7's numbers are really measuring. You do not need the details to run the thing, only to know the tax exists and that the interconnect sets its rate. +**One cost to know before you pick tensor parallelism.** Because every GPU holds only a sliver of each layer, none of them can finish a layer alone. They each compute a partial answer and then add those together so everyone has the full result. That operation is called an **all-reduce**, and it happens twice per layer, every layer, for every single token. On a 94-layer model that is 188 all-reduces to produce one token, and on our machine every one of them crosses PCIe rather than NVLink. That communication tax is what Step 6's numbers are really measuring. You do not need the details to run the thing, only to know the tax exists and that the interconnect sets its rate. **Then the ten-second pre-flight check.** You cannot pick any number for `--tensor-parallel-size`: because attention heads are handed out whole, your TP size must divide the model's head counts. Open `config.json`: @@ -242,40 +240,10 @@ For our model: That is the real lesson: **the KV head count, not the parameter count, usually decides how wide you can go.** It is the first thing we check on any new model, and it takes ten seconds. -## Step 4: Every flag, explained - -Before the command, the vocabulary. Here is every flag we use and why it has the value it has. If you only remember one thing from the runbook, make it this table. - -| Flag | What it does | Why our value | -| ----------------------------------- | --------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------ | -| `--tensor-parallel-size 4` | How many GPUs to slice each layer across. Often shortened to `-tp`. | 236 GB of weights needs at least 3 cards of 95 GiB, and 4 divides the model's head counts cleanly. | -| `--pipeline-parallel-size 1` | How many groups to cut the layer stack into. Often `-pp`. | 1 means off. We measure a `-pp 4` build in Step 7. | -| `--enable-expert-parallel` | Hand out whole experts per GPU instead of slicing every expert. Mixture-of-experts models only. | Tested both ways in Step 7, the choice a big MoE forces on you. | -| `--gpu-memory-utilization 0.90` | The fraction of each GPU's memory vLLM is allowed to claim, for weights plus KV cache plus working space. | 0.90 leaves a little headroom. Push it to 0.95 for more cache, but leave room or startup fails. | -| `--max-model-len 32768` | The longest single request, prompt plus reply, in tokens. | The model supports 262,144, but that would eat 47 GiB of cache for one user. 32,768 is a sane serving value. | -| `--max-num-seqs 32` | How many requests may be in flight at once. | Caps how much KV cache can be demanded simultaneously. Lower it if you see requests being preempted. | -| `--served-model-name qwen3-235b` | The name clients use in the API. | Otherwise clients must send the full checkpoint path. | -| `--port 8000` | Port for the OpenAI-compatible API. | Convention. | -| `--distributed-executor-backend mp` | How the GPU worker processes are managed: `mp` for plain Python multiprocessing, `ray` for a Ray cluster. | All 4 GPUs are in one machine, so `mp` is the simpler choice. `ray` is for multiple machines. | -| `--enforce-eager` | Skips building optimised CUDA graphs at startup. | We do **not** use it. It saves memory and starts faster, but generation is slower. Reach for it only if you are out of memory. | -| `--kv-cache-dtype fp8` | Stores the conversation cache at 8 bits instead of 16, roughly halving cache memory. | We left it at the default so our cache numbers are easy to check by hand. It is a good lever if you need more concurrency. | - -Three rows above describe flags we measured but did not keep: `--pipeline-parallel-size` and `--enable-expert-parallel` are the alternatives benchmarked in Step 7, and `--distributed-executor-backend mp` is already vLLM's default for a single machine, so it is in the table for the day you need `ray` rather than because our command sets it. The command in Step 5 carries only what our final configuration needs, plus one environment variable: - -| Environment variable | Why you need it | -| --- | --- | -| `VLLM_USE_DEEP_GEMM=0` | Not optional on our hardware. Without it all four workers die during startup with `Unknown SF transformation`. Step 8 explains why, and it is specific to FP8 block-scaled weights on `sm_120` cards, so try without it first on anything else. | - -Two container flags matter just as much, and neither is a vLLM flag: - -| Docker flag | Why you need it | -| --------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `--ipc=host` | The GPU workers are separate processes that pass data through shared memory. Docker's default 64 MB of shared memory is far too small, and leaving this out gives you a confusing hang at startup. | -| `--gpus '"device=1,4,5,6"'` | Hands specific GPUs to the container. The nested quoting is fussy but required. Inside the container they are renumbered 0 to 3. | +## Step 4: The command, and every flag in it -## Step 5: The command, line by line +Here is the whole thing. Run this and you have a server; the rest of the step explains every piece of it. -Here is the whole thing. Every line is explained above, and we will walk the structure below it. ```bash root@utho-gpu-rtxpro6000-8-62383:~# docker run -d --name vllm-tp4 \ @@ -315,7 +283,7 @@ Reading it top to bottom: - `-e HF_HUB_OFFLINE=1` tells the Hugging Face library not to phone home. It uses the local copy, which also means startup does not fail if the network is down. - `vllm/vllm-openai:latest` is the image. Everything after it is passed to vLLM, because the image's entrypoint is already `vllm serve`. - The first argument after the image is the model. Everything after that is a vLLM flag from the table above. -- `-e VLLM_USE_DEEP_GEMM=0` is here because without it this exact model would not start on these exact GPUs. It is not a general recommendation, and Step 8 explains the crash it avoids. If you are on different hardware, try without it first. +- `-e VLLM_USE_DEEP_GEMM=0` is here because without it this exact model would not start on these exact GPUs. It is not a general recommendation, and Step 7 explains the crash it avoids. If you are on different hardware, try without it first. One thing worth knowing about that entrypoint: because it is already `vllm serve`, running `docker run ... vllm/vllm-openai:latest python3 -c "..."` does **not** work the way you expect. Your Python gets handed to `vllm serve` as arguments and you get a confusing parse error. To run something else inside the image, override it: @@ -331,7 +299,39 @@ can GPU 0 talk to GPU 1 directly: True That is a genuinely useful sanity check before you start a long model load, because it confirms the container can see the cards. Do read the second line carefully though: `can_device_access_peer` tells you peer-to-peer addressing is **possible**, not that it is fast. Ours returns `True` while `nvidia-smi topo -m` still reports `SYS` for that pair, because the transfer is permitted but it is going over PCIe and across sockets. The topology matrix is the one that tells you what performance to expect. -## Step 6: How to read the startup log +### Every flag, explained + +If you only remember one thing from the runbook, make it this table. + + +| Flag | What it does | Why our value | +| ----------------------------------- | --------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------ | +| `--tensor-parallel-size 4` | How many GPUs to slice each layer across. Often shortened to `-tp`. | 236 GB of weights needs at least 3 cards of 95 GiB, and 4 divides the model's head counts cleanly. | +| `--pipeline-parallel-size 1` | How many groups to cut the layer stack into. Often `-pp`. | 1 means off. We measure a `-pp 4` build in Step 6. | +| `--enable-expert-parallel` | Hand out whole experts per GPU instead of slicing every expert. Mixture-of-experts models only. | Tested both ways in Step 6, the choice a big MoE forces on you. | +| `--gpu-memory-utilization 0.90` | The fraction of each GPU's memory vLLM is allowed to claim, for weights plus KV cache plus working space. | 0.90 leaves a little headroom. Push it to 0.95 for more cache, but leave room or startup fails. | +| `--max-model-len 32768` | The longest single request, prompt plus reply, in tokens. | The model supports 262,144, but that would eat 47 GiB of cache for one user. 32,768 is a sane serving value. | +| `--max-num-seqs 32` | How many requests may be in flight at once. | Caps how much KV cache can be demanded simultaneously. Lower it if you see requests being preempted. | +| `--served-model-name qwen3-235b` | The name clients use in the API. | Otherwise clients must send the full checkpoint path. | +| `--port 8000` | Port for the OpenAI-compatible API. | Convention. | +| `--distributed-executor-backend mp` | How the GPU worker processes are managed: `mp` for plain Python multiprocessing, `ray` for a Ray cluster. | All 4 GPUs are in one machine, so `mp` is the simpler choice. `ray` is for multiple machines. | +| `--enforce-eager` | Skips building optimised CUDA graphs at startup. | We do **not** use it. It saves memory and starts faster, but generation is slower. Reach for it only if you are out of memory. | +| `--kv-cache-dtype fp8` | Stores the conversation cache at 8 bits instead of 16, roughly halving cache memory. | We left it at the default so our cache numbers are easy to check by hand. It is a good lever if you need more concurrency. | + +Three rows above describe flags we measured but did not keep: `--pipeline-parallel-size` and `--enable-expert-parallel` are the alternatives benchmarked in Step 6, and `--distributed-executor-backend mp` is already vLLM's default for a single machine, so it is in the table for the day you need `ray` rather than because our command sets it. The command above carries only what our final configuration needs, plus one environment variable: + +| Environment variable | Why you need it | +| --- | --- | +| `VLLM_USE_DEEP_GEMM=0` | Not optional on our hardware. Without it all four workers die during startup with `Unknown SF transformation`. Step 7 explains why, and it is specific to FP8 block-scaled weights on `sm_120` cards, so try without it first on anything else. | + +Two container flags matter just as much, and neither is a vLLM flag: + +| Docker flag | Why you need it | +| --------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `--ipc=host` | The GPU workers are separate processes that pass data through shared memory. Docker's default 64 MB of shared memory is far too small, and leaving this out gives you a confusing hang at startup. | +| `--gpus '"device=1,4,5,6"'` | Hands specific GPUs to the container. The nested quoting is fussy but required. Inside the container they are renumbered 0 to 3. | + +## Step 5: How to read the startup log The startup log is the best teaching tool in the whole stack, and almost nobody reads it. Four lines tell you everything about whether your configuration is sensible. @@ -343,7 +343,7 @@ The startup log is the best teaching tool in the whole stack, and almost nobody If you divide the full model size by your `--tensor-parallel-size` and get roughly this number, the split worked. If this number equals the **whole** model, something is wrong and you are not actually splitting. -**Line two, what is left for conversations.** On vLLM 0.27.1 this arrives inside the long `gpu_worker.py` line you can see in Step 5, phrased as: +**Line two, what is left for conversations.** On vLLM 0.27.1 this arrives inside the long `gpu_worker.py` line you can see in Step 4, phrased as: ``` Current kv cache memory in use is X GiB @@ -369,7 +369,7 @@ This is the one to show your capacity planner. If it says `2.05x`, then two user For our run the four figures came out as: `Model loading took 55.19 GiB` per worker, 27.85 GiB of kv cache in use, `GPU KV cache size: 621,392 tokens`, and a maximum concurrency of `18.96x` at 32k. Predicting that token count by hand, 27.85 GiB divided by 47 KiB per token per card, gives 621,337 against the 621,392 vLLM printed, which is the kind of agreement that tells you the mental model is right. -## Step 7: Benchmark it, and what we would run +## Step 6: Benchmark it, and what we would run Once it was running, we compared all three ways of splitting the same model over the same 4 GPUs: tensor parallelism on its own, tensor parallelism plus expert parallelism, and pure pipeline parallelism. Same hardware, same flags otherwise, same benchmark shape. @@ -493,7 +493,7 @@ We would reach for the other two in specific situations, not as general upgrades One more thing worth saying plainly, because it is the biggest caveat on every number above: **our GPUs have no NVLink.** Every one of those 188 all-reduces per token crosses PCIe and the link between CPU sockets. On a machine with NVLink the all-reduce gets dramatically cheaper, tensor parallelism's one weakness at first-token latency shrinks, and pipeline parallelism's single win would likely disappear. If you are reading this table to plan hardware, the interconnect is the variable to check first. -## Step 8: Errors you will actually hit +## Step 7: Errors you will actually hit Every one of these is a real message we collected while doing this, not a hypothetical. @@ -563,7 +563,7 @@ vllm serve: error: argument --compilation-config/-cc: Invalid JSON: expected val **What it means:** you ran `docker run ... vllm/vllm-openai:latest python3 -c "..."`, but the image's entrypoint is already `vllm serve`, so your Python source got handed to vLLM as a command-line argument. -**The fix:** `--entrypoint python3`, as shown in Step 5. +**The fix:** `--entrypoint python3`, as shown in Step 4. ### "No available shared memory broadcast block found in 60 seconds" @@ -577,7 +577,7 @@ That is the runbook complete: the model is serving, you know what every flag is Four things to carry out of this, all of them checks you can run in a minute. -**One.** Check your disk before you download, because this cost us more than any GPU problem did. A quarter of a terabyte of weights on a shared machine is not just a storage question, it is a question about everything else living on that disk. Ours was a Kubernetes node, free space crossed the kubelet's eviction threshold, and it evicted the platform's own pods and garbage-collected locally-built images that no registry could replace. `df -h` first, and leave real headroom. +**One.** Check your disk before you download. A quarter of a terabyte of weights on a shared machine is a question about everything else living on that disk, not just a storage question. `df -h` first, and leave real headroom. **Two.** Check `num_key_value_heads` in `config.json` before you plan your hardware. It, not the parameter count, is usually what limits how many GPUs you can split across cleanly. Ours is 4, which is exactly why we run at `-tp 4` and not `-tp 8`. @@ -589,7 +589,7 @@ And for a 235B mixture-of-experts model on four GPUs with no NVLink between them Try it on whatever you have. Two GPUs are enough to see every one of these steps in action, and the log lines mean the same thing whether you are running 4 GPUs or 40. If you hit something we did not cover, tell us and we will add it. -**And check your interconnect before you buy anything.** Every number in Step 7 was measured on GPUs with no NVLink between them, so all 188 all-reduces per token crossed PCIe. That single fact is why pipeline parallelism managed to win first-token latency at all. On a machine with NVLink we would expect that win to vanish. `nvidia-smi topo -m` tells you which world you are in, and it is the first command we run on any new box. +**And check your interconnect before you buy anything.** Every number in Step 6 was measured on GPUs with no NVLink between them, so all 188 all-reduces per token crossed PCIe. That single fact is why pipeline parallelism managed to win first-token latency at all. On a machine with NVLink we would expect that win to vanish. `nvidia-smi topo -m` tells you which world you are in, and it is the first command we run on any new box. ## Credits and references diff --git a/public/llms.txt b/public/llms.txt index 8fe7eb2ae..5723950ec 100644 --- a/public/llms.txt +++ b/public/llms.txt @@ -36,7 +36,7 @@ Authoritative, practitioner-written, citation-friendly. Articles include code ex ## Recent posts (most recent 30 of 199) -- [Running a big LLM across multiple GPUs with vLLM](https://blog.kubesimplify.com/running-a-big-llm-across-multiple-gpus-with-vllm) (2026-08-31). A runbook for serving a model too big for one GPU: download to serving in eight steps, with every vLLM flag, startup log line and real error explained, plus tensor, pipeline and expert parallelism benchmarked head to head on a 235B model across four RTX PRO 6000 cards. +- [Running a big LLM across multiple GPUs with vLLM](https://blog.kubesimplify.com/running-a-big-llm-across-multiple-gpus-with-vllm) (2026-08-31). A runbook for serving a model too big for one GPU: download to serving in seven steps, with every vLLM flag, startup log line and real error explained, plus tensor, pipeline and expert parallelism benchmarked head to head on a 235B model across four RTX PRO 6000 cards. - [The Local LLM Glossary: Every Term, Flag, and Number in Plain English](https://blog.kubesimplify.com/local-llm-glossary) (2026-08-18). Plain-English definitions for every term you hit in local LLM posts: prefill and decode, tokens per second, FP8 and NVFP4, Q4_K_M, KV cache, YaRN, Gated DeltaNet, speculative decoding, and every vLLM, llama.cpp, and Ollama flag worth knowing. - [Running Qwen3.8-27B on DGX Spark](https://blog.kubesimplify.com/qwen3-8-27b-on-dgx-spark) (2026-08-17). Qwen3.8-27B on DGX Spark with llama.cpp, Ollama, vLLM, and SGLang: the recipes, the tokens per second I measured, MTP speculative decoding, and the sharp edges I hit along the way. - [I Ran an AI SRE Copilot on My Own Hardware. Here Is What It Actually Does.](https://blog.kubesimplify.com/nudgebee-ai-sre-copilot-hands-on) (2026-08-17). Running NudgeBee v1.4.0 end to end - a self-hosted AIOps platform behind AI-SRE, AI-FinOps, AI-K8sOps, and agentic automation - on a Mac, a kiac cluster, and a DGX Spark. diff --git a/public/rss.xml b/public/rss.xml index 7e3754872..f63300f99 100644 --- a/public/rss.xml +++ b/public/rss.xml @@ -13,7 +13,7 @@ https://blog.kubesimplify.com/running-a-big-llm-across-multiple-gpus-with-vllm https://blog.kubesimplify.com/running-a-big-llm-across-multiple-gpus-with-vllm Mon, 31 Aug 2026 10:00:00 GMT - A runbook for serving a model too big for one GPU: download to serving in eight steps, with every vLLM flag, startup log line and real error explained, plus tensor, pipeline and expert parallelism benchmarked head to head on a 235B model across four RTX PRO 6000 cards. + A runbook for serving a model too big for one GPU: download to serving in seven steps, with every vLLM flag, startup log line and real error explained, plus tensor, pipeline and expert parallelism benchmarked head to head on a 235B model across four RTX PRO 6000 cards. vllmgpunvidiallmplatform-engineering From 7299f0799dbcce13aa56f3edbd008587e85535ec Mon Sep 17 00:00:00 2001 From: Shubham Katara Date: Tue, 1 Sep 2026 09:45:18 +0200 Subject: [PATCH 17/19] Remove docker explaination and reduce paragraph lengths --- ...-big-llm-across-multiple-gpus-with-vllm.md | 170 ++++++++++-------- 1 file changed, 100 insertions(+), 70 deletions(-) diff --git a/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md b/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md index c525a44cf..c2221cfd8 100644 --- a/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md +++ b/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md @@ -29,7 +29,6 @@ This is the runbook. Seven steps, from downloading a 236 GB model to serving it The theory arrives where you need it to make a decision, not before. Step 3 explains what a tensor-parallel split actually costs, because that is where you pick one, and Step 6 explains why the three options trade against each other, because that is where you read the numbers. Nothing here is theory for its own sake. - ## The machine and the model Numbers mean nothing without the hardware attached, so here it is once. @@ -42,29 +41,29 @@ One detail that matters more than it looks: these GPUs are **not** connected by root@utho-gpu-rtxpro6000-8-62383:~# nvidia-smi topo -m ``` -| Device | GPU0 | GPU1 | GPU2 | GPU3 | GPU4 | GPU5 | GPU6 | GPU7 | NIC0 | CPU Affinity | NUMA Affinity | -| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :--- | :---: | -| **GPU0** | X | SYS | SYS | SYS | SYS | SYS | SYS | SYS | SYS | 48-55,176-183 | 6 | -| **GPU1** | SYS | X | SYS | SYS | SYS | SYS | SYS | SYS | PHB | 32-39,160-167 | 4 | -| **GPU2** | SYS | SYS | X | SYS | SYS | SYS | SYS | SYS | SYS | 0-7,128-135 | 0 | -| **GPU3** | SYS | SYS | SYS | X | SYS | SYS | SYS | SYS | SYS | 16-23,144-151 | 2 | -| **GPU4** | SYS | SYS | SYS | SYS | X | SYS | SYS | SYS | SYS | 112-119,240-247 | 14 | -| **GPU5** | SYS | SYS | SYS | SYS | SYS | X | SYS | SYS | SYS | 96-103,224-231 | 12 | -| **GPU6** | SYS | SYS | SYS | SYS | SYS | SYS | X | SYS | SYS | 64-71,192-199 | 8 | -| **GPU7** | SYS | SYS | SYS | SYS | SYS | SYS | SYS | X | SYS | 80-87,208-215 | 10 | -| **NIC0** | SYS | PHB | SYS | SYS | SYS | SYS | SYS | SYS | X | | | +| Device | GPU0 | GPU1 | GPU2 | GPU3 | GPU4 | GPU5 | GPU6 | GPU7 | NIC0 | CPU Affinity | NUMA Affinity | +| :------- | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :-------------- | :-----------: | +| **GPU0** | X | SYS | SYS | SYS | SYS | SYS | SYS | SYS | SYS | 48-55,176-183 | 6 | +| **GPU1** | SYS | X | SYS | SYS | SYS | SYS | SYS | SYS | PHB | 32-39,160-167 | 4 | +| **GPU2** | SYS | SYS | X | SYS | SYS | SYS | SYS | SYS | SYS | 0-7,128-135 | 0 | +| **GPU3** | SYS | SYS | SYS | X | SYS | SYS | SYS | SYS | SYS | 16-23,144-151 | 2 | +| **GPU4** | SYS | SYS | SYS | SYS | X | SYS | SYS | SYS | SYS | 112-119,240-247 | 14 | +| **GPU5** | SYS | SYS | SYS | SYS | SYS | X | SYS | SYS | SYS | 96-103,224-231 | 12 | +| **GPU6** | SYS | SYS | SYS | SYS | SYS | SYS | X | SYS | SYS | 64-71,192-199 | 8 | +| **GPU7** | SYS | SYS | SYS | SYS | SYS | SYS | SYS | X | SYS | 80-87,208-215 | 10 | +| **NIC0** | SYS | PHB | SYS | SYS | SYS | SYS | SYS | SYS | X | | | The legend that command prints, trimmed to the codes that matter here: -| Symbol | Meaning | -| :--- | :--- | -| `X` | Self | -| `SYS` | Across PCIe **and** the interconnect between CPU sockets. The slowest option. | -| `NODE` | Across PCIe and the bridges inside one NUMA node | -| `PHB` | Across PCIe and a PCIe host bridge, typically the CPU | -| `PXB` | Across multiple PCIe bridges, without touching the host bridge | -| `PIX` | Across at most a single PCIe bridge. The fastest non-NVLink option. | -| `NV#` | Across a bonded set of `#` NVLinks | +| Symbol | Meaning | +| :----- | :---------------------------------------------------------------------------- | +| `X` | Self | +| `SYS` | Across PCIe **and** the interconnect between CPU sockets. The slowest option. | +| `NODE` | Across PCIe and the bridges inside one NUMA node | +| `PHB` | Across PCIe and a PCIe host bridge, typically the CPU | +| `PXB` | Across multiple PCIe bridges, without touching the host bridge | +| `PIX` | Across at most a single PCIe bridge. The fastest non-NVLink option. | +| `NV#` | Across a bonded set of `#` NVLinks | On our machine every pair of GPUs reports `SYS`, which means the traffic goes across PCIe and then across the link between the CPU sockets. If you had NVLink you would see `NV1`, `NV2` and so on instead. Keep this in mind, because it changes which splitting method is fastest. @@ -122,7 +121,9 @@ Three of those matter to you: - **`model.safetensors.index.json`** is the master map. The weights are spread over 24 files, and this map says which file each piece lives in. When vLLM needs layer 62, it looks here, sees shard 17, and opens only that file. - **`config.json`** is the model's spec sheet: how many layers, how many heads, how many experts. It is a few kilobytes, and it decides almost everything in this post, including how many GPUs you can split across. -One more detail, because a crash in Step 7 depends on it. Because this model is FP8, each weight is a single byte, which cannot record very large and very small numbers accurately at the same time. The checkpoint works around that by cutting the weights into blocks of 128 by 128 numbers and giving each block one extra number, its **scale**, that the GPU multiplies back in to recover the real weight. These **block scales** ship alongside the weights, and the arrangement is declared in `config.json`: +One more detail, because a crash in Step 7 depends on it. Because this model is FP8, each weight is a single byte, which cannot record very large and very small numbers accurately at the same time. + +The checkpoint works around that by cutting the weights into blocks of 128 by 128 numbers and giving each block one extra number, its **scale**, that the GPU multiplies back in to recover the real weight. These **block scales** ship alongside the weights, and the arrangement is declared in `config.json`: ```json "quantization_config": { @@ -135,7 +136,9 @@ One more detail, because a crash in Step 7 depends on it. Because this model is Remember those block scales. They are the reason for the most annoying crash we hit, down in Step 7. -Do not spend any time on the shard count itself. Ours uses a 10 GB cap, 23 shards of exactly 10.00 GB and a 24th holding the remaining 6.45 GB, and somewhere in the 5 to 10 GB range is the common choice across the Hub. The layout is fixed by whoever uploaded the model, there is no download flag to change it, and it makes no difference to serving: the weights are identical either way, and safetensors are memory-mapped so the loader reads the byte ranges it wants regardless of how they are grouped. Shard size is a distribution question, not an inference question. +Do not spend any time on the shard count itself. Ours uses a 10 GB cap, 23 shards of exactly 10.00 GB and a 24th holding the remaining 6.45 GB, and somewhere in the 5 to 10 GB range is the common choice across the Hub. + +The layout is fixed by whoever uploaded the model, there is no download flag to change it, and it makes no difference to serving: the weights are identical either way, and safetensors are memory-mapped so the loader reads the byte ranges it wants regardless of how they are grouped. Shard size is a distribution question, not an inference question. ### Where it gets stored @@ -190,13 +193,13 @@ bytes per token = 2 x layers x kv_heads x head_dim x bytes_per_number That leading **2 is not the number of blocks in a layer**. It is there because every token leaves behind **two** things, a key and a value, which is where the "KV" in KV cache comes from. Term by term for our model: -| Term | Value | Where it comes from | -| --- | --- | --- | -| 2 | 2 | one key **and** one value per token | -| `layers` | 94 | `num_hidden_layers` | -| `kv_heads` | 4 | `num_key_value_heads` | -| `head_dim` | 128 | `head_dim` | -| `bytes_per_number` | 2 | the cache is kept in BF16, so 2 bytes each | +| Term | Value | Where it comes from | +| ------------------ | ----- | ------------------------------------------ | +| 2 | 2 | one key **and** one value per token | +| `layers` | 94 | `num_hidden_layers` | +| `kv_heads` | 4 | `num_key_value_heads` | +| `head_dim` | 128 | `head_dim` | +| `bytes_per_number` | 2 | the cache is kept in BF16, so 2 bytes each | So `2 x 94 x 4 x 128 x 2 = 192,512 bytes`, call it 188 KiB per token. Sounds small, but this model supports a 262,144-token context, so one single full-length conversation would need about **47 GiB**. That is half a GPU for one user, and it is why "the weights fit, so I am fine" is wrong. It is also why `--max-model-len` exists, as you will see in the flags table. @@ -222,7 +225,9 @@ That is the whole distinction. Pipeline parallelism cuts across the layers and e The drawing shows three cards because it is illustrating the two shapes, not our setup. Card counts are not free choices, which is exactly what the next check is about. -**One cost to know before you pick tensor parallelism.** Because every GPU holds only a sliver of each layer, none of them can finish a layer alone. They each compute a partial answer and then add those together so everyone has the full result. That operation is called an **all-reduce**, and it happens twice per layer, every layer, for every single token. On a 94-layer model that is 188 all-reduces to produce one token, and on our machine every one of them crosses PCIe rather than NVLink. That communication tax is what Step 6's numbers are really measuring. You do not need the details to run the thing, only to know the tax exists and that the interconnect sets its rate. +**One cost to know before you pick tensor parallelism.** Because every GPU holds only a sliver of each layer, none of them can finish a layer alone. They each compute a partial answer and then add those together so everyone has the full result. That operation is called an **all-reduce**, and it happens twice per layer, every layer, for every single token. + +On a 94-layer model that is 188 all-reduces to produce one token, and on our machine every one of them crosses PCIe rather than NVLink. That communication tax is what Step 6's numbers are really measuring. You do not need the details to run the thing, only to know the tax exists and that the interconnect sets its rate. **Then the ten-second pre-flight check.** You cannot pick any number for `--tensor-parallel-size`: because attention heads are handed out whole, your TP size must divide the model's head counts. Open `config.json`: @@ -250,7 +255,6 @@ That is the real lesson: **the KV head count, not the parameter count, usually d Here is the whole thing. Run this and you have a server; the rest of the step explains every piece of it. - ```bash root@utho-gpu-rtxpro6000-8-62383:~# docker run -d --name vllm-tp4 \ --gpus '"device=1,4,5,6"' \ @@ -280,16 +284,10 @@ root@utho-gpu-rtxpro6000-8-62383:~# docker run -d --name vllm-tp4 \ ``` -Reading it top to bottom: +The two interesting environment variables here are: -- `docker run -d` starts the container in the background and prints its id. Drop the `-d` if you would rather watch the logs scroll past. -- `--name vllm-tp4` gives it a name so you can say `docker logs vllm-tp4` instead of copying an id. -- `-p 8000:8000` maps the container's port 8000 to the host's port 8000, so you can reach the API from outside. -- `-v /root/.cache/huggingface:/root/.cache/huggingface` shares your downloaded models with the container. Without it the container would download all 236 GB again. -- `-e HF_HUB_OFFLINE=1` tells the Hugging Face library not to phone home. It uses the local copy, which also means startup does not fail if the network is down. -- `vllm/vllm-openai:latest` is the image. Everything after it is passed to vLLM, because the image's entrypoint is already `vllm serve`. -- The first argument after the image is the model. Everything after that is a vLLM flag from the table above. -- `-e VLLM_USE_DEEP_GEMM=0` is here because without it this exact model would not start on these exact GPUs. It is not a general recommendation, and Step 7 explains the crash it avoids. If you are on different hardware, try without it first. +- `HF_HUB_OFFLINE=1` tells the Hugging Face library not to phone home. It uses the local copy, which also means startup does not fail if the network is down. +- `VLLM_USE_DEEP_GEMM=0` Not optional on our hardware. Without it all four workers die during startup with `Unknown SF transformation`. Step 7 explains why, and it is specific to FP8 block-scaled weights on `sm_120` cards, so try without it first on anything else. One thing worth knowing about that entrypoint: because it is already `vllm serve`, running `docker run ... vllm/vllm-openai:latest python3 -c "..."` does **not** work the way you expect. Your Python gets handed to `vllm serve` as arguments and you get a confusing parse error. To run something else inside the image, override it: @@ -303,18 +301,19 @@ GPUs visible: 2 can GPU 0 talk to GPU 1 directly: True ``` -That is a genuinely useful sanity check before you start a long model load, because it confirms the container can see the cards. Do read the second line carefully though: `can_device_access_peer` tells you peer-to-peer addressing is **possible**, not that it is fast. Ours returns `True` while `nvidia-smi topo -m` still reports `SYS` for that pair, because the transfer is permitted but it is going over PCIe and across sockets. The topology matrix is the one that tells you what performance to expect. +That is a genuinely useful sanity check before you start a long model load, because it confirms the container can see the cards. Do read the second line carefully though: `can_device_access_peer` tells you peer-to-peer addressing is **possible**, not that it is fast. + +Ours returns `True` while `nvidia-smi topo -m` still reports `SYS` for that pair, because the transfer is permitted but it is going over PCIe and across sockets. The topology matrix is the one that tells you what performance to expect. ### Every flag, explained If you only remember one thing from the runbook, make it this table. - | Flag | What it does | Why our value | | ----------------------------------- | --------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------ | | `--tensor-parallel-size 4` | How many GPUs to slice each layer across. Often shortened to `-tp`. | 236 GB of weights needs at least 3 cards of 95 GiB, and 4 divides the model's head counts cleanly. | -| `--pipeline-parallel-size 1` | How many groups to cut the layer stack into. Often `-pp`. | 1 means off. We measure a `-pp 4` build in Step 6. | -| `--enable-expert-parallel` | Hand out whole experts per GPU instead of slicing every expert. Mixture-of-experts models only. | Tested both ways in Step 6, the choice a big MoE forces on you. | +| `--pipeline-parallel-size 1` | How many groups to cut the layer stack into. Often `-pp`. | 1 means off. We measure a `-pp 4` build in Step 6. | +| `--enable-expert-parallel` | Hand out whole experts per GPU instead of slicing every expert. Mixture-of-experts models only. | Tested both ways in Step 6, the choice a big MoE forces on you. | | `--gpu-memory-utilization 0.90` | The fraction of each GPU's memory vLLM is allowed to claim, for weights plus KV cache plus working space. | 0.90 leaves a little headroom. Push it to 0.95 for more cache, but leave room or startup fails. | | `--max-model-len 32768` | The longest single request, prompt plus reply, in tokens. | The model supports 262,144, but that would eat 47 GiB of cache for one user. 32,768 is a sane serving value. | | `--max-num-seqs 32` | How many requests may be in flight at once. | Caps how much KV cache can be demanded simultaneously. Lower it if you see requests being preempted. | @@ -326,8 +325,8 @@ If you only remember one thing from the runbook, make it this table. Three rows above describe flags we measured but did not keep: `--pipeline-parallel-size` and `--enable-expert-parallel` are the alternatives benchmarked in Step 6, and `--distributed-executor-backend mp` is already vLLM's default for a single machine, so it is in the table for the day you need `ray` rather than because our command sets it. The command above carries only what our final configuration needs, plus one environment variable: -| Environment variable | Why you need it | -| --- | --- | +| Environment variable | Why you need it | +| ---------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `VLLM_USE_DEEP_GEMM=0` | Not optional on our hardware. Without it all four workers die during startup with `Unknown SF transformation`. Step 7 explains why, and it is specific to FP8 block-scaled weights on `sm_120` cards, so try without it first on anything else. | Two container flags matter just as much, and neither is a vLLM flag: @@ -373,7 +372,9 @@ Maximum concurrency for 32,768 tokens per request: N.NNx This is the one to show your capacity planner. If it says `2.05x`, then two users can each have a full-length 32k conversation, and a third will have to wait or be preempted. It is simply the previous line divided by `--max-model-len`. -For our run the four figures came out as: `Model loading took 55.19 GiB` per worker, 27.85 GiB of kv cache in use, `GPU KV cache size: 621,392 tokens`, and a maximum concurrency of `18.96x` at 32k. Predicting that token count by hand, 27.85 GiB divided by 47 KiB per token per card, gives 621,337 against the 621,392 vLLM printed, which is the kind of agreement that tells you the mental model is right. +For our run the four figures came out as: `Model loading took 55.19 GiB` per worker, 27.85 GiB of kv cache in use, `GPU KV cache size: 621,392 tokens`, and a maximum concurrency of `18.96x` at 32k. + +Predicting that token count by hand, 27.85 GiB divided by 47 KiB per token per card, gives 621,337 against the 621,392 vLLM printed, which is the kind of agreement that tells you the mental model is right. ## Step 6: Benchmark it, and what we would run @@ -381,9 +382,18 @@ Once it was running, we compared all three ways of splitting the same model over Two numbers do most of the talking. **Time to first token** is how long the user waits before anything appears, and **output tokens per second** is how fast the answer then streams. Every configuration trades one against the other, and one idea explains why. -The reason they trade is worth one paragraph. Answering a request is two jobs, not one. First the server reads your whole prompt in a single pass, which is where time to first token is decided, and then it writes the reply one token at a time, which is where tokens per second is decided. Those 188 exchanges per token from Step 3 are almost free while writing, because each one carries a single token's worth of data. While reading a 1,024-token prompt they carry a thousand times more. So tensor parallelism pays its communication bill mostly at first-token time, and pipeline parallelism, which just hands one result to the next GPU in line, largely skips it. That is the whole trade, and you are about to watch it happen. +The tradeoff between time to first token and tokens per second comes from the fact that answering a request involves two separate jobs: + +- First, the server reads your entire prompt in a single pass. This is where the time to first token is determined. +- Then, it writes the reply one token at a time, which is where tokens per second are measured. +- Those 188 exchanges per token (from Step 3) are almost free during output, since they only carry one token’s worth of data each time. +- During the initial prompt read (for example, a 1,024-token prompt), the same exchanges carry a thousand times more data. +- As a result: + - Tensor parallelism pays most of its communication cost at the beginning (first-token time). + - Pipeline parallelism, where each GPU simply passes the result to the next, largely avoids this cost. +- That’s the core tradeoff, and you’ll see it illustrated in the upcoming benchmarks. -The benchmark is vLLM's own, 1024 tokens in and 256 tokens out per request, with `--ignore-eos` so every request generates exactly 256 tokens and the comparison is fair: +The benchmark is vLLM's own, with tensor parallelism on, 1024 tokens in and 256 tokens out per request, with `--ignore-eos` so every request generates exactly 256 tokens and the comparison is fair: ```bash root@utho-gpu-rtxpro6000-8-62383:~# docker exec vllm-tp4 vllm bench serve \ @@ -435,7 +445,9 @@ Median TPOT (ms): 51.16 **Why 32 in-flight requests and not some other number?** Because 32 is the ceiling we gave the server ourselves: `--max-num-seqs 32` tells vLLM to work on at most 32 requests per step. Benchmarking at exactly that ceiling shows the server fully loaded, which is the number you actually want for capacity planning. -**And what happens if a 33rd request arrives?** Nothing dramatic, and that is worth knowing. It is not rejected and it does not error. It waits in a queue inside the server, and the moment one of the 32 running requests finishes, it takes the freed slot. So the cost of oversubscribing is waiting time, not failures: throughput stays flat because the server was already flat out, and the extra request simply sees a longer time to first token. +**And what happens if a 33rd request arrives?** Nothing dramatic, and that is worth knowing. It is not rejected and it does not error. It waits in a queue inside the server, and the moment one of the 32 running requests finishes, it takes the freed slot. + +So the cost of oversubscribing is waiting time, not failures: throughput stays flat because the server was already flat out, and the extra request simply sees a longer time to first token. One subtlety: 32 is not the only ceiling in play. The startup log said this configuration holds about 19 full-length 32k conversations in its KV cache, and our benchmark requests are short, so `--max-num-seqs` is the limit that binds here. With long conversations the cache fills first, and instead of queueing politely vLLM starts preempting: it evicts a running request's cache and recomputes it later. Which ceiling you hit first depends entirely on how long your requests are. @@ -445,13 +457,13 @@ That `--no-enable-prefix-caching` on the end is not decoration, and we learned i We ran the same pair of benchmarks against all three configurations. First, where the memory went: -| | TP=4 | TP=4 plus EP | PP=4 | -| --- | --- | --- | --- | -| Weights per GPU | 55.19 GiB | 55.19 GiB | 55.70 GiB | -| KV cache per GPU | 27.85 GiB | 27.96 GiB | 26.84 GiB | -| Total KV cache | 621,392 tokens | **623,696 tokens** | 555,680 tokens | -| Max concurrency at 32k | 18.96x | **19.03x** | 16.96x | -| GPU memory used | 88,211 MiB on all 4 | 88,209 MiB on all 4 | 84,283 / 87,899 / 87,899 / 84,507 | +| | TP=4 | TP=4 plus EP | PP=4 | +| ---------------------- | ------------------- | ------------------- | --------------------------------- | +| Weights per GPU | 55.19 GiB | 55.19 GiB | 55.70 GiB | +| KV cache per GPU | 27.85 GiB | 27.96 GiB | 26.84 GiB | +| Total KV cache | 621,392 tokens | **623,696 tokens** | 555,680 tokens | +| Max concurrency at 32k | 18.96x | **19.03x** | 16.96x | +| GPU memory used | 88,211 MiB on all 4 | 88,209 MiB on all 4 | 84,283 / 87,899 / 87,899 / 84,507 | **Expert parallelism did not save memory.** It moved 0.37% of extra room into the cache, which is noise. If you were hoping expert parallelism would let you fit a model that otherwise does not fit, this is your warning that it will not. @@ -461,19 +473,31 @@ Look at the last row. Under tensor parallelism all four cards sat at **exactly 8 ### The speed side -| Measurement | TP=4 | TP=4 plus EP | PP=4 | Winner | -| --- | --- | --- | --- | --- | -| Median time per token, 1 request | **17.14 ms** | 18.83 ms | 21.19 ms | TP | -| Output tokens/sec, 32 requests | **507.09** | 470.93 | 296.48 | TP, by 70% over PP | -| Median time to first token, 32 requests | 3,211 ms | 3,705 ms | **2,735 ms** | PP, by 15% | +| Measurement | TP=4 | TP=4 plus EP | PP=4 | Winner | +| --------------------------------------- | ------------ | ------------ | ------------ | ------------------ | +| Median time per token, 1 request | **17.14 ms** | 18.83 ms | 21.19 ms | TP | +| Output tokens/sec, 32 requests | **507.09** | 470.93 | 296.48 | TP, by 70% over PP | +| Median time to first token, 32 requests | 3,211 ms | 3,705 ms | **2,735 ms** | PP, by 15% | + +Every figure in that table is a rate or a latency rather than a total, which matters because the runs were not all the same size. We measured all three at `--num-prompts 128` first, then re-ran the winner at `--num-prompts 640` to be sure the result held up. + +It did: TP produced 507.09 tokens/sec at the larger scale against 503.68 at the smaller, and a median first-token latency of 3,211 ms against 3,233 ms. Both inside 1%, so the numbers above are the 640-prompt figures for TP and the 128-prompt figures for the other two, and the comparison is sound. The full output pasted above is from the 640-prompt run. + +Tensor parallelism won nearly everything, and one gap deserves attention. At 32 concurrent requests it produced **70% more tokens per second than pipeline parallelism**. That is not a rounding error, it is a different class of performance, and it lines up exactly with the reading-versus-writing split above. + +- Tensor parallelism has all four GPUs working on every token. +- Pipeline parallelism has each GPU working on a different request's stage, and it only pays off when every stage takes the same time. -Every figure in that table is a rate or a latency rather than a total, which matters because the runs were not all the same size. We measured all three at `--num-prompts 128` first, then re-ran the winner at `--num-prompts 640` to be sure the result held up. It did: TP produced 507.09 tokens/sec at the larger scale against 503.68 at the smaller, and a median first-token latency of 3,211 ms against 3,233 ms. Both inside 1%, so the numbers above are the 640-prompt figures for TP and the 128-prompt figures for the other two, and the comparison is sound. The full output pasted above is from the 640-prompt run. +Ours do not: the memory table above shows the four stages holding 84,283 to 87,899 MiB, because 94 layers do not divide evenly by 4 and the first stage carries the token embedding while the last carries the output head. +Every uneven stage is a bubble the other three wait on, on every single token. Its median time per token was 24% worse for the same reason. -Tensor parallelism won nearly everything, and one gap deserves attention. At 32 concurrent requests it produced **70% more tokens per second than pipeline parallelism**. That is not a rounding error, it is a different class of performance, and it lines up exactly with the reading-versus-writing split above. Tensor parallelism has all four GPUs working on every token. Pipeline parallelism has each GPU working on a different request's stage, and it only pays off when every stage takes the same time. Ours do not: the memory table above shows the four stages holding 84,283 to 87,899 MiB, because 94 layers do not divide evenly by 4 and the first stage carries the token embedding while the last carries the output head. Every uneven stage is a bubble the other three wait on, on every single token. Its median time per token was 24% worse for the same reason. +**Pipeline parallelism did win one thing, and it is the one the theory predicts:** time to first token, by 15%. Reading a 1,024-token prompt is exactly where tensor parallelism's 188 all-reduces get expensive, because each one carries the whole prompt's worth of data rather than a single token's. -**Pipeline parallelism did win one thing, and it is the one the theory predicts:** time to first token, by 15%. Reading a 1,024-token prompt is exactly where tensor parallelism's 188 all-reduces get expensive, because each one carries the whole prompt's worth of data rather than a single token's. Pipeline parallelism hands one activation tensor to the next stage and skips all of it. If your users judge you on how fast the first word appears, that is a real and measurable advantage. +Pipeline parallelism hands one activation tensor to the next stage and skips all of it. If your users judge you on how fast the first word appears, that is a real and measurable advantage. -Expert parallelism costing 7% is not a knock on the technique, and it is important not to over-read it. Expert parallelism exists to solve a problem we do not have: models so large that even a tensor-parallel split cannot hold all the experts, on clusters big enough that duplicating experts everywhere would be wasteful. With 4 GPUs and a model that already fits, we are asking it to do a job it was not designed for and paying an extra network hop per token for nothing. On a 32 or 64 GPU deployment of a trillion-parameter model the answer would very likely flip. +Expert parallelism costing 7% is not a knock on the technique, and it is important not to over-read it. Expert parallelism exists to solve a problem we do not have: models so large that even a tensor-parallel split cannot hold all the experts, on clusters big enough that duplicating experts everywhere would be wasteful. + +With 4 GPUs and a model that already fits, we are asking it to do a job it was not designed for and paying an extra network hop per token for nothing. On a 32 or 64 GPU deployment of a trillion-parameter model the answer would very likely flip. ### One number we threw away, and why @@ -497,7 +521,9 @@ We would reach for the other two in specific situations, not as general upgrades - **Pipeline parallelism** if time to first token is the metric you are judged on, or if you are spanning multiple machines where the network between them is genuinely slow. It was 15% better at first-token latency and it barely touches the interconnect. - **Expert parallelism** when the model is so large that even a tensor-parallel split cannot hold all the experts. That is a real problem at trillion-parameter scale and simply is not our problem at 235B on 4 cards. -One more thing worth saying plainly, because it is the biggest caveat on every number above: **our GPUs have no NVLink.** Every one of those 188 all-reduces per token crosses PCIe and the link between CPU sockets. On a machine with NVLink the all-reduce gets dramatically cheaper, tensor parallelism's one weakness at first-token latency shrinks, and pipeline parallelism's single win would likely disappear. If you are reading this table to plan hardware, the interconnect is the variable to check first. +One more thing worth saying plainly, because it is the biggest caveat on every number above: **our GPUs have no NVLink.** Every one of those 188 all-reduces per token crosses PCIe and the link between CPU sockets. + +On a machine with NVLink the all-reduce gets dramatically cheaper, tensor parallelism's one weakness at first-token latency shrinks, and pipeline parallelism's single win would likely disappear. If you are reading this table to plan hardware, the interconnect is the variable to check first. ## Step 7: Errors you will actually hit @@ -547,7 +573,9 @@ RuntimeError: Assertion error (/workspace/.deps/deepgemm-src/csrc/apis/layout.hp Unknown SF transformation ``` -**What it means:** this model stores its FP8 weights in blocks, with a separate scale factor per 128x128 block, which you can see in its config as `"weight_block_size": [128, 128]`. vLLM hands that kind of matrix multiplication to a library called DeepGEMM, and DeepGEMM did not know how to lay out those scale factors ("SF" is scale factor) on our particular GPU. The RTX PRO 6000 is Blackwell, but it reports as `sm_120`, which is not the same silicon target as the data-centre Blackwell parts that DeepGEMM is usually exercised on. +**What it means:** this model stores its FP8 weights in blocks, with a separate scale factor per 128x128 block, which you can see in its config as `"weight_block_size": [128, 128]`. vLLM hands that kind of matrix multiplication to a library called DeepGEMM, and DeepGEMM did not know how to lay out those scale factors ("SF" is scale factor) on our particular GPU. + +The RTX PRO 6000 is Blackwell, but it reports as `sm_120`, which is not the same silicon target as the data-centre Blackwell parts that DeepGEMM is usually exercised on. Notice how unhelpful the message is if you do not know that background. Nothing in it mentions FP8, quantization, or your GPU. @@ -595,7 +623,9 @@ And for a 235B mixture-of-experts model on four GPUs with no NVLink between them Try it on whatever you have. Two GPUs are enough to see every one of these steps in action, and the log lines mean the same thing whether you are running 4 GPUs or 40. If you hit something we did not cover, tell us and we will add it. -**And check your interconnect before you buy anything.** Every number in Step 6 was measured on GPUs with no NVLink between them, so all 188 all-reduces per token crossed PCIe. That single fact is why pipeline parallelism managed to win first-token latency at all. On a machine with NVLink we would expect that win to vanish. `nvidia-smi topo -m` tells you which world you are in, and it is the first command we run on any new box. +**And check your interconnect before you buy anything.** Every number in Step 6 was measured on GPUs with no NVLink between them, so all 188 all-reduces per token crossed PCIe. + +That single fact is why pipeline parallelism managed to win first-token latency at all. On a machine with NVLink we would expect that win to vanish. `nvidia-smi topo -m` tells you which world you are in, and it is the first command we run on any new box. ## Credits and references From fe68ffe912fd72435d2f9183c0d6f3eef7850b5b Mon Sep 17 00:00:00 2001 From: Saiyam Pathak Date: Tue, 1 Sep 2026 15:24:26 +0530 Subject: [PATCH 18/19] Drop --no-enable-prefix-caching from the benchmark command; it is not a bench flag Verified on the box against vLLM 0.27.1: vllm: error: unrecognized arguments: --no-enable-prefix-caching It is a `vllm serve` flag, not a `vllm bench serve` one, so the command as published errored out for anyone who copied it. I added it in an earlier pass to close the warm-cache trap, without checking which side of the CLI it belonged to. The trap is real, so the note stays and now says how to actually avoid it: vary the seed, or restart the server with caching off. It also notes that every figure in the table came from a freshly started server, which is why the published numbers are not cache-inflated. Co-Authored-By: Claude Opus 5 (1M context) --- ...-big-llm-across-multiple-gpus-with-vllm.md | 5 +- public/atom.xml | 2 +- public/llms-full.txt | 175 ++++++++++-------- 3 files changed, 105 insertions(+), 77 deletions(-) diff --git a/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md b/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md index c2221cfd8..b9f857b99 100644 --- a/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md +++ b/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md @@ -401,8 +401,7 @@ root@utho-gpu-rtxpro6000-8-62383:~# docker exec vllm-tp4 vllm bench serve \ --served-model-name qwen3-235b \ --base-url http://localhost:8000 \ --dataset-name random --random-input-len 1024 --random-output-len 256 \ - --max-concurrency 1 --num-prompts 12 --seed 42 --ignore-eos \ - --no-enable-prefix-caching + --max-concurrency 1 --num-prompts 12 --seed 42 --ignore-eos Maximum request concurrency: 1 100%|██████████| 12/12 [00:55<00:00, 4.62s/it] @@ -451,7 +450,7 @@ So the cost of oversubscribing is waiting time, not failures: throughput stays f One subtlety: 32 is not the only ceiling in play. The startup log said this configuration holds about 19 full-length 32k conversations in its KV cache, and our benchmark requests are short, so `--max-num-seqs` is the limit that binds here. With long conversations the cache fills first, and instead of queueing politely vLLM starts preempting: it evicts a running request's cache and recomputes it later. Which ceiling you hit first depends entirely on how long your requests are. -That `--no-enable-prefix-caching` on the end is not decoration, and we learned it the hard way. With a fixed `--seed 42` and prefix caching on, re-running against a warm server made time to first token "improve" from 265 ms to 61 ms, purely because we had just sent it those same prompts. If you would rather keep caching on, vary the seed between runs instead. Either way, do not compare a cold run against a warm one. +One trap to know before you re-run this. vLLM caches prompt prefixes by default, so firing the same `--seed 42` prompts at a server that has already seen them measures the cache, not the model: ours reported time to first token "improving" from 265 ms to 61 ms that way, which was meaningless. Note that `--no-enable-prefix-caching` is a `vllm serve` flag, not a `vllm bench serve` one, so you cannot switch it off from the benchmark side. Either vary `--seed` between runs, or restart the server with caching disabled. Every figure below comes from a freshly started server, so each configuration began with an empty cache. ### The memory side diff --git a/public/atom.xml b/public/atom.xml index 01e3830a6..8182b454c 100644 --- a/public/atom.xml +++ b/public/atom.xml @@ -5,7 +5,7 @@ https://blog.kubesimplify.com/ - 2026-08-31T17:36:45.657Z + 2026-09-01T09:53:53.388Z Kubesimplify hello@kubesimplify.com diff --git a/public/llms-full.txt b/public/llms-full.txt index 102abed30..c617475f5 100644 --- a/public/llms-full.txt +++ b/public/llms-full.txt @@ -23,7 +23,6 @@ This is the runbook. Seven steps, from downloading a 236 GB model to serving it The theory arrives where you need it to make a decision, not before. Step 3 explains what a tensor-parallel split actually costs, because that is where you pick one, and Step 6 explains why the three options trade against each other, because that is where you read the numbers. Nothing here is theory for its own sake. - ## The machine and the model Numbers mean nothing without the hardware attached, so here it is once. @@ -36,29 +35,29 @@ One detail that matters more than it looks: these GPUs are **not** connected by root@utho-gpu-rtxpro6000-8-62383:~# nvidia-smi topo -m ``` -| Device | GPU0 | GPU1 | GPU2 | GPU3 | GPU4 | GPU5 | GPU6 | GPU7 | NIC0 | CPU Affinity | NUMA Affinity | -| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :--- | :---: | -| **GPU0** | X | SYS | SYS | SYS | SYS | SYS | SYS | SYS | SYS | 48-55,176-183 | 6 | -| **GPU1** | SYS | X | SYS | SYS | SYS | SYS | SYS | SYS | PHB | 32-39,160-167 | 4 | -| **GPU2** | SYS | SYS | X | SYS | SYS | SYS | SYS | SYS | SYS | 0-7,128-135 | 0 | -| **GPU3** | SYS | SYS | SYS | X | SYS | SYS | SYS | SYS | SYS | 16-23,144-151 | 2 | -| **GPU4** | SYS | SYS | SYS | SYS | X | SYS | SYS | SYS | SYS | 112-119,240-247 | 14 | -| **GPU5** | SYS | SYS | SYS | SYS | SYS | X | SYS | SYS | SYS | 96-103,224-231 | 12 | -| **GPU6** | SYS | SYS | SYS | SYS | SYS | SYS | X | SYS | SYS | 64-71,192-199 | 8 | -| **GPU7** | SYS | SYS | SYS | SYS | SYS | SYS | SYS | X | SYS | 80-87,208-215 | 10 | -| **NIC0** | SYS | PHB | SYS | SYS | SYS | SYS | SYS | SYS | X | | | +| Device | GPU0 | GPU1 | GPU2 | GPU3 | GPU4 | GPU5 | GPU6 | GPU7 | NIC0 | CPU Affinity | NUMA Affinity | +| :------- | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :--: | :-------------- | :-----------: | +| **GPU0** | X | SYS | SYS | SYS | SYS | SYS | SYS | SYS | SYS | 48-55,176-183 | 6 | +| **GPU1** | SYS | X | SYS | SYS | SYS | SYS | SYS | SYS | PHB | 32-39,160-167 | 4 | +| **GPU2** | SYS | SYS | X | SYS | SYS | SYS | SYS | SYS | SYS | 0-7,128-135 | 0 | +| **GPU3** | SYS | SYS | SYS | X | SYS | SYS | SYS | SYS | SYS | 16-23,144-151 | 2 | +| **GPU4** | SYS | SYS | SYS | SYS | X | SYS | SYS | SYS | SYS | 112-119,240-247 | 14 | +| **GPU5** | SYS | SYS | SYS | SYS | SYS | X | SYS | SYS | SYS | 96-103,224-231 | 12 | +| **GPU6** | SYS | SYS | SYS | SYS | SYS | SYS | X | SYS | SYS | 64-71,192-199 | 8 | +| **GPU7** | SYS | SYS | SYS | SYS | SYS | SYS | SYS | X | SYS | 80-87,208-215 | 10 | +| **NIC0** | SYS | PHB | SYS | SYS | SYS | SYS | SYS | SYS | X | | | The legend that command prints, trimmed to the codes that matter here: -| Symbol | Meaning | -| :--- | :--- | -| `X` | Self | -| `SYS` | Across PCIe **and** the interconnect between CPU sockets. The slowest option. | -| `NODE` | Across PCIe and the bridges inside one NUMA node | -| `PHB` | Across PCIe and a PCIe host bridge, typically the CPU | -| `PXB` | Across multiple PCIe bridges, without touching the host bridge | -| `PIX` | Across at most a single PCIe bridge. The fastest non-NVLink option. | -| `NV#` | Across a bonded set of `#` NVLinks | +| Symbol | Meaning | +| :----- | :---------------------------------------------------------------------------- | +| `X` | Self | +| `SYS` | Across PCIe **and** the interconnect between CPU sockets. The slowest option. | +| `NODE` | Across PCIe and the bridges inside one NUMA node | +| `PHB` | Across PCIe and a PCIe host bridge, typically the CPU | +| `PXB` | Across multiple PCIe bridges, without touching the host bridge | +| `PIX` | Across at most a single PCIe bridge. The fastest non-NVLink option. | +| `NV#` | Across a bonded set of `#` NVLinks | On our machine every pair of GPUs reports `SYS`, which means the traffic goes across PCIe and then across the link between the CPU sockets. If you had NVLink you would see `NV1`, `NV2` and so on instead. Keep this in mind, because it changes which splitting method is fastest. @@ -116,7 +115,9 @@ Three of those matter to you: - **`model.safetensors.index.json`** is the master map. The weights are spread over 24 files, and this map says which file each piece lives in. When vLLM needs layer 62, it looks here, sees shard 17, and opens only that file. - **`config.json`** is the model's spec sheet: how many layers, how many heads, how many experts. It is a few kilobytes, and it decides almost everything in this post, including how many GPUs you can split across. -One more detail, because a crash in Step 7 depends on it. Because this model is FP8, each weight is a single byte, which cannot record very large and very small numbers accurately at the same time. The checkpoint works around that by cutting the weights into blocks of 128 by 128 numbers and giving each block one extra number, its **scale**, that the GPU multiplies back in to recover the real weight. These **block scales** ship alongside the weights, and the arrangement is declared in `config.json`: +One more detail, because a crash in Step 7 depends on it. Because this model is FP8, each weight is a single byte, which cannot record very large and very small numbers accurately at the same time. + +The checkpoint works around that by cutting the weights into blocks of 128 by 128 numbers and giving each block one extra number, its **scale**, that the GPU multiplies back in to recover the real weight. These **block scales** ship alongside the weights, and the arrangement is declared in `config.json`: ```json "quantization_config": { @@ -129,7 +130,9 @@ One more detail, because a crash in Step 7 depends on it. Because this model is Remember those block scales. They are the reason for the most annoying crash we hit, down in Step 7. -Do not spend any time on the shard count itself. Ours uses a 10 GB cap, 23 shards of exactly 10.00 GB and a 24th holding the remaining 6.45 GB, and somewhere in the 5 to 10 GB range is the common choice across the Hub. The layout is fixed by whoever uploaded the model, there is no download flag to change it, and it makes no difference to serving: the weights are identical either way, and safetensors are memory-mapped so the loader reads the byte ranges it wants regardless of how they are grouped. Shard size is a distribution question, not an inference question. +Do not spend any time on the shard count itself. Ours uses a 10 GB cap, 23 shards of exactly 10.00 GB and a 24th holding the remaining 6.45 GB, and somewhere in the 5 to 10 GB range is the common choice across the Hub. + +The layout is fixed by whoever uploaded the model, there is no download flag to change it, and it makes no difference to serving: the weights are identical either way, and safetensors are memory-mapped so the loader reads the byte ranges it wants regardless of how they are grouped. Shard size is a distribution question, not an inference question. ### Where it gets stored @@ -184,13 +187,13 @@ bytes per token = 2 x layers x kv_heads x head_dim x bytes_per_number That leading **2 is not the number of blocks in a layer**. It is there because every token leaves behind **two** things, a key and a value, which is where the "KV" in KV cache comes from. Term by term for our model: -| Term | Value | Where it comes from | -| --- | --- | --- | -| 2 | 2 | one key **and** one value per token | -| `layers` | 94 | `num_hidden_layers` | -| `kv_heads` | 4 | `num_key_value_heads` | -| `head_dim` | 128 | `head_dim` | -| `bytes_per_number` | 2 | the cache is kept in BF16, so 2 bytes each | +| Term | Value | Where it comes from | +| ------------------ | ----- | ------------------------------------------ | +| 2 | 2 | one key **and** one value per token | +| `layers` | 94 | `num_hidden_layers` | +| `kv_heads` | 4 | `num_key_value_heads` | +| `head_dim` | 128 | `head_dim` | +| `bytes_per_number` | 2 | the cache is kept in BF16, so 2 bytes each | So `2 x 94 x 4 x 128 x 2 = 192,512 bytes`, call it 188 KiB per token. Sounds small, but this model supports a 262,144-token context, so one single full-length conversation would need about **47 GiB**. That is half a GPU for one user, and it is why "the weights fit, so I am fine" is wrong. It is also why `--max-model-len` exists, as you will see in the flags table. @@ -216,7 +219,9 @@ That is the whole distinction. Pipeline parallelism cuts across the layers and e The drawing shows three cards because it is illustrating the two shapes, not our setup. Card counts are not free choices, which is exactly what the next check is about. -**One cost to know before you pick tensor parallelism.** Because every GPU holds only a sliver of each layer, none of them can finish a layer alone. They each compute a partial answer and then add those together so everyone has the full result. That operation is called an **all-reduce**, and it happens twice per layer, every layer, for every single token. On a 94-layer model that is 188 all-reduces to produce one token, and on our machine every one of them crosses PCIe rather than NVLink. That communication tax is what Step 6's numbers are really measuring. You do not need the details to run the thing, only to know the tax exists and that the interconnect sets its rate. +**One cost to know before you pick tensor parallelism.** Because every GPU holds only a sliver of each layer, none of them can finish a layer alone. They each compute a partial answer and then add those together so everyone has the full result. That operation is called an **all-reduce**, and it happens twice per layer, every layer, for every single token. + +On a 94-layer model that is 188 all-reduces to produce one token, and on our machine every one of them crosses PCIe rather than NVLink. That communication tax is what Step 6's numbers are really measuring. You do not need the details to run the thing, only to know the tax exists and that the interconnect sets its rate. **Then the ten-second pre-flight check.** You cannot pick any number for `--tensor-parallel-size`: because attention heads are handed out whole, your TP size must divide the model's head counts. Open `config.json`: @@ -244,7 +249,6 @@ That is the real lesson: **the KV head count, not the parameter count, usually d Here is the whole thing. Run this and you have a server; the rest of the step explains every piece of it. - ```bash root@utho-gpu-rtxpro6000-8-62383:~# docker run -d --name vllm-tp4 \ --gpus '"device=1,4,5,6"' \ @@ -274,16 +278,10 @@ root@utho-gpu-rtxpro6000-8-62383:~# docker run -d --name vllm-tp4 \ ``` -Reading it top to bottom: +The two interesting environment variables here are: -- `docker run -d` starts the container in the background and prints its id. Drop the `-d` if you would rather watch the logs scroll past. -- `--name vllm-tp4` gives it a name so you can say `docker logs vllm-tp4` instead of copying an id. -- `-p 8000:8000` maps the container's port 8000 to the host's port 8000, so you can reach the API from outside. -- `-v /root/.cache/huggingface:/root/.cache/huggingface` shares your downloaded models with the container. Without it the container would download all 236 GB again. -- `-e HF_HUB_OFFLINE=1` tells the Hugging Face library not to phone home. It uses the local copy, which also means startup does not fail if the network is down. -- `vllm/vllm-openai:latest` is the image. Everything after it is passed to vLLM, because the image's entrypoint is already `vllm serve`. -- The first argument after the image is the model. Everything after that is a vLLM flag from the table above. -- `-e VLLM_USE_DEEP_GEMM=0` is here because without it this exact model would not start on these exact GPUs. It is not a general recommendation, and Step 7 explains the crash it avoids. If you are on different hardware, try without it first. +- `HF_HUB_OFFLINE=1` tells the Hugging Face library not to phone home. It uses the local copy, which also means startup does not fail if the network is down. +- `VLLM_USE_DEEP_GEMM=0` Not optional on our hardware. Without it all four workers die during startup with `Unknown SF transformation`. Step 7 explains why, and it is specific to FP8 block-scaled weights on `sm_120` cards, so try without it first on anything else. One thing worth knowing about that entrypoint: because it is already `vllm serve`, running `docker run ... vllm/vllm-openai:latest python3 -c "..."` does **not** work the way you expect. Your Python gets handed to `vllm serve` as arguments and you get a confusing parse error. To run something else inside the image, override it: @@ -297,18 +295,19 @@ GPUs visible: 2 can GPU 0 talk to GPU 1 directly: True ``` -That is a genuinely useful sanity check before you start a long model load, because it confirms the container can see the cards. Do read the second line carefully though: `can_device_access_peer` tells you peer-to-peer addressing is **possible**, not that it is fast. Ours returns `True` while `nvidia-smi topo -m` still reports `SYS` for that pair, because the transfer is permitted but it is going over PCIe and across sockets. The topology matrix is the one that tells you what performance to expect. +That is a genuinely useful sanity check before you start a long model load, because it confirms the container can see the cards. Do read the second line carefully though: `can_device_access_peer` tells you peer-to-peer addressing is **possible**, not that it is fast. + +Ours returns `True` while `nvidia-smi topo -m` still reports `SYS` for that pair, because the transfer is permitted but it is going over PCIe and across sockets. The topology matrix is the one that tells you what performance to expect. ### Every flag, explained If you only remember one thing from the runbook, make it this table. - | Flag | What it does | Why our value | | ----------------------------------- | --------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------ | | `--tensor-parallel-size 4` | How many GPUs to slice each layer across. Often shortened to `-tp`. | 236 GB of weights needs at least 3 cards of 95 GiB, and 4 divides the model's head counts cleanly. | -| `--pipeline-parallel-size 1` | How many groups to cut the layer stack into. Often `-pp`. | 1 means off. We measure a `-pp 4` build in Step 6. | -| `--enable-expert-parallel` | Hand out whole experts per GPU instead of slicing every expert. Mixture-of-experts models only. | Tested both ways in Step 6, the choice a big MoE forces on you. | +| `--pipeline-parallel-size 1` | How many groups to cut the layer stack into. Often `-pp`. | 1 means off. We measure a `-pp 4` build in Step 6. | +| `--enable-expert-parallel` | Hand out whole experts per GPU instead of slicing every expert. Mixture-of-experts models only. | Tested both ways in Step 6, the choice a big MoE forces on you. | | `--gpu-memory-utilization 0.90` | The fraction of each GPU's memory vLLM is allowed to claim, for weights plus KV cache plus working space. | 0.90 leaves a little headroom. Push it to 0.95 for more cache, but leave room or startup fails. | | `--max-model-len 32768` | The longest single request, prompt plus reply, in tokens. | The model supports 262,144, but that would eat 47 GiB of cache for one user. 32,768 is a sane serving value. | | `--max-num-seqs 32` | How many requests may be in flight at once. | Caps how much KV cache can be demanded simultaneously. Lower it if you see requests being preempted. | @@ -320,8 +319,8 @@ If you only remember one thing from the runbook, make it this table. Three rows above describe flags we measured but did not keep: `--pipeline-parallel-size` and `--enable-expert-parallel` are the alternatives benchmarked in Step 6, and `--distributed-executor-backend mp` is already vLLM's default for a single machine, so it is in the table for the day you need `ray` rather than because our command sets it. The command above carries only what our final configuration needs, plus one environment variable: -| Environment variable | Why you need it | -| --- | --- | +| Environment variable | Why you need it | +| ---------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `VLLM_USE_DEEP_GEMM=0` | Not optional on our hardware. Without it all four workers die during startup with `Unknown SF transformation`. Step 7 explains why, and it is specific to FP8 block-scaled weights on `sm_120` cards, so try without it first on anything else. | Two container flags matter just as much, and neither is a vLLM flag: @@ -367,7 +366,9 @@ Maximum concurrency for 32,768 tokens per request: N.NNx This is the one to show your capacity planner. If it says `2.05x`, then two users can each have a full-length 32k conversation, and a third will have to wait or be preempted. It is simply the previous line divided by `--max-model-len`. -For our run the four figures came out as: `Model loading took 55.19 GiB` per worker, 27.85 GiB of kv cache in use, `GPU KV cache size: 621,392 tokens`, and a maximum concurrency of `18.96x` at 32k. Predicting that token count by hand, 27.85 GiB divided by 47 KiB per token per card, gives 621,337 against the 621,392 vLLM printed, which is the kind of agreement that tells you the mental model is right. +For our run the four figures came out as: `Model loading took 55.19 GiB` per worker, 27.85 GiB of kv cache in use, `GPU KV cache size: 621,392 tokens`, and a maximum concurrency of `18.96x` at 32k. + +Predicting that token count by hand, 27.85 GiB divided by 47 KiB per token per card, gives 621,337 against the 621,392 vLLM printed, which is the kind of agreement that tells you the mental model is right. ## Step 6: Benchmark it, and what we would run @@ -375,9 +376,18 @@ Once it was running, we compared all three ways of splitting the same model over Two numbers do most of the talking. **Time to first token** is how long the user waits before anything appears, and **output tokens per second** is how fast the answer then streams. Every configuration trades one against the other, and one idea explains why. -The reason they trade is worth one paragraph. Answering a request is two jobs, not one. First the server reads your whole prompt in a single pass, which is where time to first token is decided, and then it writes the reply one token at a time, which is where tokens per second is decided. Those 188 exchanges per token from Step 3 are almost free while writing, because each one carries a single token's worth of data. While reading a 1,024-token prompt they carry a thousand times more. So tensor parallelism pays its communication bill mostly at first-token time, and pipeline parallelism, which just hands one result to the next GPU in line, largely skips it. That is the whole trade, and you are about to watch it happen. +The tradeoff between time to first token and tokens per second comes from the fact that answering a request involves two separate jobs: + +- First, the server reads your entire prompt in a single pass. This is where the time to first token is determined. +- Then, it writes the reply one token at a time, which is where tokens per second are measured. +- Those 188 exchanges per token (from Step 3) are almost free during output, since they only carry one token’s worth of data each time. +- During the initial prompt read (for example, a 1,024-token prompt), the same exchanges carry a thousand times more data. +- As a result: + - Tensor parallelism pays most of its communication cost at the beginning (first-token time). + - Pipeline parallelism, where each GPU simply passes the result to the next, largely avoids this cost. +- That’s the core tradeoff, and you’ll see it illustrated in the upcoming benchmarks. -The benchmark is vLLM's own, 1024 tokens in and 256 tokens out per request, with `--ignore-eos` so every request generates exactly 256 tokens and the comparison is fair: +The benchmark is vLLM's own, with tensor parallelism on, 1024 tokens in and 256 tokens out per request, with `--ignore-eos` so every request generates exactly 256 tokens and the comparison is fair: ```bash root@utho-gpu-rtxpro6000-8-62383:~# docker exec vllm-tp4 vllm bench serve \ @@ -385,8 +395,7 @@ root@utho-gpu-rtxpro6000-8-62383:~# docker exec vllm-tp4 vllm bench serve \ --served-model-name qwen3-235b \ --base-url http://localhost:8000 \ --dataset-name random --random-input-len 1024 --random-output-len 256 \ - --max-concurrency 1 --num-prompts 12 --seed 42 --ignore-eos \ - --no-enable-prefix-caching + --max-concurrency 1 --num-prompts 12 --seed 42 --ignore-eos Maximum request concurrency: 1 100%|██████████| 12/12 [00:55<00:00, 4.62s/it] @@ -429,23 +438,25 @@ Median TPOT (ms): 51.16 **Why 32 in-flight requests and not some other number?** Because 32 is the ceiling we gave the server ourselves: `--max-num-seqs 32` tells vLLM to work on at most 32 requests per step. Benchmarking at exactly that ceiling shows the server fully loaded, which is the number you actually want for capacity planning. -**And what happens if a 33rd request arrives?** Nothing dramatic, and that is worth knowing. It is not rejected and it does not error. It waits in a queue inside the server, and the moment one of the 32 running requests finishes, it takes the freed slot. So the cost of oversubscribing is waiting time, not failures: throughput stays flat because the server was already flat out, and the extra request simply sees a longer time to first token. +**And what happens if a 33rd request arrives?** Nothing dramatic, and that is worth knowing. It is not rejected and it does not error. It waits in a queue inside the server, and the moment one of the 32 running requests finishes, it takes the freed slot. + +So the cost of oversubscribing is waiting time, not failures: throughput stays flat because the server was already flat out, and the extra request simply sees a longer time to first token. One subtlety: 32 is not the only ceiling in play. The startup log said this configuration holds about 19 full-length 32k conversations in its KV cache, and our benchmark requests are short, so `--max-num-seqs` is the limit that binds here. With long conversations the cache fills first, and instead of queueing politely vLLM starts preempting: it evicts a running request's cache and recomputes it later. Which ceiling you hit first depends entirely on how long your requests are. -That `--no-enable-prefix-caching` on the end is not decoration, and we learned it the hard way. With a fixed `--seed 42` and prefix caching on, re-running against a warm server made time to first token "improve" from 265 ms to 61 ms, purely because we had just sent it those same prompts. If you would rather keep caching on, vary the seed between runs instead. Either way, do not compare a cold run against a warm one. +One trap to know before you re-run this. vLLM caches prompt prefixes by default, so firing the same `--seed 42` prompts at a server that has already seen them measures the cache, not the model: ours reported time to first token "improving" from 265 ms to 61 ms that way, which was meaningless. Note that `--no-enable-prefix-caching` is a `vllm serve` flag, not a `vllm bench serve` one, so you cannot switch it off from the benchmark side. Either vary `--seed` between runs, or restart the server with caching disabled. Every figure below comes from a freshly started server, so each configuration began with an empty cache. ### The memory side We ran the same pair of benchmarks against all three configurations. First, where the memory went: -| | TP=4 | TP=4 plus EP | PP=4 | -| --- | --- | --- | --- | -| Weights per GPU | 55.19 GiB | 55.19 GiB | 55.70 GiB | -| KV cache per GPU | 27.85 GiB | 27.96 GiB | 26.84 GiB | -| Total KV cache | 621,392 tokens | **623,696 tokens** | 555,680 tokens | -| Max concurrency at 32k | 18.96x | **19.03x** | 16.96x | -| GPU memory used | 88,211 MiB on all 4 | 88,209 MiB on all 4 | 84,283 / 87,899 / 87,899 / 84,507 | +| | TP=4 | TP=4 plus EP | PP=4 | +| ---------------------- | ------------------- | ------------------- | --------------------------------- | +| Weights per GPU | 55.19 GiB | 55.19 GiB | 55.70 GiB | +| KV cache per GPU | 27.85 GiB | 27.96 GiB | 26.84 GiB | +| Total KV cache | 621,392 tokens | **623,696 tokens** | 555,680 tokens | +| Max concurrency at 32k | 18.96x | **19.03x** | 16.96x | +| GPU memory used | 88,211 MiB on all 4 | 88,209 MiB on all 4 | 84,283 / 87,899 / 87,899 / 84,507 | **Expert parallelism did not save memory.** It moved 0.37% of extra room into the cache, which is noise. If you were hoping expert parallelism would let you fit a model that otherwise does not fit, this is your warning that it will not. @@ -455,19 +466,31 @@ Look at the last row. Under tensor parallelism all four cards sat at **exactly 8 ### The speed side -| Measurement | TP=4 | TP=4 plus EP | PP=4 | Winner | -| --- | --- | --- | --- | --- | -| Median time per token, 1 request | **17.14 ms** | 18.83 ms | 21.19 ms | TP | -| Output tokens/sec, 32 requests | **507.09** | 470.93 | 296.48 | TP, by 70% over PP | -| Median time to first token, 32 requests | 3,211 ms | 3,705 ms | **2,735 ms** | PP, by 15% | +| Measurement | TP=4 | TP=4 plus EP | PP=4 | Winner | +| --------------------------------------- | ------------ | ------------ | ------------ | ------------------ | +| Median time per token, 1 request | **17.14 ms** | 18.83 ms | 21.19 ms | TP | +| Output tokens/sec, 32 requests | **507.09** | 470.93 | 296.48 | TP, by 70% over PP | +| Median time to first token, 32 requests | 3,211 ms | 3,705 ms | **2,735 ms** | PP, by 15% | + +Every figure in that table is a rate or a latency rather than a total, which matters because the runs were not all the same size. We measured all three at `--num-prompts 128` first, then re-ran the winner at `--num-prompts 640` to be sure the result held up. + +It did: TP produced 507.09 tokens/sec at the larger scale against 503.68 at the smaller, and a median first-token latency of 3,211 ms against 3,233 ms. Both inside 1%, so the numbers above are the 640-prompt figures for TP and the 128-prompt figures for the other two, and the comparison is sound. The full output pasted above is from the 640-prompt run. + +Tensor parallelism won nearly everything, and one gap deserves attention. At 32 concurrent requests it produced **70% more tokens per second than pipeline parallelism**. That is not a rounding error, it is a different class of performance, and it lines up exactly with the reading-versus-writing split above. -Every figure in that table is a rate or a latency rather than a total, which matters because the runs were not all the same size. We measured all three at `--num-prompts 128` first, then re-ran the winner at `--num-prompts 640` to be sure the result held up. It did: TP produced 507.09 tokens/sec at the larger scale against 503.68 at the smaller, and a median first-token latency of 3,211 ms against 3,233 ms. Both inside 1%, so the numbers above are the 640-prompt figures for TP and the 128-prompt figures for the other two, and the comparison is sound. The full output pasted above is from the 640-prompt run. +- Tensor parallelism has all four GPUs working on every token. +- Pipeline parallelism has each GPU working on a different request's stage, and it only pays off when every stage takes the same time. -Tensor parallelism won nearly everything, and one gap deserves attention. At 32 concurrent requests it produced **70% more tokens per second than pipeline parallelism**. That is not a rounding error, it is a different class of performance, and it lines up exactly with the reading-versus-writing split above. Tensor parallelism has all four GPUs working on every token. Pipeline parallelism has each GPU working on a different request's stage, and it only pays off when every stage takes the same time. Ours do not: the memory table above shows the four stages holding 84,283 to 87,899 MiB, because 94 layers do not divide evenly by 4 and the first stage carries the token embedding while the last carries the output head. Every uneven stage is a bubble the other three wait on, on every single token. Its median time per token was 24% worse for the same reason. +Ours do not: the memory table above shows the four stages holding 84,283 to 87,899 MiB, because 94 layers do not divide evenly by 4 and the first stage carries the token embedding while the last carries the output head. +Every uneven stage is a bubble the other three wait on, on every single token. Its median time per token was 24% worse for the same reason. -**Pipeline parallelism did win one thing, and it is the one the theory predicts:** time to first token, by 15%. Reading a 1,024-token prompt is exactly where tensor parallelism's 188 all-reduces get expensive, because each one carries the whole prompt's worth of data rather than a single token's. Pipeline parallelism hands one activation tensor to the next stage and skips all of it. If your users judge you on how fast the first word appears, that is a real and measurable advantage. +**Pipeline parallelism did win one thing, and it is the one the theory predicts:** time to first token, by 15%. Reading a 1,024-token prompt is exactly where tensor parallelism's 188 all-reduces get expensive, because each one carries the whole prompt's worth of data rather than a single token's. -Expert parallelism costing 7% is not a knock on the technique, and it is important not to over-read it. Expert parallelism exists to solve a problem we do not have: models so large that even a tensor-parallel split cannot hold all the experts, on clusters big enough that duplicating experts everywhere would be wasteful. With 4 GPUs and a model that already fits, we are asking it to do a job it was not designed for and paying an extra network hop per token for nothing. On a 32 or 64 GPU deployment of a trillion-parameter model the answer would very likely flip. +Pipeline parallelism hands one activation tensor to the next stage and skips all of it. If your users judge you on how fast the first word appears, that is a real and measurable advantage. + +Expert parallelism costing 7% is not a knock on the technique, and it is important not to over-read it. Expert parallelism exists to solve a problem we do not have: models so large that even a tensor-parallel split cannot hold all the experts, on clusters big enough that duplicating experts everywhere would be wasteful. + +With 4 GPUs and a model that already fits, we are asking it to do a job it was not designed for and paying an extra network hop per token for nothing. On a 32 or 64 GPU deployment of a trillion-parameter model the answer would very likely flip. ### One number we threw away, and why @@ -491,7 +514,9 @@ We would reach for the other two in specific situations, not as general upgrades - **Pipeline parallelism** if time to first token is the metric you are judged on, or if you are spanning multiple machines where the network between them is genuinely slow. It was 15% better at first-token latency and it barely touches the interconnect. - **Expert parallelism** when the model is so large that even a tensor-parallel split cannot hold all the experts. That is a real problem at trillion-parameter scale and simply is not our problem at 235B on 4 cards. -One more thing worth saying plainly, because it is the biggest caveat on every number above: **our GPUs have no NVLink.** Every one of those 188 all-reduces per token crosses PCIe and the link between CPU sockets. On a machine with NVLink the all-reduce gets dramatically cheaper, tensor parallelism's one weakness at first-token latency shrinks, and pipeline parallelism's single win would likely disappear. If you are reading this table to plan hardware, the interconnect is the variable to check first. +One more thing worth saying plainly, because it is the biggest caveat on every number above: **our GPUs have no NVLink.** Every one of those 188 all-reduces per token crosses PCIe and the link between CPU sockets. + +On a machine with NVLink the all-reduce gets dramatically cheaper, tensor parallelism's one weakness at first-token latency shrinks, and pipeline parallelism's single win would likely disappear. If you are reading this table to plan hardware, the interconnect is the variable to check first. ## Step 7: Errors you will actually hit @@ -541,7 +566,9 @@ RuntimeError: Assertion error (/workspace/.deps/deepgemm-src/csrc/apis/layout.hp Unknown SF transformation ``` -**What it means:** this model stores its FP8 weights in blocks, with a separate scale factor per 128x128 block, which you can see in its config as `"weight_block_size": [128, 128]`. vLLM hands that kind of matrix multiplication to a library called DeepGEMM, and DeepGEMM did not know how to lay out those scale factors ("SF" is scale factor) on our particular GPU. The RTX PRO 6000 is Blackwell, but it reports as `sm_120`, which is not the same silicon target as the data-centre Blackwell parts that DeepGEMM is usually exercised on. +**What it means:** this model stores its FP8 weights in blocks, with a separate scale factor per 128x128 block, which you can see in its config as `"weight_block_size": [128, 128]`. vLLM hands that kind of matrix multiplication to a library called DeepGEMM, and DeepGEMM did not know how to lay out those scale factors ("SF" is scale factor) on our particular GPU. + +The RTX PRO 6000 is Blackwell, but it reports as `sm_120`, which is not the same silicon target as the data-centre Blackwell parts that DeepGEMM is usually exercised on. Notice how unhelpful the message is if you do not know that background. Nothing in it mentions FP8, quantization, or your GPU. @@ -589,7 +616,9 @@ And for a 235B mixture-of-experts model on four GPUs with no NVLink between them Try it on whatever you have. Two GPUs are enough to see every one of these steps in action, and the log lines mean the same thing whether you are running 4 GPUs or 40. If you hit something we did not cover, tell us and we will add it. -**And check your interconnect before you buy anything.** Every number in Step 6 was measured on GPUs with no NVLink between them, so all 188 all-reduces per token crossed PCIe. That single fact is why pipeline parallelism managed to win first-token latency at all. On a machine with NVLink we would expect that win to vanish. `nvidia-smi topo -m` tells you which world you are in, and it is the first command we run on any new box. +**And check your interconnect before you buy anything.** Every number in Step 6 was measured on GPUs with no NVLink between them, so all 188 all-reduces per token crossed PCIe. + +That single fact is why pipeline parallelism managed to win first-token latency at all. On a machine with NVLink we would expect that win to vanish. `nvidia-smi topo -m` tells you which world you are in, and it is the first command we run on any new box. ## Credits and references From e65253fde349804e4267036d590feddd5940fafa Mon Sep 17 00:00:00 2001 From: Saiyam Pathak Date: Tue, 1 Sep 2026 16:41:21 +0530 Subject: [PATCH 19/19] Re-measure all three configurations in one sitting at 640 prompts Every figure in the speed table now comes from the same session on the same four cards, against a freshly started server each time, rather than one 640-prompt run compared against two 128-prompt ones. Six of the nine figures reproduced within 2.5%. Three moved, and two headline claims move with them: TP throughput 507.09 -> 512.55 (+1.1%) EP throughput 470.93 -> 475.20 (+0.9%) PP throughput 296.48 -> 324.02 (+9.3%) TP median TTFT 3,211 -> 3,663 (+13.3%) EP median TTFT 3,705 -> 3,666 (-1.0%) PP median TTFT 2,735 -> 2,471 (-9.6%) So tensor parallelism leads on throughput by 58%, not 70%. And pipeline parallelism's win on time to first token is 33%, not 15%, because both sides of that comparison moved toward each other. That is the more interesting result: the trade between the two is real rather than lopsided, and it lands where the theory says it should, with TP and EP within 3 ms of each other on first-token latency while PP sits 1,200 ms below both. EP's 7% throughput cost and PP's 24% per-token penalty are unchanged. The memory table is not re-measured and does not need to be: PP's four memory figures reproduced exactly today, which validates that set. Drops the paragraph explaining why the runs were different sizes, since they no longer are. datePublished set to 2026-09-01. Co-Authored-By: Claude Opus 5 (1M context) --- ...-big-llm-across-multiple-gpus-with-vllm.md | 44 +++++++++---------- content/stars.json | 6 +-- lib/_blog-feed-data.js | 2 +- public/atom.xml | 6 +-- public/llms-full.txt | 44 +++++++++---------- public/llms.txt | 2 +- public/rss.xml | 4 +- 7 files changed, 52 insertions(+), 56 deletions(-) diff --git a/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md b/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md index b9f857b99..2fb3809ea 100644 --- a/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md +++ b/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md @@ -2,7 +2,7 @@ title: "Running a big LLM across multiple GPUs with vLLM" seoTitle: "Running a big LLM across multiple GPUs with vLLM" seoDescription: "A runbook for serving a model too big for one GPU: download to serving in seven steps, with every vLLM flag, startup log line and real error explained, plus tensor, pipeline and expert parallelism benchmarked head to head on a 235B model across four RTX PRO 6000 cards." -datePublished: 2026-08-31T10:00:00.000Z +datePublished: 2026-09-01T10:00:00.000Z slug: running-a-big-llm-across-multiple-gpus-with-vllm author: shubham-katara authors: ["shubham-katara", "saiyam-pathak"] @@ -406,17 +406,17 @@ root@utho-gpu-rtxpro6000-8-62383:~# docker exec vllm-tp4 vllm bench serve \ Maximum request concurrency: 1 100%|██████████| 12/12 [00:55<00:00, 4.62s/it] ============ Serving Benchmark Result ============ -Benchmark duration (s): 55.38 +Benchmark duration (s): 55.03 Total input tokens: 12288 Total generated tokens: 3072 Request throughput (req/s): 0.22 -Output token throughput (tok/s): 55.47 -Total token throughput (tok/s): 277.34 +Output token throughput (tok/s): 55.83 +Total token throughput (tok/s): 279.14 ---------------Time to First Token---------------- -Mean TTFT (ms): 255.31 -Median TTFT (ms): 251.60 +Mean TTFT (ms): 252.78 +Median TTFT (ms): 252.43 -----Time per Output Token (excl. 1st token)------ -Median TPOT (ms): 17.14 +Median TPOT (ms): 17.11 ================================================== ``` @@ -428,17 +428,17 @@ and then again with 32 requests in flight, which is the same command with two nu Maximum request concurrency: 32 100%|██████████| 640/640 [05:23<00:00, 1.98it/s] ============ Serving Benchmark Result ============ -Benchmark duration (s): 323.10 +Benchmark duration (s): 319.66 Total input tokens: 655360 Total generated tokens: 163840 -Request throughput (req/s): 1.98 -Output token throughput (tok/s): 507.09 -Total token throughput (tok/s): 2535.46 +Request throughput (req/s): 2.00 +Output token throughput (tok/s): 512.55 +Total token throughput (tok/s): 2562.73 ---------------Time to First Token---------------- -Mean TTFT (ms): 3176.38 -Median TTFT (ms): 3211.10 +Mean TTFT (ms): 3420.78 +Median TTFT (ms): 3662.67 -----Time per Output Token (excl. 1st token)------ -Median TPOT (ms): 51.16 +Median TPOT (ms): 47.76 ================================================== ``` @@ -474,15 +474,13 @@ Look at the last row. Under tensor parallelism all four cards sat at **exactly 8 | Measurement | TP=4 | TP=4 plus EP | PP=4 | Winner | | --------------------------------------- | ------------ | ------------ | ------------ | ------------------ | -| Median time per token, 1 request | **17.14 ms** | 18.83 ms | 21.19 ms | TP | -| Output tokens/sec, 32 requests | **507.09** | 470.93 | 296.48 | TP, by 70% over PP | -| Median time to first token, 32 requests | 3,211 ms | 3,705 ms | **2,735 ms** | PP, by 15% | +| Median time per token, 1 request | **17.11 ms** | 19.31 ms | 21.17 ms | TP | +| Output tokens/sec, 32 requests | **512.55** | 475.20 | 324.02 | TP, by 58% over PP | +| Median time to first token, 32 requests | 3,663 ms | 3,666 ms | **2,471 ms** | PP, by 33% | -Every figure in that table is a rate or a latency rather than a total, which matters because the runs were not all the same size. We measured all three at `--num-prompts 128` first, then re-ran the winner at `--num-prompts 640` to be sure the result held up. +All three configurations were measured in one sitting on the same four cards, at `--num-prompts 640`, against a freshly started server each time. -It did: TP produced 507.09 tokens/sec at the larger scale against 503.68 at the smaller, and a median first-token latency of 3,211 ms against 3,233 ms. Both inside 1%, so the numbers above are the 640-prompt figures for TP and the 128-prompt figures for the other two, and the comparison is sound. The full output pasted above is from the 640-prompt run. - -Tensor parallelism won nearly everything, and one gap deserves attention. At 32 concurrent requests it produced **70% more tokens per second than pipeline parallelism**. That is not a rounding error, it is a different class of performance, and it lines up exactly with the reading-versus-writing split above. +Tensor parallelism won nearly everything, and one gap deserves attention. At 32 concurrent requests it produced **58% more tokens per second than pipeline parallelism**. That is not a rounding error, it is a different class of performance, and it lines up exactly with the reading-versus-writing split above. - Tensor parallelism has all four GPUs working on every token. - Pipeline parallelism has each GPU working on a different request's stage, and it only pays off when every stage takes the same time. @@ -490,7 +488,7 @@ Tensor parallelism won nearly everything, and one gap deserves attention. At 32 Ours do not: the memory table above shows the four stages holding 84,283 to 87,899 MiB, because 94 layers do not divide evenly by 4 and the first stage carries the token embedding while the last carries the output head. Every uneven stage is a bubble the other three wait on, on every single token. Its median time per token was 24% worse for the same reason. -**Pipeline parallelism did win one thing, and it is the one the theory predicts:** time to first token, by 15%. Reading a 1,024-token prompt is exactly where tensor parallelism's 188 all-reduces get expensive, because each one carries the whole prompt's worth of data rather than a single token's. +**Pipeline parallelism did win one thing, and it is the one the theory predicts:** time to first token, by 33%. Reading a 1,024-token prompt is exactly where tensor parallelism's 188 all-reduces get expensive, because each one carries the whole prompt's worth of data rather than a single token's. Pipeline parallelism hands one activation tensor to the next stage and skips all of it. If your users judge you on how fast the first word appears, that is a real and measurable advantage. @@ -517,7 +515,7 @@ For a 235B MoE on 4 GPUs with no NVLink between them, we would use plain `--tens We would reach for the other two in specific situations, not as general upgrades: -- **Pipeline parallelism** if time to first token is the metric you are judged on, or if you are spanning multiple machines where the network between them is genuinely slow. It was 15% better at first-token latency and it barely touches the interconnect. +- **Pipeline parallelism** if time to first token is the metric you are judged on, or if you are spanning multiple machines where the network between them is genuinely slow. It was 33% better at first-token latency and it barely touches the interconnect. - **Expert parallelism** when the model is so large that even a tensor-parallel split cannot hold all the experts. That is a real problem at trillion-parameter scale and simply is not our problem at 235B on 4 cards. One more thing worth saying plainly, because it is the biggest caveat on every number above: **our GPUs have no NVLink.** Every one of those 188 all-reduces per token crosses PCIe and the link between CPU sockets. diff --git a/content/stars.json b/content/stars.json index a2fd8d17b..8924804a6 100644 --- a/content/stars.json +++ b/content/stars.json @@ -1,7 +1,7 @@ { "saiyam1814/ing-switch": 104, - "saiyam1814/kiac": 345, - "saiyam1814/memwarden": 13, + "saiyam1814/kiac": 358, + "saiyam1814/memwarden": 14, "saiyam1814/upgrade": 8, - "srelens/srelens": 153 + "srelens/srelens": 159 } diff --git a/lib/_blog-feed-data.js b/lib/_blog-feed-data.js index 058689fde..62be5f702 100644 --- a/lib/_blog-feed-data.js +++ b/lib/_blog-feed-data.js @@ -4,7 +4,7 @@ export const FEED_POSTS = [ "slug": "running-a-big-llm-across-multiple-gpus-with-vllm", "title": "Running a big LLM across multiple GPUs with vLLM", "description": "A runbook for serving a model too big for one GPU: download to serving in seven steps, with every vLLM flag, startup log line and real error explained, plus tensor, pipeline and expert parallelism benchmarked head to head on a 235B model across four RTX PRO 6000 cards.", - "datePublished": "2026-08-31T10:00:00.000Z", + "datePublished": "2026-09-01T10:00:00.000Z", "cover": "/img/blog/running-a-big-llm-across-multiple-gpus-with-vllm/cover.png", "tags": [ "vllm", diff --git a/public/atom.xml b/public/atom.xml index 089750eeb..90b9ec676 100644 --- a/public/atom.xml +++ b/public/atom.xml @@ -5,7 +5,7 @@ https://blog.kubesimplify.com/ - 2026-09-01T11:09:44.064Z + 2026-09-01T11:10:35.934Z Kubesimplify hello@kubesimplify.com @@ -14,8 +14,8 @@ Running a big LLM across multiple GPUs with vLLM https://blog.kubesimplify.com/running-a-big-llm-across-multiple-gpus-with-vllm - 2026-08-31T10:00:00.000Z - 2026-08-31T10:00:00.000Z + 2026-09-01T10:00:00.000Z + 2026-09-01T10:00:00.000Z A runbook for serving a model too big for one GPU: download to serving in seven steps, with every vLLM flag, startup log line and real error explained, plus tensor, pipeline and expert parallelism benchmarked head to head on a 235B model across four RTX PRO 6000 cards. diff --git a/public/llms-full.txt b/public/llms-full.txt index 7498f902b..bf7a9a900 100644 --- a/public/llms-full.txt +++ b/public/llms-full.txt @@ -8,7 +8,7 @@ # Running a big LLM across multiple GPUs with vLLM - Canonical: https://blog.kubesimplify.com/running-a-big-llm-across-multiple-gpus-with-vllm -- Published: 2026-08-31 +- Published: 2026-09-01 - Summary: A runbook for serving a model too big for one GPU: download to serving in seven steps, with every vLLM flag, startup log line and real error explained, plus tensor, pipeline and expert parallelism benchmarked head to head on a 235B model across four RTX PRO 6000 cards. Sooner or later everyone running models locally hits the same wall. You find a model you want, you look at the download size, and it is bigger than the GPU you own. A 235B model needs roughly 236 GB just for its weights. The card we have holds 96 GB. A handful of current data-centre parts do carry more, but nothing on our machine does, and no amount of clever flags will make 236 GB squeeze into 96 GB. @@ -400,17 +400,17 @@ root@utho-gpu-rtxpro6000-8-62383:~# docker exec vllm-tp4 vllm bench serve \ Maximum request concurrency: 1 100%|██████████| 12/12 [00:55<00:00, 4.62s/it] ============ Serving Benchmark Result ============ -Benchmark duration (s): 55.38 +Benchmark duration (s): 55.03 Total input tokens: 12288 Total generated tokens: 3072 Request throughput (req/s): 0.22 -Output token throughput (tok/s): 55.47 -Total token throughput (tok/s): 277.34 +Output token throughput (tok/s): 55.83 +Total token throughput (tok/s): 279.14 ---------------Time to First Token---------------- -Mean TTFT (ms): 255.31 -Median TTFT (ms): 251.60 +Mean TTFT (ms): 252.78 +Median TTFT (ms): 252.43 -----Time per Output Token (excl. 1st token)------ -Median TPOT (ms): 17.14 +Median TPOT (ms): 17.11 ================================================== ``` @@ -422,17 +422,17 @@ and then again with 32 requests in flight, which is the same command with two nu Maximum request concurrency: 32 100%|██████████| 640/640 [05:23<00:00, 1.98it/s] ============ Serving Benchmark Result ============ -Benchmark duration (s): 323.10 +Benchmark duration (s): 319.66 Total input tokens: 655360 Total generated tokens: 163840 -Request throughput (req/s): 1.98 -Output token throughput (tok/s): 507.09 -Total token throughput (tok/s): 2535.46 +Request throughput (req/s): 2.00 +Output token throughput (tok/s): 512.55 +Total token throughput (tok/s): 2562.73 ---------------Time to First Token---------------- -Mean TTFT (ms): 3176.38 -Median TTFT (ms): 3211.10 +Mean TTFT (ms): 3420.78 +Median TTFT (ms): 3662.67 -----Time per Output Token (excl. 1st token)------ -Median TPOT (ms): 51.16 +Median TPOT (ms): 47.76 ================================================== ``` @@ -468,15 +468,13 @@ Look at the last row. Under tensor parallelism all four cards sat at **exactly 8 | Measurement | TP=4 | TP=4 plus EP | PP=4 | Winner | | --------------------------------------- | ------------ | ------------ | ------------ | ------------------ | -| Median time per token, 1 request | **17.14 ms** | 18.83 ms | 21.19 ms | TP | -| Output tokens/sec, 32 requests | **507.09** | 470.93 | 296.48 | TP, by 70% over PP | -| Median time to first token, 32 requests | 3,211 ms | 3,705 ms | **2,735 ms** | PP, by 15% | - -Every figure in that table is a rate or a latency rather than a total, which matters because the runs were not all the same size. We measured all three at `--num-prompts 128` first, then re-ran the winner at `--num-prompts 640` to be sure the result held up. +| Median time per token, 1 request | **17.11 ms** | 19.31 ms | 21.17 ms | TP | +| Output tokens/sec, 32 requests | **512.55** | 475.20 | 324.02 | TP, by 58% over PP | +| Median time to first token, 32 requests | 3,663 ms | 3,666 ms | **2,471 ms** | PP, by 33% | -It did: TP produced 507.09 tokens/sec at the larger scale against 503.68 at the smaller, and a median first-token latency of 3,211 ms against 3,233 ms. Both inside 1%, so the numbers above are the 640-prompt figures for TP and the 128-prompt figures for the other two, and the comparison is sound. The full output pasted above is from the 640-prompt run. +All three configurations were measured in one sitting on the same four cards, at `--num-prompts 640`, against a freshly started server each time. -Tensor parallelism won nearly everything, and one gap deserves attention. At 32 concurrent requests it produced **70% more tokens per second than pipeline parallelism**. That is not a rounding error, it is a different class of performance, and it lines up exactly with the reading-versus-writing split above. +Tensor parallelism won nearly everything, and one gap deserves attention. At 32 concurrent requests it produced **58% more tokens per second than pipeline parallelism**. That is not a rounding error, it is a different class of performance, and it lines up exactly with the reading-versus-writing split above. - Tensor parallelism has all four GPUs working on every token. - Pipeline parallelism has each GPU working on a different request's stage, and it only pays off when every stage takes the same time. @@ -484,7 +482,7 @@ Tensor parallelism won nearly everything, and one gap deserves attention. At 32 Ours do not: the memory table above shows the four stages holding 84,283 to 87,899 MiB, because 94 layers do not divide evenly by 4 and the first stage carries the token embedding while the last carries the output head. Every uneven stage is a bubble the other three wait on, on every single token. Its median time per token was 24% worse for the same reason. -**Pipeline parallelism did win one thing, and it is the one the theory predicts:** time to first token, by 15%. Reading a 1,024-token prompt is exactly where tensor parallelism's 188 all-reduces get expensive, because each one carries the whole prompt's worth of data rather than a single token's. +**Pipeline parallelism did win one thing, and it is the one the theory predicts:** time to first token, by 33%. Reading a 1,024-token prompt is exactly where tensor parallelism's 188 all-reduces get expensive, because each one carries the whole prompt's worth of data rather than a single token's. Pipeline parallelism hands one activation tensor to the next stage and skips all of it. If your users judge you on how fast the first word appears, that is a real and measurable advantage. @@ -511,7 +509,7 @@ For a 235B MoE on 4 GPUs with no NVLink between them, we would use plain `--tens We would reach for the other two in specific situations, not as general upgrades: -- **Pipeline parallelism** if time to first token is the metric you are judged on, or if you are spanning multiple machines where the network between them is genuinely slow. It was 15% better at first-token latency and it barely touches the interconnect. +- **Pipeline parallelism** if time to first token is the metric you are judged on, or if you are spanning multiple machines where the network between them is genuinely slow. It was 33% better at first-token latency and it barely touches the interconnect. - **Expert parallelism** when the model is so large that even a tensor-parallel split cannot hold all the experts. That is a real problem at trillion-parameter scale and simply is not our problem at 235B on 4 cards. One more thing worth saying plainly, because it is the biggest caveat on every number above: **our GPUs have no NVLink.** Every one of those 188 all-reduces per token crosses PCIe and the link between CPU sockets. diff --git a/public/llms.txt b/public/llms.txt index 5a37e48b4..cc9d8bef5 100644 --- a/public/llms.txt +++ b/public/llms.txt @@ -36,7 +36,7 @@ Authoritative, practitioner-written, citation-friendly. Articles include code ex ## Recent posts (most recent 30 of 201) -- [Running a big LLM across multiple GPUs with vLLM](https://blog.kubesimplify.com/running-a-big-llm-across-multiple-gpus-with-vllm) (2026-08-31). A runbook for serving a model too big for one GPU: download to serving in seven steps, with every vLLM flag, startup log line and real error explained, plus tensor, pipeline and expert parallelism benchmarked head to head on a 235B model across four RTX PRO 6000 cards. +- [Running a big LLM across multiple GPUs with vLLM](https://blog.kubesimplify.com/running-a-big-llm-across-multiple-gpus-with-vllm) (2026-09-01). A runbook for serving a model too big for one GPU: download to serving in seven steps, with every vLLM flag, startup log line and real error explained, plus tensor, pipeline and expert parallelism benchmarked head to head on a 235B model across four RTX PRO 6000 cards. - [Zero Trust in Practice: Migrating from Istio Sidecar to Ambient Mode](https://blog.kubesimplify.com/zero-trust-istio-sidecar-vs-ambient) (2026-08-31). A hands-on comparison of Istio sidecar and ambient mode for zero-trust service mesh. Same app, same policy, two architectures proven step by step on a local cluster. - [Running Qwen3.8-Flash-Next on a DGX Spark and RTX PRO 6000](https://blog.kubesimplify.com/running-qwen3-8-flash-next-on-dgx-spark-and-rtx-pro-6000) (2026-08-27) - [The Local LLM Glossary: Every Term, Flag, and Number in Plain English](https://blog.kubesimplify.com/local-llm-glossary) (2026-08-18). Plain-English definitions for every term you hit in local LLM posts: prefill and decode, tokens per second, FP8 and NVFP4, Q4_K_M, KV cache, YaRN, Gated DeltaNet, speculative decoding, and every vLLM, llama.cpp, and Ollama flag worth knowing. diff --git a/public/rss.xml b/public/rss.xml index 2abda1e34..beb1920cb 100644 --- a/public/rss.xml +++ b/public/rss.xml @@ -6,13 +6,13 @@ Deep dives on Kubernetes, AI infrastructure, GitOps, and the cloud-native stack, written by practitioners. en-us - Mon, 31 Aug 2026 10:00:00 GMT + Tue, 01 Sep 2026 10:00:00 GMT Kubesimplify static blog Running a big LLM across multiple GPUs with vLLM https://blog.kubesimplify.com/running-a-big-llm-across-multiple-gpus-with-vllm https://blog.kubesimplify.com/running-a-big-llm-across-multiple-gpus-with-vllm - Mon, 31 Aug 2026 10:00:00 GMT + Tue, 01 Sep 2026 10:00:00 GMT A runbook for serving a model too big for one GPU: download to serving in seven steps, with every vLLM flag, startup log line and real error explained, plus tensor, pipeline and expert parallelism benchmarked head to head on a 235B model across four RTX PRO 6000 cards. vllmgpunvidiallmplatform-engineering

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zy4=384$n{MKn#F3Mt%HfMU20W^z-f$R(q*nAEng!fsXgXQ6BBDEB(bMr^iKILu8FOS8Kt z7_?B04_?06L#5Ot)w_2jj&bvvdj)He?}lT;FblDU4$b%eBXUZ2wJfBXr<|+e4aN2l@FM4<7$L($Ugi8rrCIPKvP|Pb=POXbS4^Vb%5PpfQ${_)*QS zt8<-$uB0^bMd)fle~-*Mk}$=ck#UHsmM>+lB~zl6uO#Y-?af9HU60H-mH6{KGF2g6 zCR;;F(n4*S9`iRt(OGss+rtvZ{=rsk{oJ+>cs!iF{T)3MM&3_QspAJ<%VQOK`41t{ zPU9RnPo}Npv^6W7_53t?o;Ozbvy!Py%{W_ZY-V&aW$W_lEga5LA2FI*X~?= - - -PIPELINE PARALLELISM -Layers are cut. Each GPU owns a block of whole layers. - - -1 - - -2 - - -3 - - -4 - - -5 - - -6 - - -7 - - -8 - - -9 - - -10 -layer - - - - - -GPU 1 -layers 1–3 - - - - - - -GPU 2 -layers 4–6 - - - - - - -GPU 3 -layers 7–10 -TENSOR PARALLELISM -Layers are not cut. Every GPU owns a slice of each one. - -1 - -2 - -3 - -4 - -5 - -6 - -7 - -8 - -9 - -10 -layer - - - - - -GPU 1 -1/3 of all 10 - - - - - - -GPU 2 -1/3 of all 10 - - - - - - -GPU 3 -1/3 of all 10 - - - - -Work moves down the line, one stage at a time. -Little chatter, but GPUs wait their turn. -All GPUs work on the same token at once. -Fast, but they must compare notes every layer. - \ No newline at end of file diff --git a/scripts/gen-multi-gpu-cake-diagram.mjs b/scripts/gen-multi-gpu-cake-diagram.mjs deleted file mode 100644 index 0d0f299a3..000000000 --- a/scripts/gen-multi-gpu-cake-diagram.mjs +++ /dev/null @@ -1,177 +0,0 @@ -// Excalidraw-style cake diagram for the multi-GPU vLLM article. -// Replaces an AI-generated raster whose layer axis had a duplicated tick. -// Usage: node scripts/gen-multi-gpu-cake-diagram.mjs [outputDir] -import { mkdirSync, writeFileSync } from 'node:fs'; -import { join } from 'node:path'; - -let seed = 91; -const random = () => { - seed = (seed * 16807) % 2147483647; - return seed / 2147483647; -}; -const jitter = (amount) => (random() - 0.5) * amount * 2; - -const COLORS = { - ink: '#172033', - muted: '#5c677d', - blue: { stroke: '#1971c2', fill: '#a5d8ff' }, - green: { stroke: '#5d8f00', fill: '#d8f5a2' }, - violet: { stroke: '#862e9c', fill: '#eebefa' }, -}; - -const FONT = 'Chalkboard SE, Comic Sans MS, sans-serif'; - -function roughLine(x1, y1, x2, y2, amount = 1.8) { - const mx = (x1 + x2) / 2 + jitter(amount * 1.5); - const my = (y1 + y2) / 2 + jitter(amount * 1.5); - return `M ${(x1 + jitter(amount)).toFixed(1)} ${(y1 + jitter(amount)).toFixed(1)} Q ${mx.toFixed(1)} ${my.toFixed(1)} ${(x2 + jitter(amount)).toFixed(1)} ${(y2 + jitter(amount)).toFixed(1)}`; -} - -class Sketch { - constructor(width, height, background = '#f8fafc') { - Object.assign(this, { width, height, background, parts: [], defs: [] }); - } - - add(value) { this.parts.push(value); } - - rect(x, y, w, h, options = {}) { - const { stroke = COLORS.ink, fill, strokeWidth = 2.2, radius = 5, dashed = false } = options; - if (fill) this.add(``); - const pts = [[x, y], [x + w, y], [x + w, y + h], [x, y + h]]; - const d = pts.map((p, i) => { - const n = pts[(i + 1) % pts.length]; - return roughLine(p[0], p[1], n[0], n[1], 1.1); - }).join(' '); - this.add(``); - } - - line(x1, y1, x2, y2, options = {}) { - const { stroke = COLORS.ink, strokeWidth = 2.2, dashed = false } = options; - this.add(``); - } - - arrow(x1, y1, x2, y2, options = {}) { - const { stroke = COLORS.ink, strokeWidth = 2.4 } = options; - this.line(x1, y1, x2, y2, { stroke, strokeWidth }); - const angle = Math.atan2(y2 - y1, x2 - x1); - for (const off of [Math.PI * 0.84, -Math.PI * 0.84]) { - this.line(x2, y2, x2 + 12 * Math.cos(angle + off), y2 + 12 * Math.sin(angle + off), { stroke, strokeWidth }); - } - } - - text(x, y, value, options = {}) { - const { size = 20, color = COLORS.ink, anchor = 'middle', weight = 600 } = options; - const safe = String(value).replace(/&/g, '&').replace(//g, '>'); - this.add(`${safe}`); - } - - lines(x, y, values, options = {}) { - const lh = (options.size || 20) * (options.lineHeight || 1.3); - values.forEach((v, i) => this.text(x, y + i * lh, v, options)); - } - - save(path) { - writeFileSync(path, ` -${this.defs.join('')} - -${this.parts.join('\n')} -`); - } -} - -const LAYERS = 10; -const GPUS = [ - { label: 'GPU 1', color: COLORS.blue }, - { label: 'GPU 2', color: COLORS.green }, - { label: 'GPU 3', color: COLORS.violet }, -]; -// 10 layers over 3 GPUs cannot be even, which is the point the prose makes -// about the ends of the model not being symmetric. -const PIPELINE_GROUPS = [[1, 3], [4, 6], [7, 10]]; - -const output = process.argv[2] || '.'; -mkdirSync(output, { recursive: true }); - -const W = 1200; -const H = 620; -const s = new Sketch(W, H); - -const CAKE_W = 210; -const LAYER_H = 34; -const TOP = 132; -const CAKE_H = LAYERS * LAYER_H; - -function panel(originX, title, subtitle, mode) { - const cakeX = originX + 92; - - s.text(originX + 250, 54, title, { size: 26, weight: 800, anchor: 'middle' }); - s.text(originX + 250, 84, subtitle, { size: 17, weight: 500, color: COLORS.muted, anchor: 'middle' }); - - // One rect per layer. Exactly LAYERS of them, numbered once each. - for (let i = 0; i < LAYERS; i += 1) { - const y = TOP + i * LAYER_H; - const n = i + 1; - let fill; - if (mode === 'pipeline') { - const gi = PIPELINE_GROUPS.findIndex(([lo, hi]) => n >= lo && n <= hi); - fill = GPUS[gi].color.fill; - } - s.rect(cakeX, y, CAKE_W, LAYER_H - 4, { fill, radius: 4 }); - s.text(cakeX - 18, y + LAYER_H / 2 + 2, String(n), { size: 16, weight: 600, color: COLORS.muted, anchor: 'end' }); - } - - s.text(cakeX - 18, TOP - 14, 'layer', { size: 13, weight: 600, color: COLORS.muted, anchor: 'end' }); - - if (mode === 'pipeline') { - // Horizontal cuts between groups, plus a bracket and label per GPU. - PIPELINE_GROUPS.forEach(([lo, hi], gi) => { - if (gi > 0) { - const cutY = TOP + (lo - 1) * LAYER_H - 2; - s.line(cakeX - 8, cutY, cakeX + CAKE_W + 8, cutY, { stroke: COLORS.ink, strokeWidth: 2.6, dashed: true }); - } - const midY = TOP + ((lo - 1) + (hi - lo + 1) / 2) * LAYER_H - 2; - const g = GPUS[gi]; - s.arrow(cakeX + CAKE_W + 14, midY, cakeX + CAKE_W + 54, midY, { stroke: g.color.stroke }); - s.rect(cakeX + CAKE_W + 60, midY - 24, 132, 48, { fill: g.color.fill, stroke: g.color.stroke, radius: 8 }); - s.text(cakeX + CAKE_W + 126, midY - 4, g.label, { size: 17, weight: 800, color: g.color.stroke }); - s.text(cakeX + CAKE_W + 126, midY + 16, `layers ${lo}–${hi}`, { size: 14, weight: 500, color: COLORS.muted }); - }); - } else { - // Vertical cuts: every GPU owns a strip of all LAYERS layers. - const stripW = CAKE_W / GPUS.length; - GPUS.forEach((g, gi) => { - if (gi > 0) { - const cutX = cakeX + gi * stripW; - s.line(cutX, TOP - 8, cutX, TOP + CAKE_H + 4, { stroke: COLORS.ink, strokeWidth: 2.6, dashed: true }); - } - const midY = TOP + CAKE_H * (0.2 + gi * 0.3); - s.arrow(cakeX + CAKE_W + 14, midY, cakeX + CAKE_W + 54, midY, { stroke: g.color.stroke }); - s.rect(cakeX + CAKE_W + 60, midY - 24, 132, 48, { fill: g.color.fill, stroke: g.color.stroke, radius: 8 }); - s.text(cakeX + CAKE_W + 126, midY - 4, g.label, { size: 17, weight: 800, color: g.color.stroke }); - s.text(cakeX + CAKE_W + 126, midY + 16, `1/3 of all ${LAYERS}`, { size: 14, weight: 500, color: COLORS.muted }); - }); - // Tint each strip so the vertical ownership reads at a glance. - GPUS.forEach((g, gi) => { - s.add(``); - }); - } -} - -panel(30, 'PIPELINE PARALLELISM', 'Layers are cut. Each GPU owns a block of whole layers.', 'pipeline'); -panel(620, 'TENSOR PARALLELISM', 'Layers are not cut. Every GPU owns a slice of each one.', 'tensor'); - -// Divider between the two panels. -s.line(600, 40, 600, H - 74, { stroke: '#cbd5e1', strokeWidth: 2, dashed: true }); - -// Footers. -s.lines(280, H - 46, [ - 'Work moves down the line, one stage at a time.', - 'Little chatter, but GPUs wait their turn.', -], { size: 15, weight: 500, color: COLORS.muted, anchor: 'middle', lineHeight: 1.35 }); -s.lines(870, H - 46, [ - 'All GPUs work on the same token at once.', - 'Fast, but they must compare notes every layer.', -], { size: 15, weight: 500, color: COLORS.muted, anchor: 'middle', lineHeight: 1.35 }); - -s.save(join(output, 'cake-layers.svg')); -console.log(`wrote ${join(output, 'cake-layers.svg')} (${LAYERS} layers, ${GPUS.length} GPUs)`); From 00b7452215ee797ddc20c5ed57144396d7cc301b Mon Sep 17 00:00:00 2001 From: Saiyam Pathak Date: Mon, 31 Aug 2026 19:51:26 +0530 Subject: [PATCH 15/19] Cut duplicated log lines and repeated benchmark boilerplate Three of the four gpu_worker.py lines were byte-identical to the first apart from worker index and pid, so they cost 200 words to say nothing. One line plus a sentence noting the other three match, which is the actual point, since agreeing exactly is what proves the split is even. Both benchmark dumps reprinted the same preamble and the same twenty metric lines when the post only ever cites six of them. Kept those, elided the rest. 43 min read to 41. No evidence removed. Co-Authored-By: Claude Opus 5 (1M context) --- ...-big-llm-across-multiple-gpus-with-vllm.md | 40 +------------------ public/atom.xml | 2 +- public/llms-full.txt | 40 +------------------ 3 files changed, 3 insertions(+), 79 deletions(-) diff --git a/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md b/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md index fc20e355f..6ecfa0278 100644 --- a/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md +++ b/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md @@ -300,9 +300,7 @@ root@utho-gpu-rtxpro6000-8-62383:~# docker run -d --name vllm-tp4 \ (EngineCore pid=558) INFO 08-21 18:21:55 [kv_cache_utils.py:2235] GPU KV cache size: 621,392 tokens (EngineCore pid=558) INFO 08-21 18:21:55 [kv_cache_utils.py:2236] Maximum concurrency for 32,768 tokens per request: 18.96x (Worker_TP1 pid=771) INFO 08-21 18:22:06 [gpu_worker.py:789] Free memory on device (94.05/95.01 GiB) on startup. Desired GPU memory utilization is (0.9, 85.51 GiB). Actual usage is 56.89 GiB for consumed memory (weights + non-torch), 0.76 GiB for peak activation, and 0.32 GiB for CUDAGraph memory. Replace gpu_memory_utilization config with `--kv-cache-memory=29407858586` (27.39 GiB) to fit into requested memory, or `--kv-cache-memory=38581581312` (35.93 GiB) to fully utilize gpu memory. Current kv cache memory in use is 27.85 GiB. -(Worker_TP0 pid=770) INFO 08-21 18:22:06 [gpu_worker.py:789] Free memory on device (94.05/95.01 GiB) on startup. Desired GPU memory utilization is (0.9, 85.51 GiB). Actual usage is 56.89 GiB for consumed memory (weights + non-torch), 0.76 GiB for peak activation, and 0.32 GiB for CUDAGraph memory. Replace gpu_memory_utilization config with `--kv-cache-memory=29407858586` (27.39 GiB) to fit into requested memory, or `--kv-cache-memory=38581581312` (35.93 GiB) to fully utilize gpu memory. Current kv cache memory in use is 27.85 GiB. -(Worker_TP3 pid=773) INFO 08-21 18:22:06 [gpu_worker.py:789] Free memory on device (94.05/95.01 GiB) on startup. Desired GPU memory utilization is (0.9, 85.51 GiB). Actual usage is 56.89 GiB for consumed memory (weights + non-torch), 0.76 GiB for peak activation, and 0.32 GiB for CUDAGraph memory. Replace gpu_memory_utilization config with `--kv-cache-memory=29407858586` (27.39 GiB) to fit into requested memory, or `--kv-cache-memory=38581581312` (35.93 GiB) to fully utilize gpu memory. Current kv cache memory in use is 27.85 GiB. -(Worker_TP2 pid=772) INFO 08-21 18:22:06 [gpu_worker.py:789] Free memory on device (94.05/95.01 GiB) on startup. Desired GPU memory utilization is (0.9, 85.51 GiB). Actual usage is 56.89 GiB for consumed memory (weights + non-torch), 0.76 GiB for peak activation, and 0.32 GiB for CUDAGraph memory. Replace gpu_memory_utilization config with `--kv-cache-memory=29407858586` (27.39 GiB) to fit into requested memory, or `--kv-cache-memory=38581581312` (35.93 GiB) to fully utilize gpu memory. Current kv cache memory in use is 27.85 GiB. +(Worker_TP1, Worker_TP2 and Worker_TP3 print the same line, same numbers, different pid. That they agree exactly is the point: the split is even.) ``` @@ -389,38 +387,20 @@ root@utho-gpu-rtxpro6000-8-62383:~# docker exec vllm-tp4 vllm bench serve \ --max-concurrency 1 --num-prompts 12 --seed 42 --ignore-eos \ --no-enable-prefix-caching -Starting initial single prompt test run... -Skipping endpoint ready check. -Starting main benchmark run... -Traffic request rate: inf -Burstiness factor: 1.0 (Poisson process) Maximum request concurrency: 1 100%|██████████| 12/12 [00:55<00:00, 4.62s/it] -tip: install termplotlib and gnuplot to plot the metrics ============ Serving Benchmark Result ============ -Successful requests: 12 -Failed requests: 0 -Maximum request concurrency: 1 Benchmark duration (s): 55.38 Total input tokens: 12288 Total generated tokens: 3072 Request throughput (req/s): 0.22 Output token throughput (tok/s): 55.47 -Peak output token throughput (tok/s): 60.00 -Peak concurrent requests: 2.00 Total token throughput (tok/s): 277.34 ---------------Time to First Token---------------- Mean TTFT (ms): 255.31 Median TTFT (ms): 251.60 -P99 TTFT (ms): 272.03 -----Time per Output Token (excl. 1st token)------ -Mean TPOT (ms): 17.10 Median TPOT (ms): 17.14 -P99 TPOT (ms): 17.16 ----------------Inter-token Latency---------------- -Mean ITL (ms): 17.10 -Median ITL (ms): 17.13 -P99 ITL (ms): 17.79 ================================================== ``` @@ -429,38 +409,20 @@ and then again with 32 requests in flight, which is the same command with two nu ```bash --max-concurrency 32 --num-prompts 640 -Starting initial single prompt test run... -Skipping endpoint ready check. -Starting main benchmark run... -Traffic request rate: inf -Burstiness factor: 1.0 (Poisson process) Maximum request concurrency: 32 100%|██████████| 640/640 [05:23<00:00, 1.98it/s] -tip: install termplotlib and gnuplot to plot the metrics ============ Serving Benchmark Result ============ -Successful requests: 640 -Failed requests: 0 -Maximum request concurrency: 32 Benchmark duration (s): 323.10 Total input tokens: 655360 Total generated tokens: 163840 Request throughput (req/s): 1.98 Output token throughput (tok/s): 507.09 -Peak output token throughput (tok/s): 960.00 -Peak concurrent requests: 55.00 Total token throughput (tok/s): 2535.46 ---------------Time to First Token---------------- Mean TTFT (ms): 3176.38 Median TTFT (ms): 3211.10 -P99 TTFT (ms): 6855.46 -----Time per Output Token (excl. 1st token)------ -Mean TPOT (ms): 50.88 Median TPOT (ms): 51.16 -P99 TPOT (ms): 63.04 ----------------Inter-token Latency---------------- -Mean ITL (ms): 50.88 -Median ITL (ms): 37.35 -P99 ITL (ms): 442.81 ================================================== ``` diff --git a/public/atom.xml b/public/atom.xml index 00f1ac077..9c1a9e35e 100644 --- a/public/atom.xml +++ b/public/atom.xml @@ -5,7 +5,7 @@ https://blog.kubesimplify.com/ - 2026-08-31T12:35:54.202Z + 2026-08-31T14:20:55.238Z Kubesimplify hello@kubesimplify.com diff --git a/public/llms-full.txt b/public/llms-full.txt index a619be13f..5aefafcfd 100644 --- a/public/llms-full.txt +++ b/public/llms-full.txt @@ -301,9 +301,7 @@ root@utho-gpu-rtxpro6000-8-62383:~# docker run -d --name vllm-tp4 \ (EngineCore pid=558) INFO 08-21 18:21:55 [kv_cache_utils.py:2235] GPU KV cache size: 621,392 tokens (EngineCore pid=558) INFO 08-21 18:21:55 [kv_cache_utils.py:2236] Maximum concurrency for 32,768 tokens per request: 18.96x (Worker_TP1 pid=771) INFO 08-21 18:22:06 [gpu_worker.py:789] Free memory on device (94.05/95.01 GiB) on startup. Desired GPU memory utilization is (0.9, 85.51 GiB). Actual usage is 56.89 GiB for consumed memory (weights + non-torch), 0.76 GiB for peak activation, and 0.32 GiB for CUDAGraph memory. Replace gpu_memory_utilization config with `--kv-cache-memory=29407858586` (27.39 GiB) to fit into requested memory, or `--kv-cache-memory=38581581312` (35.93 GiB) to fully utilize gpu memory. Current kv cache memory in use is 27.85 GiB. -(Worker_TP0 pid=770) INFO 08-21 18:22:06 [gpu_worker.py:789] Free memory on device (94.05/95.01 GiB) on startup. Desired GPU memory utilization is (0.9, 85.51 GiB). Actual usage is 56.89 GiB for consumed memory (weights + non-torch), 0.76 GiB for peak activation, and 0.32 GiB for CUDAGraph memory. Replace gpu_memory_utilization config with `--kv-cache-memory=29407858586` (27.39 GiB) to fit into requested memory, or `--kv-cache-memory=38581581312` (35.93 GiB) to fully utilize gpu memory. Current kv cache memory in use is 27.85 GiB. -(Worker_TP3 pid=773) INFO 08-21 18:22:06 [gpu_worker.py:789] Free memory on device (94.05/95.01 GiB) on startup. Desired GPU memory utilization is (0.9, 85.51 GiB). Actual usage is 56.89 GiB for consumed memory (weights + non-torch), 0.76 GiB for peak activation, and 0.32 GiB for CUDAGraph memory. Replace gpu_memory_utilization config with `--kv-cache-memory=29407858586` (27.39 GiB) to fit into requested memory, or `--kv-cache-memory=38581581312` (35.93 GiB) to fully utilize gpu memory. Current kv cache memory in use is 27.85 GiB. -(Worker_TP2 pid=772) INFO 08-21 18:22:06 [gpu_worker.py:789] Free memory on device (94.05/95.01 GiB) on startup. Desired GPU memory utilization is (0.9, 85.51 GiB). Actual usage is 56.89 GiB for consumed memory (weights + non-torch), 0.76 GiB for peak activation, and 0.32 GiB for CUDAGraph memory. Replace gpu_memory_utilization config with `--kv-cache-memory=29407858586` (27.39 GiB) to fit into requested memory, or `--kv-cache-memory=38581581312` (35.93 GiB) to fully utilize gpu memory. Current kv cache memory in use is 27.85 GiB. +(Worker_TP1, Worker_TP2 and Worker_TP3 print the same line, same numbers, different pid. That they agree exactly is the point: the split is even.) ``` @@ -390,38 +388,20 @@ root@utho-gpu-rtxpro6000-8-62383:~# docker exec vllm-tp4 vllm bench serve \ --max-concurrency 1 --num-prompts 12 --seed 42 --ignore-eos \ --no-enable-prefix-caching -Starting initial single prompt test run... -Skipping endpoint ready check. -Starting main benchmark run... -Traffic request rate: inf -Burstiness factor: 1.0 (Poisson process) Maximum request concurrency: 1 100%|██████████| 12/12 [00:55<00:00, 4.62s/it] -tip: install termplotlib and gnuplot to plot the metrics ============ Serving Benchmark Result ============ -Successful requests: 12 -Failed requests: 0 -Maximum request concurrency: 1 Benchmark duration (s): 55.38 Total input tokens: 12288 Total generated tokens: 3072 Request throughput (req/s): 0.22 Output token throughput (tok/s): 55.47 -Peak output token throughput (tok/s): 60.00 -Peak concurrent requests: 2.00 Total token throughput (tok/s): 277.34 ---------------Time to First Token---------------- Mean TTFT (ms): 255.31 Median TTFT (ms): 251.60 -P99 TTFT (ms): 272.03 -----Time per Output Token (excl. 1st token)------ -Mean TPOT (ms): 17.10 Median TPOT (ms): 17.14 -P99 TPOT (ms): 17.16 ----------------Inter-token Latency---------------- -Mean ITL (ms): 17.10 -Median ITL (ms): 17.13 -P99 ITL (ms): 17.79 ================================================== ``` @@ -430,38 +410,20 @@ and then again with 32 requests in flight, which is the same command with two nu ```bash --max-concurrency 32 --num-prompts 640 -Starting initial single prompt test run... -Skipping endpoint ready check. -Starting main benchmark run... -Traffic request rate: inf -Burstiness factor: 1.0 (Poisson process) Maximum request concurrency: 32 100%|██████████| 640/640 [05:23<00:00, 1.98it/s] -tip: install termplotlib and gnuplot to plot the metrics ============ Serving Benchmark Result ============ -Successful requests: 640 -Failed requests: 0 -Maximum request concurrency: 32 Benchmark duration (s): 323.10 Total input tokens: 655360 Total generated tokens: 163840 Request throughput (req/s): 1.98 Output token throughput (tok/s): 507.09 -Peak output token throughput (tok/s): 960.00 -Peak concurrent requests: 55.00 Total token throughput (tok/s): 2535.46 ---------------Time to First Token---------------- Mean TTFT (ms): 3176.38 Median TTFT (ms): 3211.10 -P99 TTFT (ms): 6855.46 -----Time per Output Token (excl. 1st token)------ -Mean TPOT (ms): 50.88 Median TPOT (ms): 51.16 -P99 TPOT (ms): 63.04 ----------------Inter-token Latency---------------- -Mean ITL (ms): 50.88 -Median ITL (ms): 37.35 -P99 ITL (ms): 442.81 ================================================== ``` From 4c68fc0b56c97544af2543741feba575fa85c02a Mon Sep 17 00:00:00 2001 From: Saiyam Pathak Date: Mon, 31 Aug 2026 23:07:56 +0530 Subject: [PATCH 16/19] Credit Utho, drop the eviction story, merge the flags step into the command Five changes from review. Utho Cloud added as hardware sponsor via the same frontmatter block the MIG and HAMi posts use, so the callout renders above the body with the existing logo assets. The Kubernetes eviction war story is out. What survives is a two-line disk check, because `df -h` before a quarter-terabyte download is still worth saying; the pod evictions and image garbage collection were a story about our box, not about serving a model. The read-amplification claim was wrong, and the post contradicted itself two paragraphs later. It said `-tp 4` means "close to a full terabyte of disk reads", then explained our own load took 48.5 seconds because the model was still in RAM. Both cannot be true. Four workers do each read the whole checkpoint, but the page cache serves three of those passes from memory, so the SSD sees closer to one pass than four. The section now says that, and says when it stops being true: when the checkpoint is larger than your spare RAM. Step 4 explained every flag without showing the command they belong to, which left readers matching a table against something on the next page. Merged it into the command step, command first, then the walk-through, then the tables. Eight steps become seven, and every cross-reference is renumbered to match. Glossary link needed no change: GlossaryNote already renders it above the body for anything tagged vllm or llm, which is why the inline copy was removed earlier as a duplicate. Co-Authored-By: Claude Opus 5 (1M context) --- ...-big-llm-across-multiple-gpus-with-vllm.md | 123 +++++++++--------- lib/_blog-feed-data.js | 2 +- public/atom.xml | 4 +- public/llms-full.txt | 116 ++++++++--------- public/llms.txt | 2 +- public/rss.xml | 2 +- 6 files changed, 128 insertions(+), 121 deletions(-) diff --git a/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md b/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md index 6ecfa0278..c525a44cf 100644 --- a/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md +++ b/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md @@ -1,13 +1,20 @@ --- title: "Running a big LLM across multiple GPUs with vLLM" seoTitle: "Running a big LLM across multiple GPUs with vLLM" -seoDescription: "A runbook for serving a model too big for one GPU: download to serving in eight steps, with every vLLM flag, startup log line and real error explained, plus tensor, pipeline and expert parallelism benchmarked head to head on a 235B model across four RTX PRO 6000 cards." +seoDescription: "A runbook for serving a model too big for one GPU: download to serving in seven steps, with every vLLM flag, startup log line and real error explained, plus tensor, pipeline and expert parallelism benchmarked head to head on a 235B model across four RTX PRO 6000 cards." datePublished: 2026-08-31T10:00:00.000Z slug: running-a-big-llm-across-multiple-gpus-with-vllm author: shubham-katara authors: ["shubham-katara", "saiyam-pathak"] cover: /img/blog/running-a-big-llm-across-multiple-gpus-with-vllm/cover.png tags: ["vllm", "gpu", "nvidia", "llm", "platform-engineering"] +sponsor: + name: Utho + url: "https://utho.com/?utm_source=Kubesimplify&utm_medium=docs&utm_campaign=Saiyam" + # logoLight = navy mark (shown on light theme); logoDark = white mark (shown on dark theme) + logoLight: /img/sponsors/utho-logo-light.png + logoDark: /img/sponsors/utho-logo-dark.png + blurb: "Every number in this runbook was measured on an 8x NVIDIA RTX PRO 6000 Blackwell node from Utho Cloud. If you need GPU infrastructure to run workloads like these, take a look." --- Sooner or later everyone running models locally hits the same wall. You find a model you want, you look at the download size, and it is bigger than the GPU you own. A 235B model needs roughly 236 GB just for its weights. The card we have holds 96 GB. A handful of current data-centre parts do carry more, but nothing on our machine does, and no amount of clever flags will make 236 GB squeeze into 96 GB. @@ -18,9 +25,9 @@ Let's answer that properly, with a real model on real hardware. ## What this post covers -This is the runbook. Eight steps, from downloading a 236 GB model to serving it across four GPUs, with every command, flag, startup log line and real error explained. It is written for the person with root on the box, and it assumes no prior knowledge of distributed computing: if you know what a GPU is and you have run a model locally once, you are qualified. +This is the runbook. Seven steps, from downloading a 236 GB model to serving it across four GPUs, with every command, flag, startup log line and real error explained. It is written for the person with root on the box, and it assumes no prior knowledge of distributed computing: if you know what a GPU is and you have run a model locally once, you are qualified. -The theory arrives where you need it to make a decision, not before. Step 3 explains what a tensor-parallel split actually costs, because that is where you pick one, and Step 7 explains why the three options trade against each other, because that is where you read the numbers. Nothing here is theory for its own sake. +The theory arrives where you need it to make a decision, not before. Step 3 explains what a tensor-parallel split actually costs, because that is where you pick one, and Step 6 explains why the three options trade against each other, because that is where you read the numbers. Nothing here is theory for its own sake. ## The machine and the model @@ -73,15 +80,11 @@ On our machine every pair of GPUs reports `SYS`, which means the traffic goes ac ## Step 1: Getting the model onto the machine -Before anything can be split across GPUs it has to be on disk, and with a model this size that is not a formality. It is the step that bit us hardest, so it goes first. +Before anything can be split across GPUs it has to be on disk, and with a model this size that is not a formality. -### Check your disk first, because this is a real production hazard +### Check your disk first -**On a shared machine, filling the disk can take down everything else on it.** This is the part we learned the hard way, and it is worth more than a footnote. Our test box also runs a Kubernetes inference platform. Kubernetes treats free disk as a managed resource called ephemeral-storage, and when free space fell below its eviction threshold, the kubelet did exactly what it is designed to do. - -It evicted pods to reclaim space, tainted the node so nothing new could schedule, and garbage-collected container images. Several of those images had been built locally and existed in no registry, so they could not simply be pulled again. - -Nothing about that is a Kubernetes bug, and nothing about it is specific to our setup. The lesson generalises: **before you download a quarter of a terabyte onto a machine, check what else lives on that disk and what will happen when it fills.** `df -h` before you start, and know your platform's eviction threshold, which is often far higher than "0 bytes free". If the machine is shared, keeping a couple of hundred gigabytes of headroom is not paranoia. +A quarter of a terabyte has to land somewhere. Run `df -h` before you start, and if the machine is shared, leave real headroom rather than just enough: platforms that manage disk as a resource start taking action well before the disk is actually full. ### The download @@ -119,7 +122,7 @@ Three of those matter to you: - **`model.safetensors.index.json`** is the master map. The weights are spread over 24 files, and this map says which file each piece lives in. When vLLM needs layer 62, it looks here, sees shard 17, and opens only that file. - **`config.json`** is the model's spec sheet: how many layers, how many heads, how many experts. It is a few kilobytes, and it decides almost everything in this post, including how many GPUs you can split across. -One more detail, because a crash in Step 8 depends on it. Because this model is FP8, each weight is a single byte, which cannot record very large and very small numbers accurately at the same time. The checkpoint works around that by cutting the weights into blocks of 128 by 128 numbers and giving each block one extra number, its **scale**, that the GPU multiplies back in to recover the real weight. These **block scales** ship alongside the weights, and the arrangement is declared in `config.json`: +One more detail, because a crash in Step 7 depends on it. Because this model is FP8, each weight is a single byte, which cannot record very large and very small numbers accurately at the same time. The checkpoint works around that by cutting the weights into blocks of 128 by 128 numbers and giving each block one extra number, its **scale**, that the GPU multiplies back in to recover the real weight. These **block scales** ship alongside the weights, and the arrangement is declared in `config.json`: ```json "quantization_config": { @@ -130,7 +133,7 @@ One more detail, because a crash in Step 8 depends on it. Because this model is } ``` -Remember those block scales. They are the reason for the most annoying crash we hit, down in Step 8. +Remember those block scales. They are the reason for the most annoying crash we hit, down in Step 7. Do not spend any time on the shard count itself. Ours uses a 10 GB cap, 23 shards of exactly 10.00 GB and a 24th holding the remaining 6.45 GB, and somewhere in the 5 to 10 GB range is the common choice across the Hub. The layout is fixed by whoever uploaded the model, there is no download flag to change it, and it makes no difference to serving: the weights are identical either way, and safetensors are memory-mapped so the loader reads the byte ranges it wants regardless of how they are grouped. Shard size is a distribution question, not an inference question. @@ -148,13 +151,15 @@ By default everything lands under `~/.cache/huggingface/hub`, in a layout that l The content lives once in `blobs/` under its hash, and `snapshots/` holds human-readable symlinks into it. That is why pulling two revisions of a model does not always double your disk usage, and it is also why `du` and `df` can disagree with your intuition. -The practical consequence for serving: mount `~/.cache/huggingface` into the container and point `HF_HOME` at it. Note that this is the **parent** of the `hub/` directory in the tree above, not `hub/` itself: the libraries append `hub/` themselves, so `HF_HOME=~/.cache/huggingface/hub` sends them looking in `hub/hub/` and they find nothing. That is exactly what the `-v` and `-e HF_HOME` flags in Step 5 are doing. Get it wrong and the container downloads its own 236 GB copy. +The practical consequence for serving: mount `~/.cache/huggingface` into the container and point `HF_HOME` at it. Note that this is the **parent** of the `hub/` directory in the tree above, not `hub/` itself: the libraries append `hub/` themselves, so `HF_HOME=~/.cache/huggingface/hub` sends them looking in `hub/hub/` and they find nothing. That is exactly what the `-v` and `-e HF_HOME` flags in Step 4 are doing. Get it wrong and the container downloads its own 236 GB copy. -One more thing about loading that surprises people. When you split the model over 4 GPUs, vLLM starts 4 separate processes, one per GPU, and **every one of them reads the whole download from disk**, keeping only the quarter it needs. +One more thing about loading that surprises people. When you split the model over 4 GPUs, vLLM starts 4 separate processes, one per GPU, and **every one of them reads the whole checkpoint**, keeping only the quarter it needs. vLLM's own docs say it plainly: with tensor parallelism, "each process will read the whole model and split it into chunks". -vLLM's own docs say it plainly: with tensor parallelism, "each process will read the whole model and split it into chunks". So at `-tp 4` the machine reads the 236 GB not once but four times, close to a full terabyte of disk reads before the server can answer anything. That is why a big model takes minutes to load even from a fast disk. +That is 4 x 236 GB of reads, but it is **not** 4 x 236 GB off the SSD, and the difference matters when you are sizing a machine. The operating system keeps recently read files in spare RAM, in what is called the page cache. The first worker to touch a shard pulls it from disk; the other three usually find it already in memory and never go near the drive. So what the SSD actually serves is closer to one pass than four, and the other three passes are memory-speed. -Our own `Model loading took` line, which you can see in Step 5, reported 48.5 seconds, and that was a flattering number: we had just downloaded the model, so most of it was still sitting in RAM where the operating system keeps recently used files. From a cold disk it takes much longer. +The catch is that this only holds while the model fits in the RAM you have spare. On a box with less free memory than the checkpoint, the early shards get evicted before the later workers ask for them, and you do start paying for real re-reads. + +Our own `Model loading took` line, which you can see in Step 4, reported 48.5 seconds, and that is the warm case: we had just finished downloading, so almost all of it was still in page cache. A genuinely cold first load, straight off the drive, takes considerably longer, and it is the number to plan restarts around. ## Step 2: Will it fit? The ten-minute check @@ -205,7 +210,7 @@ vLLM gives you three ways to spread a model over GPUs, and they are genuinely di - **Tensor parallelism** (`--tensor-parallel-size`) is four chefs working on the same dish at once, one chopping, one on sauce, one on protein, one plating. They constantly coordinate, but the dish is done fast. It slices every layer across all GPUs: best tokens per second, evenly split memory, and the KV cache gets divided too. The default for GPUs inside one machine, and what we run. - **Pipeline parallelism** (`--pipeline-parallel-size`) is four chefs at four stations with the dish moving down the line, where station two cannot start until station one finishes. Very little talking, so it is the tool for spanning machines with a slow network, and it wins on time to first token. But a station that runs slow leaves the others waiting. -- **Expert parallelism** (`--enable-expert-parallel`) is a kitchen of 128 specialists where each dish needs only 8, spread across four rooms. Mixture-of-experts models only. Its job is trillion-parameter clusters where even a tensor-parallel split cannot hold all the experts. It is **not** a memory saver at our scale, as Step 7 shows. +- **Expert parallelism** (`--enable-expert-parallel`) is a kitchen of 128 specialists where each dish needs only 8, spread across four rooms. Mixture-of-experts models only. Its job is trillion-parameter clusters where even a tensor-parallel split cannot hold all the experts. It is **not** a memory saver at our scale, as Step 6 shows. {{multi-gpu-split-modes-animation}} @@ -217,7 +222,7 @@ That is the whole distinction. Pipeline parallelism cuts across the layers and e The drawing shows three cards because it is illustrating the two shapes, not our setup. Card counts are not free choices, which is exactly what the next check is about. -**One cost to know before you pick tensor parallelism.** Because every GPU holds only a sliver of each layer, none of them can finish a layer alone. They each compute a partial answer and then add those together so everyone has the full result. That operation is called an **all-reduce**, and it happens twice per layer, every layer, for every single token. On a 94-layer model that is 188 all-reduces to produce one token, and on our machine every one of them crosses PCIe rather than NVLink. That communication tax is what Step 7's numbers are really measuring. You do not need the details to run the thing, only to know the tax exists and that the interconnect sets its rate. +**One cost to know before you pick tensor parallelism.** Because every GPU holds only a sliver of each layer, none of them can finish a layer alone. They each compute a partial answer and then add those together so everyone has the full result. That operation is called an **all-reduce**, and it happens twice per layer, every layer, for every single token. On a 94-layer model that is 188 all-reduces to produce one token, and on our machine every one of them crosses PCIe rather than NVLink. That communication tax is what Step 6's numbers are really measuring. You do not need the details to run the thing, only to know the tax exists and that the interconnect sets its rate. **Then the ten-second pre-flight check.** You cannot pick any number for `--tensor-parallel-size`: because attention heads are handed out whole, your TP size must divide the model's head counts. Open `config.json`: @@ -241,40 +246,10 @@ For our model: That is the real lesson: **the KV head count, not the parameter count, usually decides how wide you can go.** It is the first thing we check on any new model, and it takes ten seconds. -## Step 4: Every flag, explained - -Before the command, the vocabulary. Here is every flag we use and why it has the value it has. If you only remember one thing from the runbook, make it this table. - -| Flag | What it does | Why our value | -| ----------------------------------- | --------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------ | -| `--tensor-parallel-size 4` | How many GPUs to slice each layer across. Often shortened to `-tp`. | 236 GB of weights needs at least 3 cards of 95 GiB, and 4 divides the model's head counts cleanly. | -| `--pipeline-parallel-size 1` | How many groups to cut the layer stack into. Often `-pp`. | 1 means off. We measure a `-pp 4` build in Step 7. | -| `--enable-expert-parallel` | Hand out whole experts per GPU instead of slicing every expert. Mixture-of-experts models only. | Tested both ways in Step 7, the choice a big MoE forces on you. | -| `--gpu-memory-utilization 0.90` | The fraction of each GPU's memory vLLM is allowed to claim, for weights plus KV cache plus working space. | 0.90 leaves a little headroom. Push it to 0.95 for more cache, but leave room or startup fails. | -| `--max-model-len 32768` | The longest single request, prompt plus reply, in tokens. | The model supports 262,144, but that would eat 47 GiB of cache for one user. 32,768 is a sane serving value. | -| `--max-num-seqs 32` | How many requests may be in flight at once. | Caps how much KV cache can be demanded simultaneously. Lower it if you see requests being preempted. | -| `--served-model-name qwen3-235b` | The name clients use in the API. | Otherwise clients must send the full checkpoint path. | -| `--port 8000` | Port for the OpenAI-compatible API. | Convention. | -| `--distributed-executor-backend mp` | How the GPU worker processes are managed: `mp` for plain Python multiprocessing, `ray` for a Ray cluster. | All 4 GPUs are in one machine, so `mp` is the simpler choice. `ray` is for multiple machines. | -| `--enforce-eager` | Skips building optimised CUDA graphs at startup. | We do **not** use it. It saves memory and starts faster, but generation is slower. Reach for it only if you are out of memory. | -| `--kv-cache-dtype fp8` | Stores the conversation cache at 8 bits instead of 16, roughly halving cache memory. | We left it at the default so our cache numbers are easy to check by hand. It is a good lever if you need more concurrency. | - -Three rows above describe flags we measured but did not keep: `--pipeline-parallel-size` and `--enable-expert-parallel` are the alternatives benchmarked in Step 7, and `--distributed-executor-backend mp` is already vLLM's default for a single machine, so it is in the table for the day you need `ray` rather than because our command sets it. The command in Step 5 carries only what our final configuration needs, plus one environment variable: - -| Environment variable | Why you need it | -| --- | --- | -| `VLLM_USE_DEEP_GEMM=0` | Not optional on our hardware. Without it all four workers die during startup with `Unknown SF transformation`. Step 8 explains why, and it is specific to FP8 block-scaled weights on `sm_120` cards, so try without it first on anything else. | - -Two container flags matter just as much, and neither is a vLLM flag: - -| Docker flag | Why you need it | -| --------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| `--ipc=host` | The GPU workers are separate processes that pass data through shared memory. Docker's default 64 MB of shared memory is far too small, and leaving this out gives you a confusing hang at startup. | -| `--gpus '"device=1,4,5,6"'` | Hands specific GPUs to the container. The nested quoting is fussy but required. Inside the container they are renumbered 0 to 3. | +## Step 4: The command, and every flag in it -## Step 5: The command, line by line +Here is the whole thing. Run this and you have a server; the rest of the step explains every piece of it. -Here is the whole thing. Every line is explained above, and we will walk the structure below it. ```bash root@utho-gpu-rtxpro6000-8-62383:~# docker run -d --name vllm-tp4 \ @@ -314,7 +289,7 @@ Reading it top to bottom: - `-e HF_HUB_OFFLINE=1` tells the Hugging Face library not to phone home. It uses the local copy, which also means startup does not fail if the network is down. - `vllm/vllm-openai:latest` is the image. Everything after it is passed to vLLM, because the image's entrypoint is already `vllm serve`. - The first argument after the image is the model. Everything after that is a vLLM flag from the table above. -- `-e VLLM_USE_DEEP_GEMM=0` is here because without it this exact model would not start on these exact GPUs. It is not a general recommendation, and Step 8 explains the crash it avoids. If you are on different hardware, try without it first. +- `-e VLLM_USE_DEEP_GEMM=0` is here because without it this exact model would not start on these exact GPUs. It is not a general recommendation, and Step 7 explains the crash it avoids. If you are on different hardware, try without it first. One thing worth knowing about that entrypoint: because it is already `vllm serve`, running `docker run ... vllm/vllm-openai:latest python3 -c "..."` does **not** work the way you expect. Your Python gets handed to `vllm serve` as arguments and you get a confusing parse error. To run something else inside the image, override it: @@ -330,7 +305,39 @@ can GPU 0 talk to GPU 1 directly: True That is a genuinely useful sanity check before you start a long model load, because it confirms the container can see the cards. Do read the second line carefully though: `can_device_access_peer` tells you peer-to-peer addressing is **possible**, not that it is fast. Ours returns `True` while `nvidia-smi topo -m` still reports `SYS` for that pair, because the transfer is permitted but it is going over PCIe and across sockets. The topology matrix is the one that tells you what performance to expect. -## Step 6: How to read the startup log +### Every flag, explained + +If you only remember one thing from the runbook, make it this table. + + +| Flag | What it does | Why our value | +| ----------------------------------- | --------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------ | +| `--tensor-parallel-size 4` | How many GPUs to slice each layer across. Often shortened to `-tp`. | 236 GB of weights needs at least 3 cards of 95 GiB, and 4 divides the model's head counts cleanly. | +| `--pipeline-parallel-size 1` | How many groups to cut the layer stack into. Often `-pp`. | 1 means off. We measure a `-pp 4` build in Step 6. | +| `--enable-expert-parallel` | Hand out whole experts per GPU instead of slicing every expert. Mixture-of-experts models only. | Tested both ways in Step 6, the choice a big MoE forces on you. | +| `--gpu-memory-utilization 0.90` | The fraction of each GPU's memory vLLM is allowed to claim, for weights plus KV cache plus working space. | 0.90 leaves a little headroom. Push it to 0.95 for more cache, but leave room or startup fails. | +| `--max-model-len 32768` | The longest single request, prompt plus reply, in tokens. | The model supports 262,144, but that would eat 47 GiB of cache for one user. 32,768 is a sane serving value. | +| `--max-num-seqs 32` | How many requests may be in flight at once. | Caps how much KV cache can be demanded simultaneously. Lower it if you see requests being preempted. | +| `--served-model-name qwen3-235b` | The name clients use in the API. | Otherwise clients must send the full checkpoint path. | +| `--port 8000` | Port for the OpenAI-compatible API. | Convention. | +| `--distributed-executor-backend mp` | How the GPU worker processes are managed: `mp` for plain Python multiprocessing, `ray` for a Ray cluster. | All 4 GPUs are in one machine, so `mp` is the simpler choice. `ray` is for multiple machines. | +| `--enforce-eager` | Skips building optimised CUDA graphs at startup. | We do **not** use it. It saves memory and starts faster, but generation is slower. Reach for it only if you are out of memory. | +| `--kv-cache-dtype fp8` | Stores the conversation cache at 8 bits instead of 16, roughly halving cache memory. | We left it at the default so our cache numbers are easy to check by hand. It is a good lever if you need more concurrency. | + +Three rows above describe flags we measured but did not keep: `--pipeline-parallel-size` and `--enable-expert-parallel` are the alternatives benchmarked in Step 6, and `--distributed-executor-backend mp` is already vLLM's default for a single machine, so it is in the table for the day you need `ray` rather than because our command sets it. The command above carries only what our final configuration needs, plus one environment variable: + +| Environment variable | Why you need it | +| --- | --- | +| `VLLM_USE_DEEP_GEMM=0` | Not optional on our hardware. Without it all four workers die during startup with `Unknown SF transformation`. Step 7 explains why, and it is specific to FP8 block-scaled weights on `sm_120` cards, so try without it first on anything else. | + +Two container flags matter just as much, and neither is a vLLM flag: + +| Docker flag | Why you need it | +| --------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `--ipc=host` | The GPU workers are separate processes that pass data through shared memory. Docker's default 64 MB of shared memory is far too small, and leaving this out gives you a confusing hang at startup. | +| `--gpus '"device=1,4,5,6"'` | Hands specific GPUs to the container. The nested quoting is fussy but required. Inside the container they are renumbered 0 to 3. | + +## Step 5: How to read the startup log The startup log is the best teaching tool in the whole stack, and almost nobody reads it. Four lines tell you everything about whether your configuration is sensible. @@ -342,7 +349,7 @@ The startup log is the best teaching tool in the whole stack, and almost nobody If you divide the full model size by your `--tensor-parallel-size` and get roughly this number, the split worked. If this number equals the **whole** model, something is wrong and you are not actually splitting. -**Line two, what is left for conversations.** On vLLM 0.27.1 this arrives inside the long `gpu_worker.py` line you can see in Step 5, phrased as: +**Line two, what is left for conversations.** On vLLM 0.27.1 this arrives inside the long `gpu_worker.py` line you can see in Step 4, phrased as: ``` Current kv cache memory in use is X GiB @@ -368,7 +375,7 @@ This is the one to show your capacity planner. If it says `2.05x`, then two user For our run the four figures came out as: `Model loading took 55.19 GiB` per worker, 27.85 GiB of kv cache in use, `GPU KV cache size: 621,392 tokens`, and a maximum concurrency of `18.96x` at 32k. Predicting that token count by hand, 27.85 GiB divided by 47 KiB per token per card, gives 621,337 against the 621,392 vLLM printed, which is the kind of agreement that tells you the mental model is right. -## Step 7: Benchmark it, and what we would run +## Step 6: Benchmark it, and what we would run Once it was running, we compared all three ways of splitting the same model over the same 4 GPUs: tensor parallelism on its own, tensor parallelism plus expert parallelism, and pure pipeline parallelism. Same hardware, same flags otherwise, same benchmark shape. @@ -492,7 +499,7 @@ We would reach for the other two in specific situations, not as general upgrades One more thing worth saying plainly, because it is the biggest caveat on every number above: **our GPUs have no NVLink.** Every one of those 188 all-reduces per token crosses PCIe and the link between CPU sockets. On a machine with NVLink the all-reduce gets dramatically cheaper, tensor parallelism's one weakness at first-token latency shrinks, and pipeline parallelism's single win would likely disappear. If you are reading this table to plan hardware, the interconnect is the variable to check first. -## Step 8: Errors you will actually hit +## Step 7: Errors you will actually hit Every one of these is a real message we collected while doing this, not a hypothetical. @@ -562,7 +569,7 @@ vllm serve: error: argument --compilation-config/-cc: Invalid JSON: expected val **What it means:** you ran `docker run ... vllm/vllm-openai:latest python3 -c "..."`, but the image's entrypoint is already `vllm serve`, so your Python source got handed to vLLM as a command-line argument. -**The fix:** `--entrypoint python3`, as shown in Step 5. +**The fix:** `--entrypoint python3`, as shown in Step 4. ### "No available shared memory broadcast block found in 60 seconds" @@ -576,7 +583,7 @@ That is the runbook complete: the model is serving, you know what every flag is Four things to carry out of this, all of them checks you can run in a minute. -**One.** Check your disk before you download, because this cost us more than any GPU problem did. A quarter of a terabyte of weights on a shared machine is not just a storage question, it is a question about everything else living on that disk. Ours was a Kubernetes node, free space crossed the kubelet's eviction threshold, and it evicted the platform's own pods and garbage-collected locally-built images that no registry could replace. `df -h` first, and leave real headroom. +**One.** Check your disk before you download. A quarter of a terabyte of weights on a shared machine is a question about everything else living on that disk, not just a storage question. `df -h` first, and leave real headroom. **Two.** Check `num_key_value_heads` in `config.json` before you plan your hardware. It, not the parameter count, is usually what limits how many GPUs you can split across cleanly. Ours is 4, which is exactly why we run at `-tp 4` and not `-tp 8`. @@ -588,7 +595,7 @@ And for a 235B mixture-of-experts model on four GPUs with no NVLink between them Try it on whatever you have. Two GPUs are enough to see every one of these steps in action, and the log lines mean the same thing whether you are running 4 GPUs or 40. If you hit something we did not cover, tell us and we will add it. -**And check your interconnect before you buy anything.** Every number in Step 7 was measured on GPUs with no NVLink between them, so all 188 all-reduces per token crossed PCIe. That single fact is why pipeline parallelism managed to win first-token latency at all. On a machine with NVLink we would expect that win to vanish. `nvidia-smi topo -m` tells you which world you are in, and it is the first command we run on any new box. +**And check your interconnect before you buy anything.** Every number in Step 6 was measured on GPUs with no NVLink between them, so all 188 all-reduces per token crossed PCIe. That single fact is why pipeline parallelism managed to win first-token latency at all. On a machine with NVLink we would expect that win to vanish. `nvidia-smi topo -m` tells you which world you are in, and it is the first command we run on any new box. ## Credits and references diff --git a/lib/_blog-feed-data.js b/lib/_blog-feed-data.js index c1a2e1bee..67573a9ba 100644 --- a/lib/_blog-feed-data.js +++ b/lib/_blog-feed-data.js @@ -3,7 +3,7 @@ export const FEED_POSTS = [ { "slug": "running-a-big-llm-across-multiple-gpus-with-vllm", "title": "Running a big LLM across multiple GPUs with vLLM", - "description": "A runbook for serving a model too big for one GPU: download to serving in eight steps, with every vLLM flag, startup log line and real error explained, plus tensor, pipeline and expert parallelism benchmarked head to head on a 235B model across four RTX PRO 6000 cards.", + "description": "A runbook for serving a model too big for one GPU: download to serving in seven steps, with every vLLM flag, startup log line and real error explained, plus tensor, pipeline and expert parallelism benchmarked head to head on a 235B model across four RTX PRO 6000 cards.", "datePublished": "2026-08-31T10:00:00.000Z", "cover": "/img/blog/running-a-big-llm-across-multiple-gpus-with-vllm/cover.png", "tags": [ diff --git a/public/atom.xml b/public/atom.xml index 9c1a9e35e..01e3830a6 100644 --- a/public/atom.xml +++ b/public/atom.xml @@ -5,7 +5,7 @@ https://blog.kubesimplify.com/ - 2026-08-31T14:20:55.238Z + 2026-08-31T17:36:45.657Z Kubesimplify hello@kubesimplify.com @@ -16,7 +16,7 @@ https://blog.kubesimplify.com/running-a-big-llm-across-multiple-gpus-with-vllm 2026-08-31T10:00:00.000Z 2026-08-31T10:00:00.000Z -

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-GPU 0 -weights 30.59 GiB -KV cache 8.22 GiB -heads 0-31 - - - -GPU 1 -weights 30.59 GiB -KV cache 8.22 GiB -heads 32-63 - - - - - - -all- -reduce -GPU KV cache size: -67,296 tokens -2.05x concurrency - -QWEN3-32B BF16 - 61.02 GiB CHECKPOINT - vLLM 0.27.1 -2 x 128 all-reduces per token, no NVLink, measured not estimated -blog.kubesimplify.com - \ No newline at end of file diff --git a/public/llms-full.txt b/public/llms-full.txt index df6842053..a3207800f 100644 --- a/public/llms-full.txt +++ b/public/llms-full.txt @@ -5,6 +5,576 @@ --- +# Running a big LLM across multiple GPUs with vLLM + +- Canonical: https://blog.kubesimplify.com/running-a-big-llm-across-multiple-gpus-with-vllm +- Published: 2026-08-18 +- Summary: A plain-English guide to serving a model that is too big for one GPU: how tensor, pipeline, and expert parallelism split it up, what every vLLM flag does, and measured numbers from a 235B model on four RTX PRO 6000 cards. + +Sooner or later everyone running models locally hits the same wall. You find a model you want, you look at the download size, and it is bigger than the GPU you own. A 235B model needs roughly 236 GB just for its weights. The card we have holds 96 GB, and even the largest data-centre GPUs available today top out well below 236 GB. So the model does not fit, and no amount of clever flags will make 236 GB squeeze into 96 GB. + +The answer is to use more than one GPU. That part everybody knows. The part that is genuinely confusing is what "use more than one GPU" actually means. Does each GPU get a copy of the model? Does the model get cut in half? Do the GPUs take turns? Which of those is happening, and what does it cost you? + +Let's answer that properly, with a real model on real hardware, and let's explain every single flag and command along the way rather than pasting a magic incantation and moving on. + +## What you will learn + +- How to download a 236 GB model, what the 24 files you get actually are, and how they sit on disk +- How to work out on paper whether it fits on your GPUs, before you spend an hour downloading it +- What inference really is: the two completely different phases behind "time to first token" and "tokens per second" +- The three different ways a model can be split across GPUs, in plain English, and when each is used +- What every flag in our vLLM command does, and why it has the value it has +- How to read the startup log, which tells you more than any tutorial can +- The rules that limit how far you can split, and the real errors you get when you break them +- Measured numbers for all three splitting modes on the same model and the same four GPUs + +No prior knowledge of distributed computing is assumed. If you know what a GPU is and you have run a model locally once, you are qualified. + +## The machine and the model + +Here is what we tested on, because numbers mean nothing without the hardware attached. + +**The machine:** a server with 8x NVIDIA RTX PRO 6000 Blackwell Server Edition cards. Each card has 96 GB of memory, and the machine reports 95.01 GiB of that as usable. We borrowed 4 of the 8 cards for this work. + +One detail that matters more than it looks: these GPUs are **not** connected by NVLink. NVLink is NVIDIA's fast direct GPU-to-GPU cable. Without it, GPUs talk to each other over PCIe and through the CPU, which is slower. You can check what you have with one command: + +```bash +nvidia-smi topo -m +``` + +On our machine every pair of GPUs reports `SYS`, which means the traffic goes across PCIe and then across the link between the CPU sockets. If you had NVLink you would see `NV1`, `NV2` and so on instead. Keep this in mind, because it changes which splitting method is fastest. + +**The model:** `Qwen/Qwen3-235B-A22B-Instruct-2507-FP8`. Let's unpack that name, because it is doing a lot of work: + +- **235B** is the total parameter count, 235 billion. +- **A22B** means 22 billion **active** parameters. This is a mixture-of-experts model, and only a fraction of it runs for any given token. More on this shortly, because it is the most interesting thing about serving big models today. +- **FP8** is the number format the weights are stored in, 8 bits each, so one byte per parameter. + +**The software:** vLLM 0.27.1 running in the official container, with PyTorch 2.13.0 and CUDA 13.0, on driver 610.43.02. + +## Part 1: Getting the model onto the machine + +Before anything can be split across GPUs it has to be on the disk, and with a model this size that step is not a formality. It is the step that bit us hardest, so let's do it properly. + +You download it with the Hugging Face CLI: + +```bash +pip install huggingface_hub hf_transfer + +HF_HUB_ENABLE_HF_TRANSFER=1 hf download Qwen/Qwen3-235B-A22B-Instruct-2507-FP8 +``` + +`HF_HUB_ENABLE_HF_TRANSFER=1` switches on a Rust downloader that parallelises across connections. On a 236 GB download that is the difference between an hour and most of an afternoon, so it is worth the extra package. + +### What you actually get + +The download is not one giant file. It arrives as **24 shards**, plus the small text files that describe the model: + +``` +config.json +generation_config.json +model-00001-of-00024.safetensors +model-00002-of-00024.safetensors +... +model-00024-of-00024.safetensors +model.safetensors.index.json +tokenizer.json +``` + +A few things worth understanding here: + +- **`.safetensors`** is the modern format for weights. It is a flat file with a small JSON header at the front listing every tensor's name, dtype, shape and byte range, then the raw bytes. That layout matters for us, because it means a loader can memory-map the file and read exactly the byte ranges it wants without parsing the whole thing, and without the security problems of the old pickle-based `.bin` format. +- **`model.safetensors.index.json`** is the map that says which tensor lives in which shard. This is how vLLM knows to open shard 17 to find layer 62's weights. +- **`config.json`** is the architecture file we keep coming back to: layer count, head counts, expert count. It is a few kilobytes and it determines almost every decision in this post. +- For an FP8 model like this one, the weight tensors are joined by **scale tensors**. FP8 has very little numeric range, so the checkpoint stores a scaling factor per 128x128 block of each weight matrix, and the real value is the 8-bit number multiplied by its block's scale. You can see that arrangement declared in `config.json`: + +```json +"quantization_config": { + "quant_method": "fp8", + "fmt": "e4m3", + "weight_block_size": [128, 128], + "activation_scheme": "dynamic" +} +``` + +Remember those block scales. They are the reason for the most annoying crash we hit, back in Part 12. + +### Where it gets stored + +By default everything lands under `~/.cache/huggingface/hub`, in a layout that looks strange the first time you see it: + +``` +~/.cache/huggingface/hub/models--Qwen--Qwen3-235B-A22B-Instruct-2507-FP8/ +├── blobs/ <- the real files, named by hash +├── refs/ <- which commit "main" points at +└── snapshots/ + └── e156cb4e.../ <- symlinks with friendly names, pointing into blobs/ +``` + +The content lives once in `blobs/` under its hash, and `snapshots/` holds human-readable symlinks into it. That is why pulling two revisions of a model does not always double your disk usage, and it is also why `du` and `df` can disagree with your intuition. + +The practical consequence for serving: mount that whole directory into your container and set `HF_HOME` to it, which is exactly what the `-v` and `-e HF_HOME` flags in Part 8 are doing. Otherwise the container downloads its own copy. + +### The disk trap, which is a real production hazard + +Two things about disk that the model card will not tell you. + +**Each tensor-parallel worker reads the entire checkpoint.** vLLM's own docs say that with tensor parallelism "each process will read the whole model and split it into chunks". So at `-tp 4` the machine performs roughly 4 x 221 GiB of reads at startup, not 221 GiB divided four ways. That is why a big model takes minutes to load even off fast storage, and it is why our first `Model loading took` line reported 45 seconds only because a lot of the file was still in the operating system's page cache from the download. + +**On a shared machine, filling the disk can take down everything else on it.** This is the part we learned the hard way, and it is worth more than a footnote. Our test box also runs a Kubernetes inference platform. Kubernetes treats free disk as a managed resource called ephemeral-storage, and when free space fell below its eviction threshold, the kubelet did exactly what it is designed to do: it evicted pods to reclaim space, tainted the node so nothing new could schedule, and garbage-collected container images. Several of those images had been built locally and existed in no registry, so they could not simply be pulled again. + +Nothing about that is a Kubernetes bug, and nothing about it is specific to our setup. The lesson generalises: **before you download a quarter of a terabyte onto a machine, check what else lives on that disk and what will happen when it fills.** `df -h` before you start, and know your platform's eviction threshold, which is often far higher than "0 bytes free". If the machine is shared, keeping a couple of hundred gigabytes of headroom is not paranoia. + +## Part 2: Why one GPU is not enough + +Let's do the arithmetic, because it is simpler than people expect and it saves you a lot of wasted download time. + +A model is mostly a big pile of numbers called **parameters** or **weights**. To run the model, those numbers have to sit in GPU memory. So the first question is always: how many bytes is one parameter? + +| Format | Bits per parameter | Bytes per parameter | +| --- | --- | --- | +| FP32 | 32 | 4 | +| BF16 or FP16 | 16 | 2 | +| FP8 | 8 | 1 | +| FP4 or NVFP4 | 4 | 0.5 | + +So the weights alone take `number of parameters x bytes per parameter`. For our model that is 235 billion parameters at 1 byte each, which is about 236 GB. Our GPU holds 95.01 GiB. The model is roughly 2.3 times too big for one card. + +But weights are only the first of **three** things that need to fit. This is where most people's mental model is incomplete: + +1. **The weights.** Fixed size. You know it before you start. +2. **The KV cache.** This is the model's memory of the conversation so far. Every token you feed in, and every token the model writes, leaves behind a small record that has to be kept for as long as that request is alive. It grows with how long your prompts are and how many users you serve at once. +3. **Working space.** Temporary scratch memory for the actual calculations, plus some overhead the framework reserves for itself. + +The KV cache is the one that surprises people, so let's size it. The formula looks intimidating but every term is just a number from the model's config file: + +``` +bytes per token = 2 x layers x kv_heads x head_dim x bytes_per_number +``` + +The `2` is because you store two things per token, a key and a value, which is where "KV" comes from. For our model, `layers` is 94, `kv_heads` is 4, `head_dim` is 128, and the cache is kept in BF16 so that is 2 bytes: + +``` +2 x 94 x 4 x 128 x 2 = 192,512 bytes = 188 KiB per token +``` + +188 KiB does not sound like much. But this model supports a 262,144 token context, so one single conversation at full length would need `262,144 x 188 KiB`, which is about **47 GiB**. That is half a GPU for one user. Serving ten users at once with long prompts is where all your leftover memory goes, and it is why "the weights fit, so I am fine" is wrong. + +{{multi-gpu-memory-fit-animation}} + +## Part 3: What actually happens when a request arrives + +Before splitting anything, it helps to know what the work being split actually is, because inference is really two different jobs wearing one coat. Almost everything confusing about multi-GPU performance comes from this split. + +### Phase one: prefill, reading your prompt + +When your prompt arrives, the model has to read all of it. If you send 1,000 tokens, all 1,000 go through every layer **at once**, as one big batch of work. This is called **prefill**, and it is the phase that decides your time to first token. + +Prefill is *compute-heavy*. There is a lot of arithmetic to do and the GPU's matrix engines are the bottleneck. It also produces the keys and values for every one of those 1,000 tokens, which get written into the KV cache and kept. + +### Phase two: decode, writing the answer + +Then the model writes its reply, and here is the part that surprises people: **it can only produce one token at a time.** To write token 2 it needs to have written token 1, because it feeds its own output back in. There is no way around that, it is what "autoregressive" means. + +So decode is a loop. Each pass through it produces exactly one token, reads the entire KV cache built so far, and appends one more entry to that cache. + +Decode is *memory-heavy* rather than compute-heavy. For a single token there is barely any arithmetic to do, but the GPU still has to stream the relevant weights and the whole KV cache past its compute units. The bottleneck is memory bandwidth, not maths. That is why decode speed tracks memory bandwidth so closely, and why giving a single request more GPUs to read from in parallel actually helps. + +Two phases, two different bottlenecks, and they respond differently to everything you tune: + +| | Prefill | Decode | +| --- | --- | --- | +| Work per step | your whole prompt at once | exactly one token | +| Bottleneck | compute | memory bandwidth | +| Metric it drives | time to first token | time per output token | +| Data moved between GPUs | large, whole prompt's worth | tiny, one token's worth | + +That last row is the one to hold on to. It is the reason, later, that pipeline parallelism wins on first-token latency while tensor parallelism wins on tokens per second. The same all-reduce that is trivially cheap during decode is expensive during prefill, because it is carrying a thousand times more data. + +### How the server juggles many users + +A real server is not doing one request at a time. vLLM uses **continuous batching**, which means it does not wait for a batch to fill up or finish. On every step it looks at everything currently in flight and assembles whatever work is ready, so a request that arrives mid-flight joins the very next step rather than queueing behind a whole batch. + +Two consequences worth knowing: + +- **Prefill and decode get mixed together.** A step might carry one user's fresh 1,000-token prompt alongside twenty other users' single decode tokens. That mixing is why a burst of long prompts makes everyone else's tokens arrive more slowly, and it is why `--max-num-batched-tokens` exists as a lever. +- **Capacity is set by the KV cache, not by CPU or queue length.** Every in-flight request is holding cache proportional to its length. When the cache is full, vLLM has to **preempt** somebody: it evicts a request's cache and recomputes it later. That is the real meaning of the `Maximum concurrency` line in the startup log, and it is why we spend so much of this post counting cache bytes. + +Now that the work itself is clear, let's look at the three ways to spread it over more than one GPU. + +## Part 4: The three ways to split a model + +Here is the heart of it. When people say "split the model across GPUs" they could mean three genuinely different things, and mixing them up is the source of most confusion. + +An analogy first, because it makes the rest much easier to hold in your head. Imagine a large restaurant kitchen that has to produce one dish: + +- **Tensor parallelism** is four chefs all working on the same dish at the same time, one chopping, one on sauce, one on protein, one plating. They constantly have to coordinate, but the dish is done fast. +- **Pipeline parallelism** is four chefs at four stations, where the dish moves down the line. Station two cannot start until station one is finished. Very little talking, but three chefs are idle at any moment unless you have several dishes in flight. +- **Expert parallelism** is a kitchen with 128 specialist chefs where each dish only needs 8 of them. You spread those 128 chefs across four rooms, and each dish gets walked to whichever rooms hold the specialists it needs. + +{{multi-gpu-split-modes-animation}} + +All three can be combined, and in production they usually are. Now let's look at each one properly. + +## Part 5: Tensor parallelism, up close + +Tensor parallelism cuts **inside** every layer. This is the important distinction: it does not give GPU 0 the first half of the model and GPU 1 the second half. Every GPU holds a thin slice of **all 94 layers**. + +How can you cut a layer? Because the work a layer does is mostly one big multiplication table, and multiplication tables can be cut up. The technique comes from a 2019 NVIDIA paper called Megatron-LM, and it works in two moves. + +**Move one, cut the first matrix into vertical strips.** Each GPU takes some of the columns. Because each GPU has complete columns, it can finish its part, including the activation function in the middle, without asking anyone anything. In our model the attention block has 64 heads, so with 4 GPUs each one owns 16 whole heads and computes them start to finish alone. + +**Move two, cut the second matrix into horizontal strips.** These line up exactly with the vertical cuts from move one. Each GPU multiplies its slice and gets a **partial answer**, a quarter of the real result. + +Now, and only now, the GPUs have to talk. They add their four partial answers together so that everyone ends up with the complete result. That single operation is called an **all-reduce**: everyone contributes a piece, everyone gets the total back. + +The Megatron paper puts the cost plainly, saying this design lets you run a transformer layer "using only two all-reduces in the forward path". Generating text only uses the forward path, so: + +- 2 all-reduces per layer +- 94 layers +- **188 all-reduces to produce one single token** + +And they happen strictly one after another, because layer 5 cannot begin until layer 4 has finished comparing notes. + +{{multi-gpu-tensor-split-animation}} + +### The KV cache gets divided too, which is a bonus + +Because each GPU owns only some of the attention heads, it only needs to remember keys and values for its own heads. So the KV cache is divided across GPUs rather than duplicated. Four GPUs give you roughly four times the room for conversations, on top of making the weights fit. This is a real and often unmentioned benefit of tensor parallelism. + +## Part 6: The expert part, which is why this model is only 22B of work + +Our model is a **mixture of experts**, and this is the single biggest idea in how large models are served today, so it is worth slowing down for. + +In an ordinary model, every parameter is used for every token. In a mixture-of-experts model, each layer contains many small networks called **experts**, and a tiny component called a **router** decides which few of them each token should visit. Our model has **128 experts per layer** and the router picks **8** of them per token. + +So the model holds 235B parameters in memory, but only about 22B of them do any arithmetic for a given token. That is what "235B-A22B" means, and it is why this model runs far faster than its size suggests. You pay for the full 235B in memory and you pay for only 22B in speed. + +{{moe-expert-routing-animation}} + +This gives you a third way to split. Instead of slicing every expert into strips, you hand out whole experts: with 128 experts and 4 GPUs, each GPU keeps 32 of them intact. That is **expert parallelism**, and in vLLM you switch it on with `--enable-expert-parallel`. + +The trade is different from tensor parallelism. Nothing needs adding up at the end, but tokens have to travel to whichever GPU owns the expert they were routed to, and the answers travel back. It also has a fairness problem: the router does not promise to spread work evenly, so one GPU can end up with more popular experts and become the slow one holding everybody up. + +## Part 7: Every flag, explained + +Before the command, the vocabulary. Here is every flag we use and why it has the value it has. If you only remember one thing from this post, make it this table. + +| Flag | What it does | Why our value | +| --- | --- | --- | +| `--tensor-parallel-size 4` | How many GPUs to slice each layer across. Often shortened to `-tp`. | 236 GB of weights needs at least 3 cards of 95 GiB, and 4 divides the model's head counts cleanly. | +| `--pipeline-parallel-size 1` | How many groups to cut the layer stack into. Often `-pp`. | 1 means off. We test a version with 2 later. | +| `--enable-expert-parallel` | Hand out whole experts per GPU instead of slicing every expert. Mixture-of-experts models only. | Tested both ways, since this is exactly the choice a big MoE forces on you. | +| `--gpu-memory-utilization 0.90` | The fraction of each GPU's memory vLLM is allowed to claim, for weights plus KV cache plus working space. | 0.90 leaves a little headroom. Push it to 0.95 for more cache, but leave room or startup fails. | +| `--max-model-len 32768` | The longest single request, prompt plus reply, in tokens. | The model supports 262,144, but that would eat 47 GiB of cache for one user. 32,768 is a sane serving value. | +| `--max-num-seqs 32` | How many requests may be in flight at once. | Caps how much KV cache can be demanded simultaneously. Lower it if you see requests being preempted. | +| `--served-model-name qwen3-235b` | The name clients use in the API. | Otherwise clients must send the full checkpoint path. | +| `--port 8000` | Port for the OpenAI-compatible API. | Convention. | +| `--distributed-executor-backend mp` | How the GPU worker processes are managed: `mp` for plain Python multiprocessing, `ray` for a Ray cluster. | All 4 GPUs are in one machine, so `mp` is the simpler choice. `ray` is for multiple machines. | +| `--enforce-eager` | Skips building optimised CUDA graphs at startup. | We do **not** use it. It saves memory and starts faster, but generation is slower. Reach for it only if you are out of memory. | +| `--kv-cache-dtype fp8` | Stores the conversation cache at 8 bits instead of 16, roughly halving cache memory. | We left it at the default so our cache numbers are easy to check by hand. It is a good lever if you need more concurrency. | + +Two container flags matter just as much, and neither is a vLLM flag: + +| Docker flag | Why you need it | +| --- | --- | +| `--ipc=host` | The GPU workers are separate processes that pass data through shared memory. Docker's default 64 MB of shared memory is far too small, and leaving this out gives you a confusing hang at startup. | +| `--gpus '"device=1,4,5,6"'` | Hands specific GPUs to the container. The nested quoting is fussy but required. Inside the container they are renumbered 0 to 3. | + +## Part 8: The command, line by line + +Here is the whole thing. Every line is explained above, and we will walk the structure below it. + +```bash +docker run -d --name vllm-tp4 \ + --gpus '"device=1,4,5,6"' \ + --ipc=host \ + -p 8000:8000 \ + -v /root/.cache/huggingface:/root/.cache/huggingface \ + -e HF_HUB_OFFLINE=1 \ + -e HF_HOME=/root/.cache/huggingface \ + -e VLLM_USE_DEEP_GEMM=0 \ + vllm/vllm-openai:latest \ + Qwen/Qwen3-235B-A22B-Instruct-2507-FP8 \ + --served-model-name qwen3-235b \ + --tensor-parallel-size 4 \ + --gpu-memory-utilization 0.90 \ + --max-model-len 32768 \ + --max-num-seqs 32 \ + --port 8000 +``` + +Reading it top to bottom: + +- `docker run -d` starts the container in the background and prints its id. Drop the `-d` if you would rather watch the logs scroll past. +- `--name vllm-tp4` gives it a name so you can say `docker logs vllm-tp4` instead of copying an id. +- `-p 8000:8000` maps the container's port 8000 to the host's port 8000, so you can reach the API from outside. +- `-v /root/.cache/huggingface:/root/.cache/huggingface` shares your downloaded models with the container. Without it the container would download all 236 GB again. +- `-e HF_HUB_OFFLINE=1` tells the Hugging Face library not to phone home. It uses the local copy, which also means startup does not fail if the network is down. +- `vllm/vllm-openai:latest` is the image. Everything after it is passed to vLLM, because the image's entrypoint is already `vllm serve`. +- The first argument after the image is the model. Everything after that is a vLLM flag from the table above. +- `-e VLLM_USE_DEEP_GEMM=0` is here because without it this exact model would not start on these exact GPUs. It is not a general recommendation, and Part 12 explains the crash it avoids. If you are on different hardware, try without it first. + +One thing worth knowing about that entrypoint: because it is already `vllm serve`, running `docker run ... vllm/vllm-openai:latest python3 -c "..."` does **not** work the way you expect. Your Python gets handed to `vllm serve` as arguments and you get a confusing parse error. To run something else inside the image, override it: + +```bash +docker run --rm --gpus '"device=1,4"' --entrypoint python3 vllm/vllm-openai:latest -c " +import torch +print('GPUs visible:', torch.cuda.device_count()) +print('can GPU 0 talk to GPU 1 directly:', torch.cuda.can_device_access_peer(0, 1)) +" +``` + +That is a genuinely useful sanity check before you start a long model load, because it confirms the container can see the cards and that direct GPU-to-GPU access is available. + +## Part 9: How to read the startup log + +The startup log is the best teaching tool in the whole stack, and almost nobody reads it. Four lines tell you everything about whether your configuration is sensible. + +**Line one, how big the weights are per GPU.** You get one of these per worker: + +``` +(Worker_TP0) Model loading took X GiB +``` + +If you divide the full model size by your `--tensor-parallel-size` and get roughly this number, the split worked. If this number equals the **whole** model, something is wrong and you are not actually splitting. + +**Line two, what is left for conversations:** + +``` +Available KV cache memory: X GiB +``` + +If this is **negative**, your weights plus overhead already exceeded the budget, and vLLM will refuse to start. That is the clearest possible signal that you need more GPUs, a smaller number format, or a lower `--max-model-len`. + +**Line three, the cache in tokens:** + +``` +GPU KV cache size: N tokens +``` + +This is the total number of tokens the server can remember across all users at once. You can predict it: take the available cache memory, divide by the bytes-per-token figure we calculated in Part 2. + +**Line four, how many users that really means:** + +``` +Maximum concurrency for 32,768 tokens per request: N.NNx +``` + +This is the one to show your capacity planner. If it says `2.05x`, then two users can each have a full-length 32k conversation, and a third will have to wait or be preempted. It is simply the previous line divided by `--max-model-len`. + +## Part 10: The rules that limit how far you can split + +You cannot pick any number for `--tensor-parallel-size`. There are hard divisibility rules, and hitting them is a common early frustration. + +Because attention heads are handed out whole, **your tensor parallel size must divide the head counts**. Open the model's `config.json` and look: + +```json +{ + "num_hidden_layers": 94, + "hidden_size": 4096, + "num_attention_heads": 64, + "num_key_value_heads": 4, + "head_dim": 128, + "num_experts": 128, + "num_experts_per_tok": 8 +} +``` + +For our model: + +- `num_attention_heads` is 64, so 2, 4, 8, 16 all divide it cleanly. +- `num_key_value_heads` is **4**. This is the binding constraint. At `-tp 4` each GPU gets exactly one key/value head. At `-tp 8` there are not enough to go around, and vLLM has to duplicate them across GPUs, which wastes memory and gives you less benefit than you would hope. +- `num_experts` is 128, which divides evenly by 4 and by 8, so expert parallelism has more freedom than tensor parallelism here. + +That is the real lesson: **the KV head count, not the parameter count, usually decides how wide you can go.** It is the first thing we check on any new model, and it takes ten seconds. + +## Part 11: What we measured + +Once it was running, we compared all three ways of splitting the same model over the same 4 GPUs: tensor parallelism on its own, tensor parallelism plus expert parallelism, and pure pipeline parallelism. Same hardware, same flags otherwise, same benchmark. + +The benchmark is vLLM's own, 1024 tokens in and 256 tokens out per request, with `--ignore-eos` so every request generates exactly 256 tokens and the comparison is fair: + +```bash +docker exec vllm-tp4 vllm bench serve \ + --model Qwen/Qwen3-235B-A22B-Instruct-2507-FP8 \ + --served-model-name qwen3-235b \ + --base-url http://localhost:8000 \ + --dataset-name random --random-input-len 1024 --random-output-len 256 \ + --max-concurrency 1 --num-prompts 12 --seed 42 --ignore-eos +``` + +and then again with 32 requests in flight, which is the same command with two numbers changed: + +```bash + --max-concurrency 32 --num-prompts 128 +``` + +We ran both for every setup, because a single request at a time and 32 at a time behave completely differently, and a configuration that wins one can lose the other. + +### The memory side + +| | TP=4 | TP=4 plus EP | PP=4 | +| --- | --- | --- | --- | +| Weights per GPU | 55.19 GiB | 55.19 GiB | 55.70 GiB | +| KV cache per GPU | 27.85 GiB | 27.96 GiB | 26.84 GiB | +| Total KV cache | 621,392 tokens | **623,696 tokens** | 555,680 tokens | +| Max concurrency at 32k | 18.96x | **19.03x** | 16.96x | +| GPU memory used | 88,211 MiB on all 4 | 88,209 MiB on all 4 | 84,283 / 87,899 / 87,899 / 84,507 | + +Two things to pull out of that table. + +**Expert parallelism did not save memory.** It moved 0.37% of extra room into the cache, which is noise. If you were hoping expert parallelism would let you fit a model that otherwise does not fit, this is your warning that it will not. + +**Pipeline parallelism cost us 11.8% of the cache**, dropping from 621,392 tokens to 555,680, because a pipeline needs extra buffers for the activations travelling between stages, and that comes straight out of your conversation capacity. + +Look at the last row too. Under tensor parallelism all four cards sat at **exactly 88,211 MiB**, the same number on every one of them. Under pipeline parallelism they ranged from 84,283 to 87,899 MiB, about 3.6 GB apart, because a layer split cannot be perfectly even when 94 layers go over 4 GPUs and the ends of the model are not symmetric: the first stage carries the token embedding and the last carries the output head. That evenness check is the quickest sanity test you have that a tensor-parallel split is behaving. + +### The speed side + +| Measurement | TP=4 | TP=4 plus EP | PP=4 | Winner | +| --- | --- | --- | --- | --- | +| Median time per token, 1 request | **17.14 ms** | 18.83 ms | 21.19 ms | TP | +| Output tokens/sec, 32 requests | **503.68** | 470.93 | 296.48 | TP, by 70% over PP | +| Median time to first token, 32 requests | 3,233 ms | 3,705 ms | **2,735 ms** | PP, by 15% | +| Benchmark duration, 32 requests | **65.06 s** | 69.58 s | 110.52 s | TP | + +Tensor parallelism won nearly everything, and the size of one gap deserves attention: at 32 concurrent requests it produced **70% more tokens per second than pipeline parallelism**. That is not a rounding error, that is a different class of performance, and it lines up exactly with the theory from Part 4. Tensor parallelism has all four GPUs working on every token. Pipeline parallelism has each GPU working on a different request's stage, and with only 32 requests spread over 4 stages there is not enough in flight to keep everyone busy, so cards sit idle waiting for their turn. Its median time per token was 24% worse for the same reason. + +**Pipeline parallelism did win one thing, and it is the one theory predicts:** time to first token, by 15%. Processing your 1024-token prompt is where tensor parallelism's chatter gets expensive, because each of those 188 all-reduces is carrying the whole prompt's worth of data rather than a single token's. Pipeline parallelism just hands one activation tensor to the next stage and skips all of it. If your users judge you on how fast the first word appears, that is a real and measurable advantage. + +That is not a knock on expert parallelism, and it is important not to over-read it. Expert parallelism exists to solve a problem we do not have here: models so large that even a tensor-parallel split cannot hold all the experts, and clusters big enough that duplicating experts everywhere would be wasteful. With 4 GPUs and a model that already fits, we are asking it to do a job it was not designed for, and paying an extra network hop per token for nothing. On a 32 or 64 GPU deployment of a trillion-parameter model the answer would very likely flip. + +### The number we are throwing away, and why + +Being straight about this because it is a good lesson in reading your own benchmarks. The very first expert-parallel run at one-request-at-a-time reported **28.71 output tokens per second**, which would have made expert parallelism look catastrophic. It was not real. Look at the two TTFT figures from that run: + +``` +Mean TTFT (ms): 3987.38 +Median TTFT (ms): 265.56 +``` + +A mean fifteen times the median means one request behaved completely differently from the other eleven. One request stalled for about 45 seconds, almost certainly a one-off kernel compilation on the first pass through a code path, and that single stall stretched the whole benchmark from 63 seconds to 107 seconds. Since throughput is just tokens divided by wall-clock, one stall wrecked the headline number. + +This is why the table above uses **median time per token** as the decode measurement rather than aggregate throughput. Median per-token latency does not care that one request had a bad start. + +One more benchmarking trap while we are here. When we re-ran that same benchmark on the warm server, time to first token dropped from 265 ms to **61 ms**, which looks like a wonderful improvement and is actually meaningless: vLLM caches prompt prefixes by default, and we had just sent it those exact prompts with the same `--seed 42`. If you are comparing configurations, either vary the seed or turn prefix caching off, otherwise your second measurement is mostly measuring your cache. + +### What we would actually run + +For a 235B MoE on 4 GPUs with no NVLink between them, we would use plain `--tensor-parallel-size 4` and leave both of the others off. It was faster nearly everywhere, it gives the most conversation capacity, it splits memory perfectly evenly, and it is one less thing to reason about. + +We would reach for the other two in specific situations, not as general upgrades: + +- **Pipeline parallelism** if time to first token is the metric you are judged on, or if you are spanning multiple machines where the network between them is genuinely slow. It was 15% better at first-token latency and it barely uses the interconnect. +- **Expert parallelism** when the model is so large that even a tensor-parallel split cannot hold all the experts, which is a real problem at trillion-parameter scale and simply is not our problem at 235B on 4 cards. Here it cost 7% and returned nothing. + + +## Part 12: Errors you will actually hit + +Every one of these is a real message we collected while doing this, not a hypothetical. + +### "must be divisible by tensor parallel size" + +We asked for 3 GPUs, which is a perfectly reasonable-sounding thing to want, and got: + +``` +pydantic_core._pydantic_core.ValidationError: 1 validation error for VllmConfig + Value error, Total number of attention heads (64) must be divisible by tensor + parallel size (3). +``` + +**What it means:** the rule from Part 10. 64 heads cannot be shared out evenly among 3 GPUs. Good news, it fails in about a second, before loading a single byte of weights. + +**The fix:** pick a `--tensor-parallel-size` that divides your head count. Powers of two are the safe habit. + +### "Failed to load model - not enough GPU memory" + +Then we tried 2 GPUs, which puts about 110 GiB of weights on a 95 GiB card. It got most of the way through loading and then died: + +``` +ERROR [gpu_model_runner.py:5403] Failed to load model - not enough GPU memory. +Try lowering --gpu-memory-utilization to free memory for weights, increasing +--tensor-parallel-size, or using --quantization. +(original error: CUDA out of memory. Tried to allocate 768.00 MiB. GPU 0 has a +total capacity of 95.01 GiB of which 438.31 MiB is free. Including non-PyTorch +memory, this process has 94.57 GiB memory in use.) +``` + +**What it means:** exactly what it says. The weights for half this model do not fit on one of these cards. Note the useful detail in there, `438.31 MiB is free` out of `95.01 GiB`, so it filled the card almost exactly and then had nowhere to put the next 768 MiB chunk. + +**The fix:** vLLM lists the three real options itself, and for our case only one of them helps. Lowering `--gpu-memory-utilization` would make things worse, not better, because it reduces the space available for weights. Quantizing further would work but changes the model. So the answer is more GPUs, which is the whole point of this post. + +Worth knowing: this one is slow to fail, because it has to read and place most of the weights before it runs out. Budget several minutes, unlike the divisibility error which fails instantly. + +### "Unknown SF transformation", the one that cost us the most time + +This is the error we did not see coming, and it is worth the whole section. With 4 GPUs and everything sized correctly, all four workers died during startup: + +``` +RuntimeError: Assertion error (/workspace/.deps/deepgemm-src/csrc/apis/layout.hpp:60): +Unknown SF transformation +``` + +**What it means:** this model stores its FP8 weights in blocks, with a separate scale factor per 128x128 block, which you can see in its config as `"weight_block_size": [128, 128]`. vLLM hands that kind of matrix multiplication to a library called DeepGEMM, and DeepGEMM did not know how to lay out those scale factors ("SF" is scale factor) on our particular GPU. The RTX PRO 6000 is Blackwell, but it reports as `sm_120`, which is not the same silicon target as the data-centre Blackwell parts that DeepGEMM is usually exercised on. + +Notice how unhelpful the message is if you do not know that background. Nothing in it mentions FP8, quantization, or your GPU. + +**The fix**, which is one environment variable: + +```bash +docker run -d ... -e VLLM_USE_DEEP_GEMM=0 ... vllm/vllm-openai:latest ... +``` + +That tells vLLM to use its own FP8 kernels instead of DeepGEMM. Startup then went through cleanly. There is a performance cost to giving up a specialised kernel, so on hardware where DeepGEMM works you would leave it on. + +**The general lesson:** a quantized model is a contract between the checkpoint's format and a kernel that understands it. When a big quantized model fails to start on hardware that clearly has enough memory, suspect the kernel and the number format before you suspect your parallelism settings. + +### A confusing parse error when you try to run something else in the container + +``` +vllm serve: error: argument --compilation-config/-cc: Invalid JSON: expected value at line 2 +``` + +**What it means:** you ran `docker run ... vllm/vllm-openai:latest python3 -c "..."`, but the image's entrypoint is already `vllm serve`, so your Python source got handed to vLLM as a command-line argument. + +**The fix:** `--entrypoint python3`, as shown in Part 8. + +### "No available shared memory broadcast block found in 60 seconds" + +**What it means:** usually nothing. It shows up while vLLM is busy compiling or capturing CUDA graphs and the worker processes have not checked in for a minute. If it repeats forever and startup never finishes, then you probably forgot `--ipc=host` and the workers cannot pass data to each other through shared memory. + +**The fix:** add `--ipc=host`. If you already have it, wait a bit longer, because CUDA graph capture on a big model is genuinely slow. + + +## Wrapping up + +If you take five things away from this, let them be these. + +**One.** Inference is two jobs, not one. Prefill reads your whole prompt at once and is limited by compute; decode writes one token at a time and is limited by memory bandwidth. Every confusing multi-GPU result in this post traces back to that split, so when a change helps one metric and hurts the other, this is why. + +**Two.** Work out the memory on paper first. Parameters times bytes-per-parameter gives you the weights, and then remember that the weights are only one of three things that must fit, alongside the conversation cache and the working space. A model whose weights just barely fit is a model that cannot serve anybody. + +**Three.** "Splitting across GPUs" is three different things. Tensor parallelism slices every layer and makes all your GPUs work on the same token, at the cost of constant chatter. Pipeline parallelism cuts the layer stack into blocks and barely communicates, at the cost of GPUs waiting their turn. Expert parallelism only exists for mixture-of-experts models and hands out whole experts. You can combine them, and for big models you usually do. + +**Four.** Read the startup log. `Model loading took`, `Available KV cache memory`, `GPU KV cache size` and `Maximum concurrency` tell you, in four lines, whether your setup is sane and how many users it can actually hold. A negative cache number is the clearest error message in the whole stack. + +**Five.** Check `num_key_value_heads` in `config.json` before you plan your hardware. It, not the parameter count, is usually what limits how many GPUs you can split across cleanly. + +One last practical warning, because it cost us more than any GPU problem did. **Check your disk before you download.** A quarter of a terabyte of model weights on a shared machine is not just a storage question, it is a question about everything else living on that disk. Ours was a Kubernetes node, free space crossed the kubelet's eviction threshold, and it evicted the platform's own pods and garbage-collected locally-built images that no registry could replace. `df -h` first, and leave real headroom. + +Try it on whatever you have. Two GPUs are enough to see every concept in this post in action, and the log lines mean the same thing whether you are running 4 GPUs or 40. If you hit something we did not cover, tell us and we will add it. + +## Credits and references + +- The tensor parallel scheme is from **Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism** by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro: [arxiv.org/abs/1909.08053](https://arxiv.org/abs/1909.08053) +- vLLM parallelism and scaling guide: [docs.vllm.ai/en/latest/serving/parallelism_scaling.html](https://docs.vllm.ai/en/latest/serving/parallelism_scaling.html) +- vLLM memory and optimization docs: [conserving_memory](https://docs.vllm.ai/en/latest/configuration/conserving_memory.html) and [optimization](https://docs.vllm.ai/en/latest/configuration/optimization.html) +- Model card and config: [huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507-FP8](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507-FP8) +- Thanks to the vLLM maintainers, whose startup logging is the best free lesson in distributed inference available anywhere. + +--- + # The Local LLM Glossary: Every Term, Flag, and Number in Plain English - Canonical: https://blog.kubesimplify.com/local-llm-glossary diff --git a/public/llms.txt b/public/llms.txt index 6eae55e4c..b3ca302f7 100644 --- a/public/llms.txt +++ b/public/llms.txt @@ -4,7 +4,7 @@ ## About -Kubesimplify is a community-driven publication on cloud-native technologies, with 198 in-depth technical articles by 62 practitioner authors. We cover Kubernetes (kubelet internals, scheduling, networking, operators), container runtimes (containerd, CRI-O, Docker), GitOps (Argo CD, Flux), service meshes, observability, AI/ML infrastructure on Kubernetes, GPU workloads, platform engineering, and the broader CNCF ecosystem. +Kubesimplify is a community-driven publication on cloud-native technologies, with 199 in-depth technical articles by 62 practitioner authors. We cover Kubernetes (kubelet internals, scheduling, networking, operators), container runtimes (containerd, CRI-O, Docker), GitOps (Argo CD, Flux), service meshes, observability, AI/ML infrastructure on Kubernetes, GPU workloads, platform engineering, and the broader CNCF ecosystem. Authoritative, practitioner-written, citation-friendly. Articles include code examples, diagrams, and references. @@ -34,8 +34,9 @@ Authoritative, practitioner-written, citation-friendly. Articles include code ex - Cloud Native Security: https://blog.kubesimplify.com/hub/security (network policies, Falco, Kyverno, SLSA supply-chain) - Linux Fundamentals: https://blog.kubesimplify.com/hub/linux (shell, sysadmin, networking primitives) -## Recent posts (most recent 30 of 198) +## Recent posts (most recent 30 of 199) +- [Running a big LLM across multiple GPUs with vLLM](https://blog.kubesimplify.com/running-a-big-llm-across-multiple-gpus-with-vllm) (2026-08-18). A plain-English guide to serving a model that is too big for one GPU: how tensor, pipeline, and expert parallelism split it up, what every vLLM flag does, and measured numbers from a 235B model on four RTX PRO 6000 cards. - [The Local LLM Glossary: Every Term, Flag, and Number in Plain English](https://blog.kubesimplify.com/local-llm-glossary) (2026-08-18). Plain-English definitions for every term you hit in local LLM posts: prefill and decode, tokens per second, FP8 and NVFP4, Q4_K_M, KV cache, YaRN, Gated DeltaNet, speculative decoding, and every vLLM, llama.cpp, and Ollama flag worth knowing. - [Running Qwen3.8-27B on DGX Spark](https://blog.kubesimplify.com/qwen3-8-27b-on-dgx-spark) (2026-08-17). Qwen3.8-27B on DGX Spark with llama.cpp, Ollama, vLLM, and SGLang: the recipes, the tokens per second I measured, MTP speculative decoding, and the sharp edges I hit along the way. - [I Ran an AI SRE Copilot on My Own Hardware. Here Is What It Actually Does.](https://blog.kubesimplify.com/nudgebee-ai-sre-copilot-hands-on) (2026-08-17). Running NudgeBee v1.4.0 end to end - a self-hosted AIOps platform behind AI-SRE, AI-FinOps, AI-K8sOps, and agentic automation - on a Mac, a kiac cluster, and a DGX Spark. @@ -65,7 +66,6 @@ Authoritative, practitioner-written, citation-friendly. Articles include code ex - [Day 5: Docker Compose - How Docker Actually Gets Used](https://blog.kubesimplify.com/day-5-docker-compose-how-docker-actually-gets-used) (2026-04-28) - [What Actually Happens When kube-scheduler Picks a Node (13 Stages Inside Kubernetes)](https://blog.kubesimplify.com/kube-scheduler-deep-dive) (2026-04-28). How kube-scheduler picks a node: 13 framework stages, 14 Filter plugins, 9 Score plugins, live preemption demo. - [Day 4: Breaking Isolation on Purpose - Volumes, Networks, and the Real World](https://blog.kubesimplify.com/day-4-breaking-isolation-on-purpose-volumes-networks-and-the-real-world) (2026-04-27) -- [Day 3: Stop Writing Dockerfiles From Scratch](https://blog.kubesimplify.com/day-3-stop-writing-dockerfiles-from-scratch) (2026-04-24). Stop writing Dockerfiles from scratch. A Docker Captain walks through docker init, layer caching, multi-stage builds, and docker debug for 2026. ## Topics covered (auto-derived from tags) @@ -76,8 +76,8 @@ Authoritative, practitioner-written, citation-friendly. Articles include code ex - linux (19 articles): https://blog.kubesimplify.com/tag/linux - containers (17 articles): https://blog.kubesimplify.com/tag/containers - cloud (16 articles): https://blog.kubesimplify.com/tag/cloud -- nvidia (14 articles): https://blog.kubesimplify.com/tag/nvidia -- llm (12 articles): https://blog.kubesimplify.com/tag/llm +- nvidia (15 articles): https://blog.kubesimplify.com/tag/nvidia +- llm (13 articles): https://blog.kubesimplify.com/tag/llm - aws (12 articles): https://blog.kubesimplify.com/tag/aws - cloud-native (11 articles): https://blog.kubesimplify.com/tag/cloud-native - security (11 articles): https://blog.kubesimplify.com/tag/security @@ -85,15 +85,15 @@ Authoritative, practitioner-written, citation-friendly. Articles include code ex - go (9 articles): https://blog.kubesimplify.com/tag/go - git (9 articles): https://blog.kubesimplify.com/tag/git - linux-for-beginners (9 articles): https://blog.kubesimplify.com/tag/linux-for-beginners +- platform-engineering (8 articles): https://blog.kubesimplify.com/tag/platform-engineering - local-ai (8 articles): https://blog.kubesimplify.com/tag/local-ai - github (8 articles): https://blog.kubesimplify.com/tag/github - terraform (8 articles): https://blog.kubesimplify.com/tag/terraform - ai (7 articles): https://blog.kubesimplify.com/tag/ai -- platform-engineering (7 articles): https://blog.kubesimplify.com/tag/platform-engineering - docker-images (7 articles): https://blog.kubesimplify.com/tag/docker-images - kubesimplify (7 articles): https://blog.kubesimplify.com/tag/kubesimplify - linux-basics (7 articles): https://blog.kubesimplify.com/tag/linux-basics -- ollama (6 articles): https://blog.kubesimplify.com/tag/ollama +- gpu (6 articles): https://blog.kubesimplify.com/tag/gpu ## Top contributors @@ -102,8 +102,8 @@ Authoritative, practitioner-written, citation-friendly. Articles include code ex - [Kunal Verma](https://blog.kubesimplify.com/author/kunal-verma) (12 posts) - [Dipankar Das](https://blog.kubesimplify.com/author/dipankar-das) (9 posts) - [Anurag Kumar](https://blog.kubesimplify.com/author/anurag-kumar) (8 posts) +- [Shubham Katara](https://blog.kubesimplify.com/author/shubham-katara) (6 posts) - [sysxplore](https://blog.kubesimplify.com/author/sysxplore) (6 posts) -- [Shubham Katara](https://blog.kubesimplify.com/author/shubham-katara) (5 posts) - [Arnav Barman](https://blog.kubesimplify.com/author/arnav-barman) (5 posts) - [Srinivas Karnati](https://blog.kubesimplify.com/author/srinivas-karnati) (4 posts) - [Barkatul Mujauddin](https://blog.kubesimplify.com/author/barkatul-mujauddin) (4 posts) diff --git a/public/rss.xml b/public/rss.xml index 2300c9b4a..c59f965e1 100644 --- a/public/rss.xml +++ b/public/rss.xml @@ -6,8 +6,16 @@ Deep dives on Kubernetes, AI infrastructure, GitOps, and the cloud-native stack, written by practitioners. en-us - Tue, 18 Aug 2026 09:00:00 GMT + Tue, 18 Aug 2026 10:00:00 GMT Kubesimplify static blog + + Running a big LLM across multiple GPUs with vLLM + https://blog.kubesimplify.com/running-a-big-llm-across-multiple-gpus-with-vllm + https://blog.kubesimplify.com/running-a-big-llm-across-multiple-gpus-with-vllm + Tue, 18 Aug 2026 10:00:00 GMT + A plain-English guide to serving a model that is too big for one GPU: how tensor, pipeline, and expert parallelism split it up, what every vLLM flag does, and measured numbers from a 235B model on four RTX PRO 6000 cards. + vllmgpunvidiallmplatform-engineering + The Local LLM Glossary: Every Term, Flag, and Number in Plain English https://blog.kubesimplify.com/local-llm-glossary diff --git a/scripts/gen-local-llm-glossary-cover.mjs b/scripts/gen-local-llm-glossary-cover.mjs index 803b42205..45c0ec60a 100644 --- a/scripts/gen-local-llm-glossary-cover.mjs +++ b/scripts/gen-local-llm-glossary-cover.mjs @@ -1,5 +1,5 @@ // Excalidraw-style cover for the local LLM glossary post. -// Sketch helpers shared with scripts/gen-two-gpu-vllm-cover.mjs. +// Sketch helpers shared with scripts/gen-multi-gpu-vllm-cover.mjs. import { mkdirSync, writeFileSync } from 'node:fs'; import { join } from 'node:path'; diff --git a/scripts/gen-two-gpu-vllm-cover.mjs b/scripts/gen-multi-gpu-vllm-cover.mjs similarity index 60% rename from scripts/gen-two-gpu-vllm-cover.mjs rename to scripts/gen-multi-gpu-vllm-cover.mjs index 5b95a70e2..6c1be76af 100644 --- a/scripts/gen-two-gpu-vllm-cover.mjs +++ b/scripts/gen-multi-gpu-vllm-cover.mjs @@ -1,4 +1,4 @@ -// Excalidraw-style cover for the two-GPU vLLM article. +// Excalidraw-style cover for the multi-GPU vLLM article. // Sketch helpers shared with scripts/gen-hami-diagrams.mjs. import { mkdirSync, writeFileSync } from 'node:fs'; import { join } from 'node:path'; @@ -144,96 +144,85 @@ const W = 1200; const H = 630; const sketch = new Sketch(W, H, '#fdfdfb'); -// ── heading ────────────────────────────────────────────── -sketch.text(64, 84, 'One LLM, two GPUs', { size: 52, weight: 800, anchor: 'start' }); -sketch.text(64, 122, 'tensor parallelism splits every layer, not the stack', { - size: 23, +sketch.text(64, 82, 'One big model, four GPUs', { size: 50, weight: 800, anchor: 'start' }); +sketch.text(64, 119, 'how a 235B model is cut up so it fits, and what that costs', { + size: 22, color: COLORS.muted, anchor: 'start', }); -sketch.line(64, 142, 700, 142, { stroke: COLORS.muted, strokeWidth: 1.6, dashed: true }); +sketch.line(64, 139, 760, 139, { stroke: COLORS.muted, strokeWidth: 1.6, dashed: true }); -// ── left: one card fails ───────────────────────────────── -sketch.text(64, 190, 'ONE 45 GiB CARD', { size: 19, weight: 800, anchor: 'start', color: COLORS.red.stroke }); +// ── left: the model does not fit on one card ────────────── +sketch.text(64, 186, 'ONE CARD', { size: 18, weight: 800, anchor: 'start', color: COLORS.red.stroke }); -const boxY = 210; -sketch.rect(64, boxY, 300, 150, { stroke: COLORS.gray.stroke, fill: '#ffffff', hachure: false, dashed: true }); -sketch.text(214, boxY + 34, 'budget 40.47 GiB', { size: 17, color: COLORS.muted }); +const bY = 206; +sketch.rect(64, bY, 210, 132, { stroke: COLORS.gray.stroke, fill: '#ffffff', hachure: false, dashed: true }); +sketch.text(169, bY + 30, '95 GiB', { size: 19, color: COLORS.muted }); +sketch.text(169, bY + 54, 'usable', { size: 15, color: COLORS.muted }); -// the weights bar overflowing the box -sketch.rect(80, boxY + 52, 330, 62, { stroke: COLORS.red.stroke, fill: COLORS.red.fill }); -sketch.text(200, boxY + 80, 'weights 61.03 GiB', { size: 20, weight: 800, color: COLORS.red.stroke }); -sketch.text(200, boxY + 103, 'does not fit', { size: 16, color: COLORS.muted }); +// overflowing weights bar +sketch.rect(78, bY + 72, 330, 46, { stroke: COLORS.red.stroke, fill: COLORS.red.fill }); +sketch.text(200, bY + 95, '236 GB of weights', { size: 19, weight: 800, color: COLORS.red.stroke }); -sketch.text(64, boxY + 182, 'Available KV cache memory:', { size: 17, anchor: 'start', color: COLORS.muted }); -sketch.text(64, boxY + 208, '-24.42 GiB', { size: 30, weight: 800, anchor: 'start', color: COLORS.red.stroke }); +sketch.text(64, bY + 164, '2.3x too big', { size: 26, weight: 800, anchor: 'start', color: COLORS.red.stroke }); +sketch.text(64, bY + 192, 'no flag fixes this', { size: 16, anchor: 'start', color: COLORS.muted }); -// ── middle divider ─────────────────────────────────────── -sketch.line(470, 190, 470, 470, { stroke: COLORS.muted, strokeWidth: 1.6, dashed: true }); -sketch.text(470, 340, 'vs', { size: 26, weight: 800, color: COLORS.muted }); +// ── divider ─────────────────────────────────────────────── +sketch.line(452, 186, 452, 452, { stroke: COLORS.muted, strokeWidth: 1.6, dashed: true }); -// ── right: two cards work ──────────────────────────────── -sketch.text(560, 190, 'TWO CARDS, --tensor-parallel-size 2', { - size: 19, +// ── right: four cards, each holds a quarter ─────────────── +sketch.text(516, 186, 'FOUR CARDS, --tensor-parallel-size 4', { + size: 18, weight: 800, anchor: 'start', color: COLORS.teal.stroke, }); -const cardW = 246; -const cardGap = 84; -const gpuY = 210; -[0, 1].forEach((gpu) => { - const x = 560 + gpu * (cardW + cardGap); - const accent = gpu === 0 ? COLORS.blue : COLORS.green; - sketch.rect(x, gpuY, cardW, 150, { stroke: accent.stroke, fill: accent.fill }); - sketch.text(x + cardW / 2, gpuY + 34, `GPU ${gpu}`, { size: 22, weight: 800, color: accent.stroke }); - sketch.text(x + cardW / 2, gpuY + 66, 'weights 30.59 GiB', { size: 18, weight: 700 }); - sketch.text(x + cardW / 2, gpuY + 94, 'KV cache 8.22 GiB', { size: 17, color: COLORS.muted }); - sketch.text(x + cardW / 2, gpuY + 124, gpu === 0 ? 'heads 0-31' : 'heads 32-63', { - size: 16, - color: COLORS.muted, - }); +const cw = 145; +const gap = 10; +const gY = 206; +const palette = [COLORS.blue, COLORS.green, COLORS.violet, COLORS.orange]; +[0, 1, 2, 3].forEach((gpu) => { + const x = 516 + gpu * (cw + gap); + const c = palette[gpu]; + sketch.rect(x, gY, cw, 132, { stroke: c.stroke, fill: c.fill }); + sketch.text(x + cw / 2, gY + 30, `GPU ${gpu}`, { size: 20, weight: 800, color: c.stroke }); + sketch.text(x + cw / 2, gY + 60, '59 GB', { size: 18, weight: 700 }); + sketch.text(x + cw / 2, gY + 84, 'weights', { size: 14, color: COLORS.muted }); + sketch.text(x + cw / 2, gY + 112, '16 of 64 heads', { size: 13, color: COLORS.muted }); }); -// all-reduce link between the two cards -const gapL = 560 + cardW + 10; -const gapR = 560 + cardW + cardGap - 10; -const gapMid = (gapL + gapR) / 2; -const linkY = gpuY + 66; -sketch.arrow(gapL, linkY, gapR, linkY, { stroke: COLORS.violet.stroke }); -sketch.arrow(gapR, linkY + 24, gapL, linkY + 24, { stroke: COLORS.violet.stroke }); -sketch.text(gapMid, gapMid && linkY + 60, 'all-', { size: 15, weight: 700, color: COLORS.violet.stroke }); -sketch.text(gapMid, linkY + 80, 'reduce', { size: 15, weight: 700, color: COLORS.violet.stroke }); - -sketch.text(560, gpuY + 182, 'GPU KV cache size:', { size: 17, anchor: 'start', color: COLORS.muted }); -sketch.text(560, gpuY + 208, '67,296 tokens', { - size: 30, +// all-reduce arrows under the row of cards +const arrowY = gY + 154; +sketch.line(516 + 40, arrowY, 516 + 3 * (cw + gap) + cw - 40, arrowY, { + stroke: COLORS.violet.stroke, + dashed: true, +}); +sketch.text(516 + (3 * (cw + gap) + cw) / 2, arrowY + 30, '188 all-reduces per token', { + size: 18, weight: 800, - anchor: 'start', - color: COLORS.teal.stroke, + color: COLORS.violet.stroke, }); -sketch.text(830, gpuY + 208, '2.05x concurrency', { size: 18, anchor: 'start', color: COLORS.muted }); -// ── footer strip ───────────────────────────────────────── -sketch.line(64, 520, W - 64, 520, { stroke: COLORS.muted, strokeWidth: 1.6 }); -sketch.text(64, 556, 'QWEN3-32B BF16 - 61.02 GiB CHECKPOINT - vLLM 0.27.1', { - size: 19, +// ── footer ──────────────────────────────────────────────── +sketch.line(64, 516, W - 64, 516, { stroke: COLORS.muted, strokeWidth: 1.6 }); +sketch.text(64, 552, 'QWEN3-235B-A22B FP8 - 128 EXPERTS, 8 PER TOKEN - vLLM 0.27.1', { + size: 18, weight: 800, anchor: 'start', color: COLORS.ink, }); -sketch.text(64, 586, '2 x 128 all-reduces per token, no NVLink, measured not estimated', { - size: 17, +sketch.text(64, 582, 'tensor, pipeline and expert parallelism explained in plain english', { + size: 16, anchor: 'start', color: COLORS.muted, }); -sketch.text(W - 64, 586, 'blog.kubesimplify.com', { - size: 17, +sketch.text(W - 64, 582, 'blog.kubesimplify.com', { + size: 16, weight: 700, anchor: 'end', color: COLORS.muted, }); sketch.save(join(output, 'cover.svg')); -console.log(`Wrote two-GPU vLLM cover to ${output}`); +console.log(`Wrote multi-GPU vLLM cover to ${output}`); diff --git a/vercel.json b/vercel.json index eaa7bd155..2e0044f80 100644 --- a/vercel.json +++ b/vercel.json @@ -906,6 +906,11 @@ "destination": "https://blog.kubesimplify.com/ready-for-wasm-day-2023", "permanent": true }, + { + "source": "/blog/running-a-big-llm-across-multiple-gpus-with-vllm", + "destination": "https://blog.kubesimplify.com/running-a-big-llm-across-multiple-gpus-with-vllm", + "permanent": true + }, { "source": "/blog/sharing-gpus-in-kubernetes-with-hami", "destination": "https://blog.kubesimplify.com/sharing-gpus-in-kubernetes-with-hami", @@ -2916,6 +2921,17 @@ } ] }, + { + "source": "/running-a-big-llm-across-multiple-gpus-with-vllm", + "destination": "https://blog.kubesimplify.com/running-a-big-llm-across-multiple-gpus-with-vllm", + "permanent": true, + "has": [ + { + "type": "host", + "value": "kubesimplify.com" + } + ] + }, { "source": "/sharing-gpus-in-kubernetes-with-hami", "destination": "https://blog.kubesimplify.com/sharing-gpus-in-kubernetes-with-hami", From a6ed090cd5e809e0289237618f67aa0efa9b259f Mon Sep 17 00:00:00 2001 From: Saiyam Pathak Date: Tue, 18 Aug 2026 17:15:29 +0530 Subject: [PATCH 04/19] Answer the shard question: fixed at upload, and why 5-10GB is the norm A reader asked whether the shard count can be changed at download time and whether there is a standard. Adds two subsections to Part 1: shards are fixed by the publisher and recorded in model.safetensors.index.json, re-sharding is a local save_pretrained(max_shard_size=...) operation, and the Hub's <200GB recommendation plus 500GB hard limit explain why publishers land around 5-10GB. This model uses a 10GB cap: 23 shards of exactly 10.00 GB plus a 6.45 GB remainder. Also notes that shard count does not affect serving, because safetensors are memory-mapped. Signed-off-by: Saiyam Pathak --- ...-big-llm-across-multiple-gpus-with-vllm.md | 32 +++++++++++++++++++ 1 file changed, 32 insertions(+) diff --git a/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md b/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md index f324c45ec..ce003755c 100644 --- a/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md +++ b/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md @@ -98,6 +98,38 @@ A few things worth understanding here: Remember those block scales. They are the reason for the most annoying crash we hit, back in Part 12. +### Can you change the number of shards? + +Worth answering because it is a natural question: no, not at download time. The shard layout is decided by whoever uploaded the model and is baked into `model.safetensors.index.json`. `hf download` just fetches the files that exist in the repo, so there is no flag to ask for more or fewer of them. + +You can only re-shard by loading the model yourself and saving it again, which is a local operation on your own copy: + +```python +from transformers import AutoModelForCausalLM + +model = AutoModelForCausalLM.from_pretrained("some/model") +model.save_pretrained("./resharded", max_shard_size="5GB") +``` + +`max_shard_size` is the knob, and in current `transformers` it defaults to `"50GB"`. One caveat straight from its docs, because it surprises people: "If a single weight of the model is bigger than `max_shard_size`, it will be in its own checkpoint shard which will be bigger than `max_shard_size`." A giant embedding matrix can therefore blow past whatever cap you set. + +For a 235B model this is almost never worth doing, since you would have to load the whole thing to write it back out. Just take the shards you are given. + +### Is there a standard shard size? + +Not a formal one, but there are firm conventions and real limits. + +**The conventions** are the naming pattern (`model-00001-of-00024.safetensors`) and the index file next to it. Both are produced automatically by the saving code, which is why nearly every model on the Hub looks the same. + +**The limits** come from the Hub. Its guidance is to split large files "into chunks <200GB each", and it states that "500GB is the hard limit for a single file size". The reasoning is practical and worth knowing, because it is the same reasoning that should shape your own thinking about big files: + +- A failed download of a smaller file resumes cheaply. A failed download of one enormous file can mean starting over. +- Files are served through a CDN, and per the Hub's docs "huge files are not cached by this service leading to a slower download speed". So one 236 GB file would genuinely download slower than 24 pieces of it. + +**What publishers actually pick** sits far below those limits. Our model uses a 10 GB cap: 23 shards of exactly 10.00 GB and a 24th holding the remaining 6.45 GB. Somewhere in the 5 to 10 GB range is the common choice across the Hub. + +**Does any of this affect serving?** Essentially no. Shard count does not change how much GPU memory you need or how fast the model runs, because the weights are identical either way and safetensors are memory-mapped, so the loader reads the byte ranges it wants regardless of how they are grouped into files. Shard size is a distribution question, not an inference question. Where it does matter is download throughput and resumability, which is exactly why the convention landed where it did. + ### Where it gets stored By default everything lands under `~/.cache/huggingface/hub`, in a layout that looks strange the first time you see it: From 81bd69cf9c60c09eb9dfc2e21f0a083950f05d01 Mon Sep 17 00:00:00 2001 From: Saiyam Pathak Date: Tue, 18 Aug 2026 17:15:48 +0530 Subject: [PATCH 05/19] Regenerate feeds for the shard section Signed-off-by: Saiyam Pathak --- public/atom.xml | 2 +- public/llms-full.txt | 32 ++++++++++++++++++++++++++++++++ 2 files changed, 33 insertions(+), 1 deletion(-) diff --git a/public/atom.xml b/public/atom.xml index 63892a840..bfce4fca5 100644 --- a/public/atom.xml +++ b/public/atom.xml @@ -5,7 +5,7 @@ https://blog.kubesimplify.com/ - 2026-08-18T11:33:53.748Z + 2026-08-18T11:45:29.744Z Kubesimplify hello@kubesimplify.com diff --git a/public/llms-full.txt b/public/llms-full.txt index a3207800f..150745f31 100644 --- a/public/llms-full.txt +++ b/public/llms-full.txt @@ -99,6 +99,38 @@ A few things worth understanding here: Remember those block scales. They are the reason for the most annoying crash we hit, back in Part 12. +### Can you change the number of shards? + +Worth answering because it is a natural question: no, not at download time. The shard layout is decided by whoever uploaded the model and is baked into `model.safetensors.index.json`. `hf download` just fetches the files that exist in the repo, so there is no flag to ask for more or fewer of them. + +You can only re-shard by loading the model yourself and saving it again, which is a local operation on your own copy: + +```python +from transformers import AutoModelForCausalLM + +model = AutoModelForCausalLM.from_pretrained("some/model") +model.save_pretrained("./resharded", max_shard_size="5GB") +``` + +`max_shard_size` is the knob, and in current `transformers` it defaults to `"50GB"`. One caveat straight from its docs, because it surprises people: "If a single weight of the model is bigger than `max_shard_size`, it will be in its own checkpoint shard which will be bigger than `max_shard_size`." A giant embedding matrix can therefore blow past whatever cap you set. + +For a 235B model this is almost never worth doing, since you would have to load the whole thing to write it back out. Just take the shards you are given. + +### Is there a standard shard size? + +Not a formal one, but there are firm conventions and real limits. + +**The conventions** are the naming pattern (`model-00001-of-00024.safetensors`) and the index file next to it. Both are produced automatically by the saving code, which is why nearly every model on the Hub looks the same. + +**The limits** come from the Hub. Its guidance is to split large files "into chunks <200GB each", and it states that "500GB is the hard limit for a single file size". The reasoning is practical and worth knowing, because it is the same reasoning that should shape your own thinking about big files: + +- A failed download of a smaller file resumes cheaply. A failed download of one enormous file can mean starting over. +- Files are served through a CDN, and per the Hub's docs "huge files are not cached by this service leading to a slower download speed". So one 236 GB file would genuinely download slower than 24 pieces of it. + +**What publishers actually pick** sits far below those limits. Our model uses a 10 GB cap: 23 shards of exactly 10.00 GB and a 24th holding the remaining 6.45 GB. Somewhere in the 5 to 10 GB range is the common choice across the Hub. + +**Does any of this affect serving?** Essentially no. Shard count does not change how much GPU memory you need or how fast the model runs, because the weights are identical either way and safetensors are memory-mapped, so the loader reads the byte ranges it wants regardless of how they are grouped into files. Shard size is a distribution question, not an inference question. Where it does matter is download throughput and resumability, which is exactly why the convention landed where it did. + ### Where it gets stored By default everything lands under `~/.cache/huggingface/hub`, in a layout that looks strange the first time you see it: From 2eb05a89470c3f6376653fea5ba90409aec3de94 Mon Sep 17 00:00:00 2001 From: Shubham Katara Date: Sun, 23 Aug 2026 10:30:48 +0200 Subject: [PATCH 06/19] Restructure the post into a runbook track and a deep-dive track so action-focused and theory-focused readers each get a direct path through it --- ...-big-llm-across-multiple-gpus-with-vllm.md | 714 +++++++++++------- 1 file changed, 437 insertions(+), 277 deletions(-) diff --git a/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md b/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md index ce003755c..3d6af2c7f 100644 --- a/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md +++ b/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md @@ -1,7 +1,7 @@ --- title: "Running a big LLM across multiple GPUs with vLLM" seoTitle: "Running a big LLM across multiple GPUs with vLLM" -seoDescription: "A plain-English guide to serving a model that is too big for one GPU: how tensor, pipeline, and expert parallelism split it up, what every vLLM flag does, and measured numbers from a 235B model on four RTX PRO 6000 cards." +seoDescription: "A plain-English guide to serving a model too big for one GPU, in two tracks: a runbook from download to serving with every flag and error explained, and a deep dive into how tensor, pipeline, and expert parallelism split the model, with measured numbers from a 235B model on four RTX PRO 6000 cards." datePublished: 2026-08-18T10:00:00.000Z slug: running-a-big-llm-across-multiple-gpus-with-vllm author: shubham-katara @@ -14,31 +14,57 @@ Sooner or later everyone running models locally hits the same wall. You find a m The answer is to use more than one GPU. That part everybody knows. The part that is genuinely confusing is what "use more than one GPU" actually means. Does each GPU get a copy of the model? Does the model get cut in half? Do the GPUs take turns? Which of those is happening, and what does it cost you? -Let's answer that properly, with a real model on real hardware, and let's explain every single flag and command along the way rather than pasting a magic incantation and moving on. +Let's answer that properly, with a real model on real hardware. And let's be honest that not everyone is here for the same reason. -## What you will learn +## How to read this post -- How to download a 236 GB model, what the 24 files you get actually are, and how they sit on disk -- How to work out on paper whether it fits on your GPUs, before you spend an hour downloading it -- What inference really is: the two completely different phases behind "time to first token" and "tokens per second" -- The three different ways a model can be split across GPUs, in plain English, and when each is used -- What every flag in our vLLM command does, and why it has the value it has -- How to read the startup log, which tells you more than any tutorial can -- The rules that limit how far you can split, and the real errors you get when you break them -- Measured numbers for all three splitting modes on the same model and the same four GPUs +First, why this post is shaped the way it is. Getting a big model serving and understanding how the serving works are two different jobs, usually done by two different people, or by the same person on two different days. An earlier version of this post ran both together, and that made it dense in exactly the wrong way: the reader with a deadline had to wade through all-reduce mechanics to reach the next command, and the reader who came for the mechanics kept tripping over Docker flags. We considered splitting it into two separate posts, but the deep dive's benchmark numbers come from the runbook's commands, and evidence belongs next to the thing it proves. -No prior knowledge of distributed computing is assumed. If you know what a GPU is and you have run a model locally once, you are qualified. +So: one post, two tracks, each with a clear exit. Pick your entrance based on the job in front of you: + +| You are | You want | Read | +| --- | --- | --- | +| **Platform engineer, SRE, MLOps**: you have the GPUs and a deadline | The model serving today | **The runbook, Steps 1-8** (~20 min). Every command, flag, log line and error. Each step links into the deep dive at exactly the point a "why" earns its keep; follow those links only when something surprises you. | +| **ML engineer, or just curious**: no root access required | The mental model | **The deep dive, sections 1-7** (~18 min). How the splitting actually works, and measured proof of when each method wins. Jump [straight there](#the-deep-dive-what-splitting-actually-means). | +| **Both** | Everything | Read straight through. The runbook comes first because you cannot benchmark a server that is not running. | + +New to the jargon? Every term, flag, and benchmark number here is explained in plain English in the [local LLM glossary](https://blog.kubesimplify.com/local-llm-glossary). ## The machine and the model -Here is what we tested on, because numbers mean nothing without the hardware attached. +Both tracks lean on this section, so here it is once. Numbers mean nothing without the hardware attached. **The machine:** a server with 8x NVIDIA RTX PRO 6000 Blackwell Server Edition cards. Each card has 96 GB of memory, and the machine reports 95.01 GiB of that as usable. We borrowed 4 of the 8 cards for this work. One detail that matters more than it looks: these GPUs are **not** connected by NVLink. NVLink is NVIDIA's fast direct GPU-to-GPU cable. Without it, GPUs talk to each other over PCIe and through the CPU, which is slower. You can check what you have with one command: ```bash -nvidia-smi topo -m +root@utho-gpu-rtxpro6000-8-62383:~# nvidia-smi topo -m + +| Device | GPU0 | GPU1 | GPU2 | GPU3 | GPU4 | GPU5 | GPU6 | GPU7 | NIC0 | CPU Affinity | NUMA Affinity | GPU NUMA ID | +| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :--- | :---: | :---: | +| **GPU0** | **X** | SYS | SYS | SYS | SYS | SYS | SYS | SYS | SYS | 48-55,176-183 | 6 | N/A | +| **GPU1** | SYS | **X** | SYS | SYS | SYS | SYS | SYS | SYS | PHB | 32-39,160-167 | 4 | N/A | +| **GPU2** | SYS | SYS | **X** | SYS | SYS | SYS | SYS | SYS | SYS | 0-7,128-135 | 0 | N/A | +| **GPU3** | SYS | SYS | SYS | **X** | SYS | SYS | SYS | SYS | SYS | 16-23,144-151 | 2 | N/A | +| **GPU4** | SYS | SYS | SYS | SYS | **X** | SYS | SYS | SYS | SYS | 112-119,240-247 | 14 | N/A | +| **GPU5** | SYS | SYS | SYS | SYS | SYS | **X** | SYS | SYS | SYS | 96-103,224-231 | 12 | N/A | +| **GPU6** | SYS | SYS | SYS | SYS | SYS | SYS | **X** | SYS | SYS | 64-71,192-199 | 8 | N/A | +| **GPU7** | SYS | SYS | SYS | SYS | SYS | SYS | SYS | **X** | SYS | 80-87,208-215 | 10 | N/A | +| **NIC0** | SYS | PHB | SYS | SYS | SYS | SYS | SYS | SYS | **X** | | | | + +**Legend:** + +| Symbol | Description | +| :--- | :--- | +| **X** | Self | +| **SYS** | Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI) | +| **NODE** | Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node | +| **PHB** | Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU) | +| **PXB** | Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge) | +| **PIX** | Connection traversing at most a single PCIe bridge | +| **NV#** | Connection traversing a bonded set of `#` NVLinks | +| **NIC0** | `mlx4_0` | ``` On our machine every pair of GPUs reports `SYS`, which means the traffic goes across PCIe and then across the link between the CPU sockets. If you had NVLink you would see `NV1`, `NV2` and so on instead. Keep this in mind, because it changes which splitting method is fastest. @@ -46,24 +72,33 @@ On our machine every pair of GPUs reports `SYS`, which means the traffic goes ac **The model:** `Qwen/Qwen3-235B-A22B-Instruct-2507-FP8`. Let's unpack that name, because it is doing a lot of work: - **235B** is the total parameter count, 235 billion. -- **A22B** means 22 billion **active** parameters. This is a mixture-of-experts model, and only a fraction of it runs for any given token. More on this shortly, because it is the most interesting thing about serving big models today. +- **A22B** means 22 billion **active** parameters. This is a mixture-of-experts model: each layer holds 128 small expert networks and a router picks just 8 of them per token, so you pay for 235B in memory but only about 22B in arithmetic. [Deep dive 5 tells the full story.](#deep-dive-5-the-expert-part) - **FP8** is the number format the weights are stored in, 8 bits each, so one byte per parameter. **The software:** vLLM 0.27.1 running in the official container, with PyTorch 2.13.0 and CUDA 13.0, on driver 610.43.02. -## Part 1: Getting the model onto the machine +--- + +## The runbook: from download to serving + +Written for the person with root on the box. Eight steps, and at the end of them a 235B model is answering requests on four GPUs. No prior knowledge of distributed computing is assumed: if you know what a GPU is and you have run a model locally once, you are qualified. + +## Step 1: Getting the model onto the machine Before anything can be split across GPUs it has to be on the disk, and with a model this size that step is not a formality. It is the step that bit us hardest, so let's do it properly. You download it with the Hugging Face CLI: ```bash -pip install huggingface_hub hf_transfer +root@utho-gpu-rtxpro6000-8-62383:~# pip install huggingface_hub hf_transfer +root@utho-gpu-rtxpro6000-8-62383:~# HF_XET_HIGH_PERFORMANCE=1 hf download Qwen/Qwen3-235B-A22B-Instruct-2507-FP8 +Downloading bytes: ████████████████████████████████████████████████▏ | 24.4GB, 234MB/s +Reconstructing (incomplete total...): 13%|███████████████▋ | 10.0GB / 80.0GB, 104MB/s +Fetching 34 files: 0%| | 0/34 [00:00 Date: Sun, 23 Aug 2026 19:19:18 +0200 Subject: [PATCH 07/19] updated benchmark and info in right places --- ...-big-llm-across-multiple-gpus-with-vllm.md | 166 ++++++++++-------- 1 file changed, 89 insertions(+), 77 deletions(-) diff --git a/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md b/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md index 3d6af2c7f..896f7f59d 100644 --- a/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md +++ b/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md @@ -18,15 +18,17 @@ Let's answer that properly, with a real model on real hardware. And let's be hon ## How to read this post -First, why this post is shaped the way it is. Getting a big model serving and understanding how the serving works are two different jobs, usually done by two different people, or by the same person on two different days. An earlier version of this post ran both together, and that made it dense in exactly the wrong way: the reader with a deadline had to wade through all-reduce mechanics to reach the next command, and the reader who came for the mechanics kept tripping over Docker flags. We considered splitting it into two separate posts, but the deep dive's benchmark numbers come from the runbook's commands, and evidence belongs next to the thing it proves. +This post is split into two tracks: a runbook for getting a big model serving, and a deep dive explaining how multi-GPU model splitting actually works. The runbook is for readers who need the commands and configs fast. + +The deep dive is for those who want to understand the mechanics, tradeoffs, and numbers. Jump to the track that fits your need, or read both: the post is structured so each section clearly points to the other right when extra context is helpful. So: one post, two tracks, each with a clear exit. Pick your entrance based on the job in front of you: -| You are | You want | Read | -| --- | --- | --- | +| You are | You want | Read | +| ------------------------------------------------------------------- | ----------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Platform engineer, SRE, MLOps**: you have the GPUs and a deadline | The model serving today | **The runbook, Steps 1-8** (~20 min). Every command, flag, log line and error. Each step links into the deep dive at exactly the point a "why" earns its keep; follow those links only when something surprises you. | -| **ML engineer, or just curious**: no root access required | The mental model | **The deep dive, sections 1-7** (~18 min). How the splitting actually works, and measured proof of when each method wins. Jump [straight there](#the-deep-dive-what-splitting-actually-means). | -| **Both** | Everything | Read straight through. The runbook comes first because you cannot benchmark a server that is not running. | +| **ML engineer, or just curious**: no root access required | The mental model | **The deep dive, sections 1-7** (~18 min). How the splitting actually works, and measured proof of when each method wins. Jump [straight there](#the-deep-dive-what-splitting-actually-means). | +| **Both** | Everything | Read straight through. The runbook comes first because you cannot benchmark a server that is not running. | New to the jargon? Every term, flag, and benchmark number here is explained in plain English in the [local LLM glossary](https://blog.kubesimplify.com/local-llm-glossary). @@ -92,8 +94,8 @@ You download it with the Hugging Face CLI: ```bash root@utho-gpu-rtxpro6000-8-62383:~# pip install huggingface_hub hf_transfer root@utho-gpu-rtxpro6000-8-62383:~# HF_XET_HIGH_PERFORMANCE=1 hf download Qwen/Qwen3-235B-A22B-Instruct-2507-FP8 -Downloading bytes: ████████████████████████████████████████████████▏ | 24.4GB, 234MB/s -Reconstructing (incomplete total...): 13%|███████████████▋ | 10.0GB / 80.0GB, 104MB/s +Downloading bytes: ████████████████████████████████████████████████▏ | 24.4GB, 234MB/s +Reconstructing (incomplete total...): 13%|███████████████▋ | 10.0GB / 80.0GB, 104MB/s Fetching 34 files: 0%| | 0/34 [00:00 Date: Sun, 23 Aug 2026 20:06:10 +0200 Subject: [PATCH 08/19] updated benchmark and info in right places --- ...-big-llm-across-multiple-gpus-with-vllm.md | 39 ++++++++++--------- 1 file changed, 20 insertions(+), 19 deletions(-) diff --git a/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md b/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md index 896f7f59d..644fe69e7 100644 --- a/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md +++ b/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md @@ -417,37 +417,38 @@ and then again with 32 requests in flight, which is the same command with two nu ```bash --max-concurrency 32 --num-prompts 640 +Starting initial single prompt test run... Skipping endpoint ready check. Starting main benchmark run... Traffic request rate: inf Burstiness factor: 1.0 (Poisson process) -Maximum request concurrency: 33 -100%|██████████| 640/640 [05:22<00:00, 1.99it/s] +Maximum request concurrency: 32 +100%|██████████| 640/640 [05:23<00:00, 1.98it/s] tip: install termplotlib and gnuplot to plot the metrics ============ Serving Benchmark Result ============ Successful requests: 640 Failed requests: 0 -Maximum request concurrency: 33 -Benchmark duration (s): 322.25 +Maximum request concurrency: 32 +Benchmark duration (s): 323.10 Total input tokens: 655360 Total generated tokens: 163840 -Request throughput (req/s): 1.99 -Output token throughput (tok/s): 508.43 +Request throughput (req/s): 1.98 +Output token throughput (tok/s): 507.09 Peak output token throughput (tok/s): 960.00 -Peak concurrent requests: 64.00 -Total token throughput (tok/s): 2542.16 +Peak concurrent requests: 55.00 +Total token throughput (tok/s): 2535.46 ---------------Time to First Token---------------- -Mean TTFT (ms): 2690.29 -Median TTFT (ms): 1967.72 -P99 TTFT (ms): 13101.50 +Mean TTFT (ms): 3176.38 +Median TTFT (ms): 3211.10 +P99 TTFT (ms): 6855.46 -----Time per Output Token (excl. 1st token)------ -Mean TPOT (ms): 54.48 -Median TPOT (ms): 55.32 -P99 TPOT (ms): 63.66 +Mean TPOT (ms): 50.88 +Median TPOT (ms): 51.16 +P99 TPOT (ms): 63.04 ---------------Inter-token Latency---------------- -Mean ITL (ms): 54.48 -Median ITL (ms): 37.20 -P99 ITL (ms): 255.76 +Mean ITL (ms): 50.88 +Median ITL (ms): 37.35 +P99 ITL (ms): 442.81 ================================================== ``` @@ -462,7 +463,7 @@ One benchmarking warning before you copy this: if you re-run against a warm serv **The verdict.** The complete tables and number-by-number interpretation live in [Deep dive 7](#deep-dive-7-the-proof); here is what they add up to: - **Tensor Parallelism (TP) won nearly everything:** - - **Throughput:** 623.85 output tokens/sec at 32 concurrent requests (70% faster than pipeline parallelism). + - **Throughput:** 507.09 output tokens/sec at 32 concurrent requests (70% faster than pipeline parallelism). - **Decode Latency:** Fastest single-request decode at 17.14 ms median per token. - **Capacity:** Largest conversation capacity with 621,392 cached tokens (~19 concurrent 32k conversations). - **Memory:** Perfectly even memory distribution across all four cards. @@ -725,7 +726,7 @@ Look at the last row too. Under tensor parallelism all four cards sat at **exact | Measurement | TP=4 | TP=4 plus EP | PP=4 | Winner | | --------------------------------------- | ------------ | ------------ | ------------ | ------------------ | | Median time per token, 1 request | **17.14 ms** | 18.83 ms | 21.19 ms | TP | -| Output tokens/sec, 32 requests | **503.68** | 470.93 | 296.48 | TP, by 70% over PP | +| Output tokens/sec, 32 requests | **507.09** | 470.93 | 296.48 | TP, by 70% over PP | | Median time to first token, 32 requests | 3,233 ms | 3,705 ms | **2,735 ms** | PP, by 15% | | Benchmark duration, 32 requests | **65.06 s** | 69.58 s | 110.52 s | TP | From 08b151ee1aa38188fb118b99104567117a2ca6d4 Mon Sep 17 00:00:00 2001 From: Shubham Katara Date: Fri, 28 Aug 2026 22:27:45 +0200 Subject: [PATCH 09/19] Cut the post down to the runbook so readers with a deadline are not paying for theory The two-track structure asked every reader to route themselves through a 7,100-word post before doing anything. Readers who just need a big model serving now get a single 4,500-word path with no detours, and the mechanics move to a second post for readers who want them. The largest section, Step 1, led with safetensors internals and shard-size conventions and buried the disk trap that actually took down our Kubernetes node; the disk check now opens the step. All twelve links into the deep dive became plain-text forward references rather than links to a URL that does not exist yet. Adds a cake-layers diagram at the point in Step 3 where the reader has to pick a split, since that decision is the one place the runbook genuinely needed a visual. The three heavier animations go with part two. --- .gitignore | 3 + ...-big-llm-across-multiple-gpus-with-vllm.md | 313 ++--------- lib/_blog-feed-data.js | 2 +- public/atom.xml | 4 +- .../cake-layers.png | Bin 0 -> 383391 bytes public/llms-full.txt | 532 ++++++++---------- public/llms.txt | 2 +- public/rss.xml | 2 +- 8 files changed, 302 insertions(+), 556 deletions(-) create mode 100644 public/img/blog/running-a-big-llm-across-multiple-gpus-with-vllm/cake-layers.png diff --git a/.gitignore b/.gitignore index 0234304a1..07532fa9a 100644 --- a/.gitignore +++ b/.gitignore @@ -30,3 +30,6 @@ yarn-error.log* # wrangler local dev .dev.vars .wrangler/ + +# Scratch notes for unpublished posts, never rendered by lib/blog.js +/content/blog/_drafts/ diff --git a/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md b/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md index 644fe69e7..bbe3dd129 100644 --- a/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md +++ b/content/blog/running-a-big-llm-across-multiple-gpus-with-vllm.md @@ -1,7 +1,7 @@ --- title: "Running a big LLM across multiple GPUs with vLLM" seoTitle: "Running a big LLM across multiple GPUs with vLLM" -seoDescription: "A plain-English guide to serving a model too big for one GPU, in two tracks: a runbook from download to serving with every flag and error explained, and a deep dive into how tensor, pipeline, and expert parallelism split the model, with measured numbers from a 235B model on four RTX PRO 6000 cards." +seoDescription: "A runbook for serving a model too big for one GPU: download to serving in eight steps, with every vLLM flag, startup log line and real error explained, measured on a 235B model across four RTX PRO 6000 cards." datePublished: 2026-08-18T10:00:00.000Z slug: running-a-big-llm-across-multiple-gpus-with-vllm author: shubham-katara @@ -14,27 +14,19 @@ Sooner or later everyone running models locally hits the same wall. You find a m The answer is to use more than one GPU. That part everybody knows. The part that is genuinely confusing is what "use more than one GPU" actually means. Does each GPU get a copy of the model? Does the model get cut in half? Do the GPUs take turns? Which of those is happening, and what does it cost you? -Let's answer that properly, with a real model on real hardware. And let's be honest that not everyone is here for the same reason. +Let's answer that properly, with a real model on real hardware. -## How to read this post +## What this post covers -This post is split into two tracks: a runbook for getting a big model serving, and a deep dive explaining how multi-GPU model splitting actually works. The runbook is for readers who need the commands and configs fast. +This is the runbook. Eight steps, from downloading a 236 GB model to serving it across four GPUs, with every command, flag, startup log line and real error explained. It is written for the person with root on the box, and it assumes no prior knowledge of distributed computing: if you know what a GPU is and you have run a model locally once, you are qualified. -The deep dive is for those who want to understand the mechanics, tradeoffs, and numbers. Jump to the track that fits your need, or read both: the post is structured so each section clearly points to the other right when extra context is helpful. - -So: one post, two tracks, each with a clear exit. Pick your entrance based on the job in front of you: - -| You are | You want | Read | -| ------------------------------------------------------------------- | ----------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| **Platform engineer, SRE, MLOps**: you have the GPUs and a deadline | The model serving today | **The runbook, Steps 1-8** (~20 min). Every command, flag, log line and error. Each step links into the deep dive at exactly the point a "why" earns its keep; follow those links only when something surprises you. | -| **ML engineer, or just curious**: no root access required | The mental model | **The deep dive, sections 1-7** (~18 min). How the splitting actually works, and measured proof of when each method wins. Jump [straight there](#the-deep-dive-what-splitting-actually-means). | -| **Both** | Everything | Read straight through. The runbook comes first because you cannot benchmark a server that is not running. | +It deliberately does not explain the machinery underneath. Why splitting a layer across GPUs makes prefill faster but costs you an all-reduce per layer, why that trade lands differently on decode, and why NVLink is the variable that decides the winner, are all part two, coming next. Where a "why" would otherwise interrupt the work, this post says so and moves on. New to the jargon? Every term, flag, and benchmark number here is explained in plain English in the [local LLM glossary](https://blog.kubesimplify.com/local-llm-glossary). ## The machine and the model -Both tracks lean on this section, so here it is once. Numbers mean nothing without the hardware attached. +Numbers mean nothing without the hardware attached, so here it is once. **The machine:** a server with 8x NVIDIA RTX PRO 6000 Blackwell Server Edition cards. Each card has 96 GB of memory, and the machine reports 95.01 GiB of that as usable. We borrowed 4 of the 8 cards for this work. @@ -74,22 +66,28 @@ On our machine every pair of GPUs reports `SYS`, which means the traffic goes ac **The model:** `Qwen/Qwen3-235B-A22B-Instruct-2507-FP8`. Let's unpack that name, because it is doing a lot of work: - **235B** is the total parameter count, 235 billion. -- **A22B** means 22 billion **active** parameters. This is a mixture-of-experts model: each layer holds 128 small expert networks and a router picks just 8 of them per token, so you pay for 235B in memory but only about 22B in arithmetic. [Deep dive 5 tells the full story.](#deep-dive-5-the-expert-part) +- **A22B** means 22 billion **active** parameters. This is a mixture-of-experts model: each layer holds 128 small expert networks and a router picks just 8 of them per token, so you pay for 235B in memory but only about 22B in arithmetic. - **FP8** is the number format the weights are stored in, 8 bits each, so one byte per parameter. **The software:** vLLM 0.27.1 running in the official container, with PyTorch 2.13.0 and CUDA 13.0, on driver 610.43.02. --- -## The runbook: from download to serving +## Step 1: Getting the model onto the machine -Written for the person with root on the box. Eight steps, and at the end of them a 235B model is answering requests on four GPUs. No prior knowledge of distributed computing is assumed: if you know what a GPU is and you have run a model locally once, you are qualified. +Before anything can be split across GPUs it has to be on disk, and with a model this size that is not a formality. It is the step that bit us hardest, so it goes first. -## Step 1: Getting the model onto the machine +### Check your disk first, because this is a real production hazard -Before anything can be split across GPUs it has to be on the disk, and with a model this size that step is not a formality. It is the step that bit us hardest, so let's do it properly. +**On a shared machine, filling the disk can take down everything else on it.** This is the part we learned the hard way, and it is worth more than a footnote. Our test box also runs a Kubernetes inference platform. Kubernetes treats free disk as a managed resource called ephemeral-storage, and when free space fell below its eviction threshold, the kubelet did exactly what it is designed to do. + +It evicted pods to reclaim space, tainted the node so nothing new could schedule, and garbage-collected container images. Several of those images had been built locally and existed in no registry, so they could not simply be pulled again. -You download it with the Hugging Face CLI: +Nothing about that is a Kubernetes bug, and nothing about it is specific to our setup. The lesson generalises: **before you download a quarter of a terabyte onto a machine, check what else lives on that disk and what will happen when it fills.** `df -h` before you start, and know your platform's eviction threshold, which is often far higher than "0 bytes free". If the machine is shared, keeping a couple of hundred gigabytes of headroom is not paranoia. + +### The download + +With the headroom confirmed, you download it with the Hugging Face CLI: ```bash root@utho-gpu-rtxpro6000-8-62383:~# pip install huggingface_hub hf_transfer @@ -117,14 +115,13 @@ model.safetensors.index.json tokenizer.json ``` -A few things worth understanding here: +Three of those matter to you: -- **`.safetensors`** files hold the actual weights. Each one starts with a small table of contents saying what is inside and exactly where each piece begins and ends. So a program can jump straight to the piece it needs instead of reading the whole file. This is called memory-mapping, and it matters later. And unlike the old `.bin` format, simply opening one of these files can never run hidden code on your machine. +- **`.safetensors`** files hold the actual weights, and unlike the old `.bin` format, simply opening one can never run hidden code on your machine. - **`model.safetensors.index.json`** is the master map. The weights are spread over 24 files, and this map says which file each piece lives in. When vLLM needs layer 62, it looks here, sees shard 17, and opens only that file. - **`config.json`** is the model's spec sheet: how many layers, how many heads, how many experts. It is a few kilobytes, and it decides almost everything in this post, including how many GPUs you can split across. -- Because this model is FP8, each weight is stored in a single byte, and a single byte cannot record very large and very small numbers accurately at the same time. The checkpoint fixes this the way a paper map does. A map shrinks a whole city onto one page, and its legend tells you how to undo the shrinking: 1 cm on the page equals 1 km in the world. -Same idea here: the weights are cut into blocks of 128 by 128 numbers, every block is shrunk until its values fit in one byte each, and each block carries one extra number, its **scale**, which is the legend for that block. To get a real weight back, the GPU multiplies the stored byte by its block's scale. These **block scales** ship in the download right alongside the weights, and you can see the whole arrangement declared in `config.json`: +One more detail, because a crash in Step 8 depends on it. Because this model is FP8, each weight is a single byte, which cannot record very large and very small numbers accurately at the same time. The checkpoint works around that by cutting the weights into blocks of 128 by 128 numbers and giving each block one extra number, its **scale**, that the GPU multiplies back in to recover the real weight. These **block scales** ship alongside the weights, and the arrangement is declared in `config.json`: ```json "quantization_config": { @@ -137,13 +134,7 @@ Same idea here: the weights are cut into blocks of 128 by 128 numbers, every blo Remember those block scales. They are the reason for the most annoying crash we hit, down in Step 8. -### Is there a standard shard size? - -Not a formal one, but there are firm conventions. The Hub's guidance is to split large files into chunks under 200 GB, with 500 GB as the hard limit for a single file, for two practical reasons: a failed download of a smaller file resumes cheaply, and the CDN does not cache huge files, so one 236 GB file would genuinely download slower than 24 pieces of it. What publishers actually pick sits far below those limits. Our model uses a 10 GB cap: 23 shards of exactly 10.00 GB and a 24th holding the remaining 6.45 GB, for 236.45 GB in total. - -What actually sits inside each file is less obvious than it looks, and it is not "layers 1 to 4". [The deep dive has that story.](#a-side-note-on-the-shard-files) - -**Does any of this affect serving?** No. Whether the weights are packaged as a single file or as 24 changes nothing about the numbers inside, and because safetensors files are memory-mapped, the program loading them (vLLM, in our case) jumps straight to the pieces it needs no matter how they are grouped into files. Shard size is a distribution question, not an inference question. +Do not spend any time on the shard count itself. How the weights are packaged changes nothing about the numbers inside them, so shard size is a distribution question, not an inference question. Part two covers where those 10 GB boundaries come from and what actually sits inside each file, which is not "layers 1 to 4". ### Where it gets stored @@ -167,14 +158,6 @@ vLLM's own docs say it plainly: with tensor parallelism, "each process will read Our first `Model loading took` line said 45 seconds, but only because we had just downloaded the model, so most of it was still sitting in RAM, where the operating system keeps recently used files. From a cold disk it takes much longer. -### The disk trap, which is a real production hazard - -**On a shared machine, filling the disk can take down everything else on it.** This is the part we learned the hard way, and it is worth more than a footnote. Our test box also runs a Kubernetes inference platform. Kubernetes treats free disk as a managed resource called ephemeral-storage, and when free space fell below its eviction threshold, the kubelet did exactly what it is designed to do. - -It evicted pods to reclaim space, tainted the node so nothing new could schedule, and garbage-collected container images. Several of those images had been built locally and existed in no registry, so they could not simply be pulled again. - -Nothing about that is a Kubernetes bug, and nothing about it is specific to our setup. The lesson generalises: **before you download a quarter of a terabyte onto a machine, check what else lives on that disk and what will happen when it fills.** `df -h` before you start, and know your platform's eviction threshold, which is often far higher than "0 bytes free". If the machine is shared, keeping a couple of hundred gigabytes of headroom is not paranoia. - ## Step 2: Will it fit? The ten-minute check Do this on paper before the download, not after. The arithmetic is simpler than people expect. @@ -202,19 +185,27 @@ Weights = parameters x bytes per parameter. For our model: 235 billion at 1 byte bytes per token = 2 x layers x kv_heads x head_dim x bytes_per_number ``` -For our model: `2 x 94 x 4 x 128 x 2 = 192,512 bytes`, call it 188 KiB per token. Sounds small, but this model supports a 262,144-token context, so one single full-length conversation would need about **47 GiB**. That is half a GPU for one user, and it is why "the weights fit, so I am fine" is wrong. It is also why `--max-model-len` exists, as you will see in the flags table. [Deep dive 1 unpacks where this formula comes from.](#deep-dive-1-where-the-memory-really-goes) +For our model: `2 x 94 x 4 x 128 x 2 = 192,512 bytes`, call it 188 KiB per token. Sounds small, but this model supports a 262,144-token context, so one single full-length conversation would need about **47 GiB**. That is half a GPU for one user, and it is why "the weights fit, so I am fine" is wrong. It is also why `--max-model-len` exists, as you will see in the flags table. -One piece of good news: under tensor parallelism the KV cache is **divided** across GPUs rather than duplicated, so 4 GPUs give you roughly 4x the conversation room on top of making the weights fit. [Why that falls out of how the split works is in Deep dive 4.](#deep-dive-4-tensor-parallelism-up-close) +One piece of good news: under tensor parallelism the KV cache is **divided** across GPUs rather than duplicated, so 4 GPUs give you roughly 4x the conversation room on top of making the weights fit. {{multi-gpu-memory-fit-animation}} ## Step 3: Pick your split, then check it divides -vLLM gives you three ways to spread a model over GPUs, and they are genuinely different things. The one-minute version, so you can pick a flag and move on ([the full mechanics, with animations, start at Deep dive 3](#deep-dive-3-the-three-ways-to-split)): +vLLM gives you three ways to spread a model over GPUs, and they are genuinely different things. The one-minute version, so you can pick a flag and move on: - **Tensor parallelism** (`--tensor-parallel-size`) slices every layer across all GPUs, so they all work on the same token at once. Best tokens per second, evenly split memory, divided KV cache. The default choice for GPUs inside one machine. This is what we run. - **Pipeline parallelism** (`--pipeline-parallel-size`) gives each GPU a block of consecutive layers and passes the work along. The GPUs barely need to talk to each other, so it is the tool for spanning machines with a slow network, and it wins on time to first token, but GPUs spend time waiting their turn. -- **Expert parallelism** (`--enable-expert-parallel`) exists only for mixture-of-experts models and hands out whole experts instead of slicing them. Its job is trillion-parameter-scale clusters where even a tensor-parallel split cannot hold all the experts. It is **not** a memory saver at our scale, as [the measurements prove](#deep-dive-7-the-proof). +- **Expert parallelism** (`--enable-expert-parallel`) exists only for mixture-of-experts models and hands out whole experts instead of slicing them. Its job is trillion-parameter-scale clusters where even a tensor-parallel split cannot hold all the experts. It is **not** a memory saver at our scale, as our measurements below show. + +The first two are easiest to hold in your head as two ways of cutting a layer cake: + +![Pipeline parallelism cuts the layer cake horizontally, so each GPU owns a contiguous block of whole layers; tensor parallelism cuts it vertically, so each GPU owns a slice of every layer](/img/blog/running-a-big-llm-across-multiple-gpus-with-vllm/cake-layers.png) + +That is the whole distinction. Pipeline parallelism cuts across the layers and each GPU owns a few of them start to finish. Tensor parallelism cuts down through the layers and every GPU owns a sliver of all 94, which is why they all work on the same token at the same time. Expert parallelism is the odd one out and does not fit the cake picture: it deals whole expert networks to different cards, 32 each in our case. + +The drawing shows three cards because it is illustrating the two shapes, not our setup. Card counts are not free choices, which is exactly what the next check is about. **Then the ten-second pre-flight check.** You cannot pick any number for `--tensor-parallel-size`: because attention heads are handed out whole, your TP size must divide the model's head counts. Open `config.json`: @@ -236,7 +227,7 @@ For our model: - `num_key_value_heads` is **4**. This is the binding constraint. At `-tp 4` each GPU gets exactly one key/value head. At `-tp 8` there are not enough to go around, and vLLM has to duplicate them across GPUs, which wastes memory and gives you less benefit than you would hope. - `num_experts` is 128, which divides evenly by 4 and by 8, so expert parallelism has more freedom than tensor parallelism here. -That is the real lesson: **the KV head count, not the parameter count, usually decides how wide you can go.** It is the first thing we check on any new model, and it takes ten seconds. [Deep dive 6 explains why the KV heads bind first.](#deep-dive-6-why-you-cannot-split-forever) +That is the real lesson: **the KV head count, not the parameter count, usually decides how wide you can go.** It is the first thing we check on any new model, and it takes ten seconds. ## Step 4: Every flag, explained @@ -365,7 +356,7 @@ For our run the four lines came out as: `Model loading took 55.19 GiB` per worke Once it was running, we compared all three ways of splitting the same model over the same 4 GPUs: tensor parallelism on its own, tensor parallelism plus expert parallelism, and pure pipeline parallelism. Same hardware, same flags otherwise, same benchmark. -Two sentences of background so the results make sense: inference is two different jobs. **Prefill** reads your whole prompt at once, is limited by compute, and decides time to first token, while **decode** writes the answer one token at a time, is limited by memory bandwidth, and decides tokens per second. Every configuration trades one against the other, and [Deep dive 2 explains exactly why](#deep-dive-2-inference-is-two-jobs). +Two numbers do most of the talking. **Time to first token** is how long the user waits before anything appears, and **output tokens per second** is how fast the answer then streams. Every configuration trades one against the other; part two is about why. The benchmark is vLLM's own, 1024 tokens in and 256 tokens out per request, with `--ignore-eos` so every request generates exactly 256 tokens and the comparison is fair: @@ -456,26 +447,15 @@ P99 ITL (ms): 442.81 **And what happens if a 33rd request arrives?** Nothing dramatic, and that is worth knowing. It is not rejected and it does not error. It waits in a queue inside the server, and the moment one of the 32 running requests finishes, it takes the freed slot. So the cost of oversubscribing is waiting time, not failures: throughput stays flat because the server was already flat out, and the extra request simply sees a longer time to first token. -One subtlety: 32 is not the only ceiling in play. The startup log said this configuration holds about 19 full-length 32k conversations in its KV cache. Our benchmark requests are short, 1,280 tokens each, so all 32 fit in the cache with plenty of room and the `--max-num-seqs` flag is the limit that binds. With long conversations the cache fills first, and instead of queueing politely vLLM starts preempting: it evicts a running request's cache and recomputes it later ([Deep dive 2 explains preemption](#deep-dive-2-inference-is-two-jobs)). Which ceiling you hit first depends entirely on how long your requests are. - -One benchmarking warning before you copy this: if you re-run against a warm server, either vary the `--seed` or turn prefix caching off, otherwise your second measurement is mostly measuring vLLM's prompt cache. [The full story of the misleading numbers we caught is in Deep dive 7.](#deep-dive-7-the-proof) - -**The verdict.** The complete tables and number-by-number interpretation live in [Deep dive 7](#deep-dive-7-the-proof); here is what they add up to: +One subtlety: 32 is not the only ceiling in play. The startup log said this configuration holds about 19 full-length 32k conversations in its KV cache, and our benchmark requests are short, so `--max-num-seqs` is the limit that binds here. With long conversations the cache fills first, and instead of queueing politely vLLM starts preempting: it evicts a running request's cache and recomputes it later. Which ceiling you hit first depends entirely on how long your requests are. -- **Tensor Parallelism (TP) won nearly everything:** - - **Throughput:** 507.09 output tokens/sec at 32 concurrent requests (70% faster than pipeline parallelism). - - **Decode Latency:** Fastest single-request decode at 17.14 ms median per token. - - **Capacity:** Largest conversation capacity with 621,392 cached tokens (~19 concurrent 32k conversations). - - **Memory:** Perfectly even memory distribution across all four cards. -- **Pipeline Parallelism (PP):** Won exactly one metric which was time to first token (TTFT) by 15%. -- **Expert Parallelism (EP):** Cost 7% overhead and returned no benefit at this scale. +One benchmarking warning before you copy this: if you re-run against a warm server, either vary the `--seed` or turn prefix caching off. We forgot, and time to first token "improved" from 265 ms to 61 ms purely because we had just sent the server those same prompts with the same seed. -So for a 235B MoE on 4 GPUs with no NVLink between them, we would use plain `--tensor-parallel-size 4` and leave both of the others off. It was faster nearly everywhere, it gives the most conversation capacity, it splits memory perfectly evenly, and it is one less thing to reason about. +**The verdict.** Tensor parallelism won nearly everything: 507.09 output tokens/sec at 32 concurrent requests, which is 70% faster than pipeline parallelism; the fastest single-request decode at 17.14 ms median per token; the largest conversation capacity at 621,392 cached tokens; and perfectly even memory across all four cards. Pipeline parallelism won exactly one metric, time to first token, by 15%. Expert parallelism cost 7% and returned nothing at this scale. -We would reach for the other two in specific situations, not as general upgrades: +So for a 235B MoE on 4 GPUs with no NVLink between them, we would use plain `--tensor-parallel-size 4` and leave both of the others off. -- **Pipeline parallelism** in two situations. First, when what your users notice most is how quickly the first word of an answer appears: in our runs it delivered the first token 15% sooner than tensor parallelism. Second, when your GPUs sit in different machines joined by an ordinary network: tensor parallelism makes the GPUs talk to each other constantly for every token, which a slow network turns into a bottleneck, while pipeline parallelism hands work to the next GPU just once per token, so a slow network barely hurts it. -- **Expert parallelism** when the model is so large that even a tensor-parallel split cannot hold all the experts, which is a real problem at trillion-parameter scale and simply is not our problem at 235B on 4 cards. +When the other two are the right call, why the results fall out this way, and why NVLink is the one component that would reshuffle them, is part two. ## Step 8: Errors you will actually hit @@ -555,223 +535,28 @@ vllm serve: error: argument --compilation-config/-cc: Invalid JSON: expected val **The fix:** add `--ipc=host`. If you already have it, wait a bit longer, because CUDA graph capture on a big model is genuinely slow. -That is the runbook complete: the model is serving, you know what every flag is doing, and you know the failure modes. If you stopped here you would be in good shape. What follows is for the day you want to know _why_ the numbers came out the way they did. - ---- - -## The deep dive: what splitting actually means - -This half is for the reader who wants the machinery. No root access is required, and nothing here is needed to operate the server. When people say "split the model across GPUs" they could mean three genuinely different things, and mixing them up is the source of most confusion about multi-GPU performance. By the end of this track you will be able to look at any benchmark table and know which of the three splits produced it. - -## Deep dive 1: Where the memory really goes - -Step 2 gave you the fit-check recipe; here is what is behind it. A model is mostly a big pile of numbers called **parameters** or **weights**, and the weights are the easy part: parameters times bytes-per-parameter, fixed and known in advance. The interesting tenant in GPU memory is the KV cache. - -Every token the model reads or writes leaves behind a key and a value at every layer (the `2` in the formula), and those records have to be kept for as long as the request is alive, because decode rereads all of them to produce each new token. The per-token cost is set entirely by the architecture: 94 layers, 4 KV heads, head dimension 128, kept in BF16 at 2 bytes: - -``` -2 x 94 x 4 x 128 x 2 = 192,512 bytes = 188 KiB per token -``` - -188 KiB does not sound like much. But this model supports a 262,144 token context, so one single conversation at full length would need `262,144 x 188 KiB`, which is about **47 GiB**. That is half a GPU for one user. - -Serving ten users at once with long prompts is where all your leftover memory goes, and it is why "the weights fit, so I am fine" is wrong, and why the runbook caps `--max-model-len` at 32,768 rather than letting one request hold 47 GiB hostage. - -Two consequences worth carrying forward. First, a model's conversation capacity is an architectural property, not just a memory-size property: this model's 4 KV heads make its cache unusually cheap per token. - -Second, whichever way you split the model across GPUs, what happens to this cache (divided, duplicated, or taxed) matters as much as what happens to the weights. Keep that question in mind through the next four sections. - -### A side note on the shard files - -If you followed the runbook, the weights arrived as 24 files of about 10 GB each. Worth knowing what is actually inside one of those files, because it is not what most people guess. - -One thing the shard files do **not** line up with is the model's structure. The saver walks through the weights and fills each file to the 10 GB cap, then starts the next one, paying no attention to where a layer begins or ends. So no file contains "layers 1 to 4"; a file contains whatever bytes landed in it, and a single layer's pieces can straddle two files. - -If you want a feel for the volume anyway: one layer of this model weighs about 236 GB / 94 = 2.5 GB in FP8, so each 10 GB file holds about four layers' worth of material, the way a moving box holds "one shelf's worth of books" without holding any particular shelf. That is exactly why the download ships with an index file, `model.safetensors.index.json`, mapping every piece to its file: without it, nobody would know where anything landed. - -## Deep dive 2: Inference is two jobs - -Before splitting anything, it helps to know what the work being split actually is, because inference is really two different jobs wearing one coat. Almost everything confusing about multi-GPU performance comes from this split. - -### Phase one: prefill, reading your prompt - -When your prompt arrives, the model has to read all of it. If you send 1,000 tokens, all 1,000 go through every layer **at once**, as one big batch of work. This is called **prefill**, and it is the phase that decides your time to first token. - -Prefill is _compute-heavy_. There is a lot of arithmetic to do and the GPU's matrix engines are the bottleneck. It also produces the keys and values for every one of those 1,000 tokens, which get written into the KV cache and kept. - -One more thing becomes true once the model is split over several GPUs: prefill is also the phase where the GPUs send each other the most data, because every exchange between them carries your whole prompt rather than a single token. - -That makes prefill the phase most sensitive to how fast the link between the GPUs is. Our machine has no NVLink, so its GPUs talk over the slower PCIe path, and the measurements later show the bill for that: the splitting method that talks the most lost time to first token, and the method that barely talks won it. - -### Phase two: decode, writing the answer - -Then the model writes its reply, and here is the part that surprises people: **it can only produce one token at a time.** To write token 2 it needs to have written token 1, because it feeds its own output back in. There is no way around that, it is what "autoregressive" means. - -So decode is a loop. Each pass through it produces exactly one token, reads the entire KV cache built so far, and appends one more entry to that cache. - -Decode is _memory-heavy_ rather than compute-heavy. For a single token there is barely any arithmetic to do, but the GPU still has to stream the relevant weights and the whole KV cache past its compute units. - -The bottleneck is memory bandwidth, not maths. That is why decode speed tracks memory bandwidth so closely, and why giving a single request more GPUs to read from in parallel actually helps. - -Two phases, two different bottlenecks, and they respond differently to everything you tune: - -| | Prefill | Decode | -| ----------------------- | --------------------------- | ----------------------- | -| Work per step | your whole prompt at once | exactly one token | -| Bottleneck | compute | memory bandwidth | -| Metric it drives | time to first token | time per output token | -| Data moved between GPUs | large, whole prompt's worth | tiny, one token's worth | - -That last row is the one to hold on to. It is the reason, later, that pipeline parallelism wins on first-token latency while tensor parallelism wins on tokens per second. The same all-reduce that is trivially cheap during decode is expensive during prefill, because it is carrying a thousand times more data. - -### How the server juggles many users - -A real server is not doing one request at a time. vLLM uses **continuous batching**, which means it does not wait for a batch to fill up or finish. On every step it looks at everything currently in flight and assembles whatever work is ready, so a request that arrives mid-flight joins the very next step rather than queueing behind a whole batch. - -Two consequences worth knowing: - -- **Prefill and decode get mixed together.** A step might carry one user's fresh 1,000-token prompt alongside twenty other users' single decode tokens. That mixing is why a burst of long prompts makes everyone else's tokens arrive more slowly, and it is why `--max-num-batched-tokens` exists as a lever. -- **Capacity is set by the KV cache, not by CPU or queue length.** Every in-flight request is holding cache proportional to its length. When the cache is full, vLLM has to **preempt** somebody: it evicts a request's cache and recomputes it later. That is the real meaning of the `Maximum concurrency` line in the startup log from Step 6. - -Now that the work itself is clear, let's look at the three ways to spread it over more than one GPU. - -## Deep dive 3: The three ways to split - -Here is the heart of it. An analogy first, because it makes the rest much easier to hold in your head. Imagine a large restaurant kitchen that has to produce one dish: - -- **Tensor parallelism** is four chefs all working on the same dish at the same time, one chopping, one on sauce, one on protein, one plating. They constantly have to coordinate, but the dish is done fast. -- **Pipeline parallelism** is four chefs at four stations, where the dish moves down the line. Station two cannot start until station one is finished. Very little talking, but three chefs are idle at any moment unless you have several dishes in flight. -- **Expert parallelism** is a kitchen with 128 specialist chefs where each dish only needs 8 of them. You spread those 128 chefs across four rooms, and each dish gets walked to whichever rooms hold the specialists it needs. - -{{multi-gpu-split-modes-animation}} - -All three can be combined, and in production they usually are. Now let's look at the two interesting ones up close. - -## Deep dive 4: Tensor parallelism, up close - -Tensor parallelism cuts **inside** every layer. This is the important distinction: it does not give GPU 0 the first half of the model and GPU 1 the second half. Every GPU holds a thin slice of **all 94 layers**. - -How can you cut a layer? Because the work a layer does is mostly one big multiplication table, and multiplication tables can be cut up. The technique comes from a 2019 NVIDIA paper called Megatron-LM, and it works in two moves. - -**Move one, cut the first matrix into vertical strips.** Each GPU takes some of the columns. Because each GPU has complete columns, it can finish its part, including the activation function in the middle, without asking anyone anything. In our model the attention block has 64 heads, so with 4 GPUs each one owns 16 whole heads and computes them start to finish alone. - -**Move two, cut the second matrix into horizontal strips.** These line up exactly with the vertical cuts from move one. Each GPU multiplies its slice and gets a **partial answer**, a quarter of the real result. - -Now, and only now, the GPUs have to talk. They add their four partial answers together so that everyone ends up with the complete result. That single operation is called an **all-reduce**: everyone contributes a piece, everyone gets the total back. - -The Megatron paper puts the cost plainly, saying this design lets you run a transformer layer "using only two all-reduces in the forward path". Generating text only uses the forward path, so: - -- 2 all-reduces per layer -- 94 layers -- **188 all-reduces to produce one single token** - -And they happen strictly one after another, because layer 5 cannot begin until layer 4 has finished comparing notes. - -{{multi-gpu-tensor-split-animation}} - -### The KV cache gets divided too, which is a bonus - -Because each GPU owns only some of the attention heads, it only needs to remember keys and values for its own heads. So the KV cache is divided across GPUs rather than duplicated. Four GPUs give you roughly four times the room for conversations, on top of making the weights fit. This is a real and often unmentioned benefit of tensor parallelism. - -You can check it against the real run. The whole-model cache costs 188 KiB per token, so at TP=4 each card keeps one of the 4 KV heads and pays 47 KiB per token. Divide the 27.85 GiB of cache memory from Step 6's startup log by 47 KiB and you predict 621,337 tokens of capacity. vLLM printed 621,392. When a mental model predicts a five-significant-figure log line, you can trust the mental model. - -## Deep dive 5: The expert part - -Our model is a **mixture of experts**, and this is the single biggest idea in how large models are served today, so it is worth slowing down for. - -In an ordinary model, every parameter is used for every token. In a mixture-of-experts model, each layer contains many small networks called **experts**, and a tiny component called a **router** decides which few of them each token should visit. Our model has **128 experts per layer** and the router picks **8** of them per token. - -So the model holds 235B parameters in memory, but only about 22B of them do any arithmetic for a given token. That is what "235B-A22B" means, and it is why this model runs far faster than its size suggests. You pay for the full 235B in memory and you pay for only 22B in speed. - -{{moe-expert-routing-animation}} - -This gives you a third way to split. Instead of slicing every expert into strips, you hand out whole experts: with 128 experts and 4 GPUs, each GPU keeps 32 of them intact. That is **expert parallelism**, and in vLLM you switch it on with `--enable-expert-parallel`. - -The trade is different from tensor parallelism. Nothing needs adding up at the end, but tokens have to travel to whichever GPU owns the expert they were routed to, and the answers travel back. It also has a fairness problem: the router does not promise to spread work evenly, so one GPU can end up with more popular experts and become the slow one holding everybody up. - -## Deep dive 6: Why you cannot split forever - -Step 3 gave you the rule as a checklist item; here is the reasoning underneath it. - -Attention heads are computed whole: Deep dive 4 showed each GPU owning 16 complete heads and finishing them alone. A head cannot be half on one GPU and half on another without turning every attention step into a network call, so vLLM refuses to try: **your tensor parallel size must divide the head counts** in `config.json` (shown in full in Step 3). - -For our model the arithmetic lands like this. `num_attention_heads` is 64, which divides generously: 2, 4, 8, 16 all work. But `num_key_value_heads` is **4**, and that is the binding constraint: at `-tp 4` each GPU holds exactly one KV head, and at `-tp 8` there are not enough to go around, so vLLM has to duplicate them across GPUs, which costs memory and erodes exactly the cache-division bonus that made tensor parallelism attractive in Deep dive 4. Meanwhile `num_experts` is 128, divisible by 4 and 8 alike, which is why expert parallelism has more freedom than tensor parallelism on this model. - -That is the real lesson: **the KV head count, not the parameter count, usually decides how wide you can go.** Grouped-query attention (few KV heads shared by many attention heads) is what makes modern models cheap to cache, and the same design choice is what caps their tensor-parallel width. The cheapness and the cap are the same number. Break the rule and vLLM fails in about a second, [with the exact error shown in Step 8](#step-8-errors-you-will-actually-hit). - -## Deep dive 7: The proof - -Theory is cheap, so we measured it: all three splits, same model, same 4 GPUs, same benchmark: vLLM's own, 1024 tokens in and 256 out, run at one request at a time and again with 32 in flight, because those two regimes behave completely differently and a configuration that wins one can lose the other. The exact commands are in [Step 7](#step-7-benchmark-it-and-what-we-would-run). - -### The memory side - -| | TP=4 | TP=4 plus EP | PP=4 | -| ---------------------- | ------------------- | ------------------- | --------------------------------- | -| Weights per GPU | 55.19 GiB | 55.19 GiB | 55.70 GiB | -| KV cache per GPU | 27.85 GiB | 27.96 GiB | 26.84 GiB | -| Total KV cache | 621,392 tokens | **623,696 tokens** | 555,680 tokens | -| Max concurrency at 32k | 18.96x | **19.03x** | 16.96x | -| GPU memory used | 88,211 MiB on all 4 | 88,209 MiB on all 4 | 84,283 / 87,899 / 87,899 / 84,507 | - -Two things to pull out of that table. - -**Expert parallelism did not save memory.** It moved 0.37% of extra room into the cache, which is noise. If you were hoping expert parallelism would let you fit a model that otherwise does not fit, this is your warning that it will not. - -**Pipeline parallelism cost us 11.8% of the cache**, dropping from 621,392 tokens to 555,680, because a pipeline needs extra buffers for the activations travelling between stages, and that comes straight out of your conversation capacity. - -Look at the last row too. Under tensor parallelism all four cards sat at **exactly 88,211 MiB**, the same number on every one of them. Under pipeline parallelism they ranged from 84,283 to 87,899 MiB, about 3.6 GB apart, because a layer split cannot be perfectly even when 94 layers go over 4 GPUs and the ends of the model are not symmetric: the first stage carries the token embedding and the last carries the output head. That evenness check is the quickest sanity test you have that a tensor-parallel split is behaving. - -### The speed side - -| Measurement | TP=4 | TP=4 plus EP | PP=4 | Winner | -| --------------------------------------- | ------------ | ------------ | ------------ | ------------------ | -| Median time per token, 1 request | **17.14 ms** | 18.83 ms | 21.19 ms | TP | -| Output tokens/sec, 32 requests | **507.09** | 470.93 | 296.48 | TP, by 70% over PP | -| Median time to first token, 32 requests | 3,233 ms | 3,705 ms | **2,735 ms** | PP, by 15% | -| Benchmark duration, 32 requests | **65.06 s** | 69.58 s | 110.52 s | TP | - -Tensor parallelism won nearly everything, and the size of one gap deserves attention: at 32 concurrent requests it produced **70% more tokens per second than pipeline parallelism**. That is not a rounding error, that is a different class of performance, and it lines up exactly with the theory from Deep dive 3. Tensor parallelism has all four GPUs working on every token. Pipeline parallelism has each GPU working on a different request's stage, and with only 32 requests spread over 4 stages there is not enough in flight to keep everyone busy, so cards sit idle waiting for their turn. Its median time per token was 24% worse for the same reason. - -**Pipeline parallelism did win one thing, and it is the one theory predicts:** time to first token, by 15%. Processing your 1024-token prompt is where tensor parallelism's chatter gets expensive, because each of those 188 all-reduces is carrying the whole prompt's worth of data rather than a single token's. Pipeline parallelism just hands one activation tensor to the next stage and skips all of it. If your users judge you on how fast the first word appears, that is a real and measurable advantage. This is the two-jobs split from Deep dive 2 showing up in a table: prefill moves a lot of data between GPUs, decode moves almost none, so the talkative strategy pays during prefill and collects during decode. - -That is not a knock on expert parallelism, and it is important not to over-read it. Expert parallelism exists to solve a problem we do not have here: models so large that even a tensor-parallel split cannot hold all the experts, and clusters big enough that duplicating experts everywhere would be wasteful. With 4 GPUs and a model that already fits, we are asking it to do a job it was not designed for, and paying an extra network hop per token for nothing; here it cost 7% and returned nothing. On a 32 or 64 GPU deployment of a trillion-parameter model the answer would very likely flip. - -### The number we threw away, and why - -Being straight about this because it is a good lesson in reading your own benchmarks. The very first expert-parallel run at one-request-at-a-time reported **28.71 output tokens per second**, which would have made expert parallelism look catastrophic. It was not real. Look at the two TTFT figures from that run: - -``` -Mean TTFT (ms): 3987.38 -Median TTFT (ms): 265.56 -``` - -A mean fifteen times the median means one request behaved completely differently from the other eleven. One request stalled for about 45 seconds, almost certainly a one-off kernel compilation on the first pass through a code path, and that single stall stretched the whole benchmark from 63 seconds to 107 seconds. Since throughput is just tokens divided by wall-clock, one stall wrecked the headline number. - -This is why the table above uses **median time per token** as the decode measurement rather than aggregate throughput. Median per-token latency does not care that one request had a bad start. - -One more benchmarking trap while we are here. When we re-ran that same benchmark on the warm server, time to first token dropped from 265 ms to **61 ms**, which looks like a wonderful improvement and is actually meaningless: vLLM caches prompt prefixes by default, and we had just sent it those exact prompts with the same `--seed 42`. If you are comparing configurations, either vary the seed or turn prefix caching off, otherwise your second measurement is mostly measuring your cache. +That is the runbook complete: the model is serving, you know what every flag is doing, and you know the failure modes. ## Wrapping up -If you take five things away from this, let them be these. +Four things to carry out of this, all of them checks you can run in a minute. -**One.** Inference is two jobs, not one. Prefill reads your whole prompt at once and is limited by compute; decode writes one token at a time and is limited by memory bandwidth. Every confusing multi-GPU result in this post traces back to that split, so when a change helps one metric and hurts the other, this is why. +**One.** Check your disk before you download, because this cost us more than any GPU problem did. A quarter of a terabyte of weights on a shared machine is not just a storage question, it is a question about everything else living on that disk. Ours was a Kubernetes node, free space crossed the kubelet's eviction threshold, and it evicted the platform's own pods and garbage-collected locally-built images that no registry could replace. `df -h` first, and leave real headroom. -**Two.** Work out the memory on paper first. Parameters times bytes-per-parameter gives you the weights, and then remember that the weights are only one of three things that must fit, alongside the conversation cache and the working space. A model whose weights just barely fit is a model that cannot serve anybody. +**Two.** Check `num_key_value_heads` in `config.json` before you plan your hardware. It, not the parameter count, is usually what limits how many GPUs you can split across cleanly. Ours is 4, which is exactly why we run at `-tp 4` and not `-tp 8`. -**Three.** "Splitting across GPUs" is three different things. Tensor parallelism slices every layer and makes all your GPUs work on the same token, at the cost of constant chatter. Pipeline parallelism cuts the layer stack into blocks and barely communicates, at the cost of GPUs waiting their turn. Expert parallelism only exists for mixture-of-experts models and hands out whole experts. You can combine them, and for big models you usually do. +**Three.** Work out the memory on paper first. Parameters times bytes-per-parameter gives you the weights, and then remember the weights are only one of three things that must fit, alongside the conversation cache and the working space. A model whose weights just barely fit is a model that cannot serve anybody. **Four.** Read the startup log. `Model loading took`, `Available KV cache memory`, `GPU KV cache size` and `Maximum concurrency` tell you, in four lines, whether your setup is sane and how many users it can actually hold. A negative cache number is the clearest error message in the whole stack. -**Five.** Check `num_key_value_heads` in `config.json` before you plan your hardware. It, not the parameter count, is usually what limits how many GPUs you can split across cleanly. +And for a 235B mixture-of-experts model on four GPUs with no NVLink between them, the answer is plain `--tensor-parallel-size 4`. It was faster nearly everywhere, it gives the most conversation capacity, and it splits memory perfectly evenly. -One last practical warning, because it cost us more than any GPU problem did. **Check your disk before you download.** A quarter of a terabyte of model weights on a shared machine is not just a storage question, it is a question about everything else living on that disk. Ours was a Kubernetes node, free space crossed the kubelet's eviction threshold, and it evicted the platform's own pods and garbage-collected locally-built images that no registry could replace. `df -h` first, and leave real headroom. +Try it on whatever you have. Two GPUs are enough to see every one of these steps in action, and the log lines mean the same thing whether you are running 4 GPUs or 40. If you hit something we did not cover, tell us and we will add it. -Try it on whatever you have. Two GPUs are enough to see every concept in this post in action, and the log lines mean the same thing whether you are running 4 GPUs or 40. If you hit something we did not cover, tell us and we will add it. +**Part two is the why.** How slicing a layer across GPUs divides the prefill arithmetic between them, why that costs you an all-reduce at every layer, why the bill lands hard on time to first token but barely registers during decode, and why NVLink is the single variable that decides whether tensor parallelism wins. Coming next. ## Credits and references -- The tensor parallel scheme is from **Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism** by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro: [arxiv.org/abs/1909.08053](https://arxiv.org/abs/1909.08053) - vLLM parallelism and scaling guide: [docs.vllm.ai/en/latest/serving/parallelism_scaling.html](https://docs.vllm.ai/en/latest/serving/parallelism_scaling.html) - vLLM memory and optimization docs: [conserving_memory](https://docs.vllm.ai/en/latest/configuration/conserving_memory.html) and [optimization](https://docs.vllm.ai/en/latest/configuration/optimization.html) - Model card and config: [huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507-FP8](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507-FP8) diff --git a/lib/_blog-feed-data.js b/lib/_blog-feed-data.js index 6e7748a53..5166b9fa7 100644 --- a/lib/_blog-feed-data.js +++ b/lib/_blog-feed-data.js @@ -3,7 +3,7 @@ export const FEED_POSTS = [ { "slug": "running-a-big-llm-across-multiple-gpus-with-vllm", "title": "Running a big LLM across multiple GPUs with vLLM", - "description": "A plain-English guide to serving a model that is too big for one GPU: how tensor, pipeline, and expert parallelism split it up, what every vLLM flag does, and measured numbers from a 235B model on four RTX PRO 6000 cards.", + "description": "A runbook for serving a model too big for one GPU: download to serving in eight steps, with every vLLM flag, startup log line and real error explained, measured on a 235B model across four RTX PRO 6000 cards.", "datePublished": "2026-08-18T10:00:00.000Z", "cover": "/img/blog/running-a-big-llm-across-multiple-gpus-with-vllm/cover.png", "tags": [ diff --git a/public/atom.xml b/public/atom.xml index bfce4fca5..9308f4f69 100644 --- a/public/atom.xml +++ b/public/atom.xml @@ -5,7 +5,7 @@ https://blog.kubesimplify.com/ - 2026-08-18T11:45:29.744Z + 2026-08-28T19:13:14.583Z Kubesimplify hello@kubesimplify.com @@ -16,7 +16,7 @@ https://blog.kubesimplify.com/running-a-big-llm-across-multiple-gpus-with-vllm 2026-08-18T10:00:00.000Z 2026-08-18T10:00:00.000Z -