skills/model-optimization/sglang/sglang-gpt-oss-optimization/SKILL.md
PR-backed optimization manual for GPT-OSS in SGLang. Use when an engineer needs to audit, debug, extend, or document OpenAI GPT-OSS MoE, MXFP4/FP8 quantization, DP/EP, reasoning parser, tool calling, and Eagle/spec decode.
npx skillsauth add BBuf/AI-Infra-Auto-Driven-SKILLS sglang-gpt-oss-optimizationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill covers OpenAI GPT-OSS MoE, MXFP4/FP8 quantization, DP/EP, reasoning parser, tool calling, and Eagle/spec decode.
Evidence snapshot:
c122d343adb969cd9bbd1af2ca86727a11be3845e88b0fd8ac5b1caa6eb42766035029220053369breferences/pr-history.mdmodel-pr-optimization-history/sglang/gpt-oss/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/gpt_oss.pySupport mxfp4 for GPT-OSS: Added the headline quantized checkpoint path.Expert Parallelism for GPT-OSS: Scaled GPT-OSS beyond pure tensor parallel.Implement Native GPT-OSS Tool Call Support: Added native tool parser support instead of Harmony integration.Support DP attention with GPT-OSS: Enabled larger topologies via DP attention.GPT-OSS Eagle v2 support: Added speculative decoding support.Support fp8 online quantization for gpt-oss bf16: Extended quantization coverage to online FP8.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.