skills/model-optimization/vllm/vllm-nemotron-super-optimization/SKILL.md
PR-backed optimization manual for Nemotron Super / Nano Hybrid in vLLM. Use when an engineer needs to audit, debug, extend, or document NemotronH, Nemotron 3 Super, Nemotron Nano hybrid Mamba+Attention+MoE, MTP, NVFP4, and VL adjacencies.
npx skillsauth add BBuf/AI-Infra-Auto-Driven-SKILLS vllm-nemotron-super-optimizationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill covers NemotronH, Nemotron 3 Super, Nemotron Nano hybrid Mamba+Attention+MoE, MTP, NVFP4, and VL adjacencies.
Evidence snapshot:
0f7be0f2f76814f80f9091220a5fbbb53912ad00references/pr-history.mdmodel-pr-optimization-history/vllm/nemotron-super/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/nemotron_h.pyvllm/vllm/model_executor/models/nemotron_h_mtp.pyvllm/vllm/model_executor/models/nano_nemotron_vl.pyvllm/vllm/model_executor/models/nemotron_vl.pyNemotronH support: Initial NemotronH landing in vLLM.Add MoE support for NemotronH: Extended the hybrid family to routed experts.Nemotron-H MTP and Mamba Speculative Decoding Support: Opened the MTP / spec-decode path.E2E Nemotron-3-Super tests: Added direct Super-family regression coverage.Enable NemotronHPuzzle + NemotronHMTP: Expanded hybrid and MTP coverage for the family.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.