skills/model-optimization/vllm/vllm-qwen3-next-optimization/SKILL.md
PR-backed optimization manual for Qwen3-Next in vLLM. Use when an engineer needs to audit, debug, extend, or document Qwen3-Next GDN attention, MTP, packed module naming, PP, and cross-hardware tuned MoE configuration in vLLM.
npx skillsauth add BBuf/AI-Infra-Auto-Driven-SKILLS vllm-qwen3-next-optimizationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill covers Qwen3-Next GDN attention, MTP, packed module naming, PP, and cross-hardware tuned MoE configuration in vLLM.
Evidence snapshot:
0f7be0f2f76814f80f9091220a5fbbb53912ad00references/pr-history.mdmodel-pr-optimization-history/vllm/qwen3-next/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_next.pyvllm/vllm/model_executor/models/qwen3_next_mtp.pyFix Qwen3-Next PP: Corrected pipeline-parallel execution on Qwen3-Next.Fix the varlen issue in qwen3-next MTP implementation: Removed a concrete MTP correctness bug on variable-length batches.Add prefixes to shared_expert in qwen3-next: Fixed ignored-parameter and quantized weight loading for shared experts.Fix cuda graph capture bug in GDN metadata and a stride bug: Stabilized GDN execution under CUDA graphs.Speed-up of GDN attention decode part: Improved decode throughput on the GDN attention path.Initial support for GDN attention on Qwen3-next/Qwen3.5 (XPU): Extended the family beyond CUDA with XPU GDN coverage.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.