skills/model-optimization/sglang/sglang-nemotron-super-optimization/SKILL.md
PR-backed optimization manual for Nemotron Super / Nano Hybrid in SGLang. 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 sglang-nemotron-super-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 NemotronH, Nemotron 3 Super, Nemotron Nano hybrid Mamba+Attention+MoE, MTP, NVFP4, and VL adjacencies.
Evidence snapshot:
c122d343adb969cd9bbd1af2ca86727a11be3845e88b0fd8ac5b1caa6eb42766035029220053369breferences/pr-history.mdmodel-pr-optimization-history/sglang/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.
sglang/python/sglang/srt/models/nemotron_h.pysglang/python/sglang/srt/models/nemotron_h_mtp.pysglang/python/sglang/srt/models/nano_nemotron_vl.pyNemotronH PP support: Opened pipeline parallelism on NemotronH.Add latent MoE support: Added the hybrid latent-MoE path.Enable Piecewise CUDA Graph for NemotronH Hybrid Models: Improved hybrid serving efficiency.Support Nemotron 3 Super NVFP4: Added the key quantized Super checkpoint path.Add Nemotron 3 Super CI tests for BF16 and NVFP4: Added regression coverage for the production checkpoint variants.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.