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chore: move vllm-xpu-bench to intel/gpu-ai-skills@6e7bce819e2e #79

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chore: move vllm-xpu-bench to intel/gpu-ai-skills@6e7bce819e2e #79

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2 changes: 1 addition & 1 deletion skills.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -291,7 +291,7 @@
- name: vllm-xpu-bench
maintainer: "xaleryb"
external-repo: "https://github.com/intel/gpu-ai-skills"
external-commit: "0b4fafd09c5eb4cc5daf532d915ef5984a919775"
external-commit: "6e7bce819e2e44a4aac692b0a49253f39c0f6957"
external-path: "plugins/intel-gpu-ai-skills/skills/vllm-xpu-bench"
external-license: "Apache-2.0"
intel-products: "Intel GPU"
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2 changes: 1 addition & 1 deletion skills/vllm-xpu-bench/.source.json
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
{
"repo": "https://github.com/intel/gpu-ai-skills",
"path": "plugins/intel-gpu-ai-skills/skills/vllm-xpu-bench",
"commit": "0b4fafd09c5eb4cc5daf532d915ef5984a919775",
"commit": "6e7bce819e2e44a4aac692b0a49253f39c0f6957",
"license": "Apache-2.0"
}
12 changes: 7 additions & 5 deletions skills/vllm-xpu-bench/SKILL.md
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Expand Up @@ -7,9 +7,13 @@ description: Benchmark a **running vLLM-XPU OpenAI-compatible server** on an Int

`vllm bench` is the same CLI on Intel as on CUDA. The XPU-specific
levers are the serve-side flags from **vllm-xpu-run**
(`--enforce-eager`, `--max-model-len`, `--gpu-memory-utilization`,
`--block-size=64`). Pure measurement; for fixes see profiling
skills.
(`--max-model-len`, `--gpu-memory-utilization`, `--block-size=64`).
Pure measurement; for fixes see profiling skills.

Leave XPU graphs on when benchmarking v0.31.0+ images: adding
`--enforce-eager` roughly halves decode throughput (measured TPOT
64 ms vs 36 ms), so an eager baseline understates what the deployment
will actually do.

## Preflight — find the container and verify the server

Expand Down Expand Up @@ -78,7 +82,6 @@ docker run -d --name <container-name> \
vllm/vllm-openai-xpu:latest \
<model-id> \
--dtype bfloat16 \
--enforce-eager \
--block-size=64 \
--max-model-len 4096 \
--gpu-memory-utilization 0.85 \
Expand Down Expand Up @@ -226,7 +229,6 @@ docker run --rm \
bench throughput \
--model Qwen/Qwen2.5-1.5B-Instruct \
--dtype bfloat16 \
--enforce-eager \
--max-model-len 4096 \
--gpu-memory-utilization 0.85 \
--dataset-name random \
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