sglang-gpt-oss-optimization

Auditing GPT-OSS PR diffs in SGLang involves running a PR-diff audit on the optimization PR to generate a structured summary that centralizes patterns and preserves evidence references.

Covered GPT-OSS optimization patterns for SGLang include OpenAI MoE, MXFP4 and FP8 quantization, DP/EP, reasoning parser, tool calling, and Eagle speculative decode across PR diffs.

SGLang handles MXFP4 and FP8 quantization for GPT-OSS by codifying PR-backed optimization patterns into a centralized manual, ensuring consistent engineering practices and preserving historical context.

Yes, you can track historical context and evidence references for GPT-OSS PRs. The Skill preserves these references within a centralized manual to accelerate reviews and consolidate scattered improvement notes.

The best way to review GPT-OSS MoE optimization PRs in SGLang is applying codified PR-backed audit patterns that provide runtime surfaces and example workflows for end-to-end optimization.

Yes, SGLang supports Eagle speculative decode optimization for GPT-OSS. The Skill identifies and codifies PR-backed optimization patterns specifically across Eagle and spec decode PR diffs.