skills/model-optimization/vllm/vllm-qwen35-optimization/SKILL.md
PR-backed optimization manual for Qwen3.5 in vLLM. Use when an engineer needs to audit, debug, extend, or document Qwen3.5 dense / MoE / GDN runtime, MTP, FP8 and NVFP4 quantization, LoRA, and Eagle3 in vLLM.
npx skillsauth add BBuf/AI-Infra-Auto-Driven-SKILLS vllm-qwen35-optimizationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill covers Qwen3.5 dense / MoE / GDN runtime, MTP, FP8 and NVFP4 quantization, LoRA, and Eagle3 in vLLM.
Evidence snapshot:
0f7be0f2f76814f80f9091220a5fbbb53912ad00references/pr-history.mdmodel-pr-optimization-history/vllm/qwen35/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/qwen3_5.pyvllm/vllm/model_executor/models/qwen3_5_mtp.pyAdding Support for Qwen3.5 Models: Landed the Qwen3.5 runtime family.Redo Qwen3.5/Qwen3-Next GDN projector fusion: Reworked an earlier fusion that had to be reverted.Fix Qwen3.5 FP8 quantization tuple shard_id weight loading: Closed a concrete FP8 weight-loading failure.Add Eagle3 support for Qwen3.5: Enabled the draft-model fast path.Extract GatedDeltaNetAttention into shared layer for Qwen3Next and Qwen3.5: Reduced duplicated GDN logic across related families.Fix EP precision for Qwen3.5, Qwen3-Next: Patched a serving-precision bug under expert parallelism.development
Perform SGLang code review in the style of human maintainers by consulting the full non-agent PR review episode corpus from project start through the latest refresh (June 2026), including inline review threads, top-level PR comments, review submissions, original multilingual text, and multi-round discussions. Use when reviewing SGLang PRs, diffs, patches, or local changes for correctness, tests, performance, GPU/runtime risks, API compatibility, and maintainability.
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.
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.