skills/model-optimization/vllm/vllm-deepseek-v31-optimization/SKILL.md
PR-backed optimization manual for DeepSeek V3.1 in vLLM. Use when an engineer needs to audit, debug, extend, or document DeepSeek V3.1 parser, scale-format, DeepGEMM, and reasoning-tooling deltas layered on top of the base DeepSeek V3 runtime.
npx skillsauth add BBuf/AI-Infra-Auto-Driven-SKILLS vllm-deepseek-v31-optimizationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
This skill covers DeepSeek V3.1 parser, scale-format, DeepGEMM, and reasoning-tooling deltas layered on top of the base DeepSeek V3 runtime.
Evidence snapshot:
0f7be0f2f76814f80f9091220a5fbbb53912ad00deepseek-v3-r1 first; this dossier only records the delta for deepseek-v31.references/pr-history.mdmodel-pr-optimization-history/vllm/deepseek-v31/README.zh.md and README.en.mdUse skills/model-optimization/model-pr-diff-dossier/SKILL.md as the production bar.
Every PR cited for this family must be based on diff reading, not only PR titles.
vllm/vllm/model_executor/models/deepseek_v2.pyvllm/vllm/model_executor/layers/quantization/utils/flashinfer_utils.pySupport DeepSeek-V3.1 tool call: Added the first V3.1-specific tool-call parser surface to vLLM.Add Hopper DeepGEMM E8M0 for DeepSeekV3.1 scale_fmt: Tuned the scale-format path used by DeepGEMM-based DeepSeek V3.1 kernels.Add DeepSeek-V3.1 reasoning parser: Separated V3.1 reasoning output handling from generic DeepSeek parsing.Fix DeepSeek-V3.1 + DeepGEMM incompatible scale shapes: Patched a concrete shape mismatch between newer checkpoints and DeepGEMM assumptions.development
Run an autonomous Humanize-governed vLLM SOTA performance loop for one LLM model: first perform the fixed fair vLLM/SGLang/TensorRT-LLM deployment search and benchmark, then start one RLCR loop that repeatedly decides the gap, profiles the current bottleneck, runs layer/kernel pipeline analysis, patches vLLM code, optionally uses ncu-report-skill for kernel evidence, and revalidates until vLLM matches or beats the best observed framework under the same workload and SLA.
devops
Inspect LLM torch profiler traces at forward-pass, layer, and kernel level. Use when you need layer timings, anchor-kernel boundaries, representative kernel flows, or Perfetto time ranges.
development
Run an autonomous Humanize-governed SGLang SOTA performance loop for one LLM model: first perform the fixed fair SGLang/vLLM/TensorRT-LLM deployment search and benchmark, then start one RLCR loop that repeatedly decides the gap, profiles the current bottleneck, runs layer/kernel pipeline analysis, patches SGLang code, optionally uses ncu-report-skill for kernel evidence, and revalidates until SGLang matches or beats the best observed framework under the same workload and SLA.
documentation
Use when an SGLang, vLLM, or TensorRT-LLM serving/model optimization task needs prior model-family PR evidence. Query and read the PR-driven history docs under model-pr-optimization-history before choosing source paths, fast paths, kernel/fusion ideas, regression risks, or validation lanes.