skills/model-optimization/sglang/sglang-mixtral-quark-int4fp8-moe-optimization/SKILL.md
PR-backed optimization manual for Mixtral-8x7B with SGLang's AMD-only quark_int4fp8_moe online MoE quantization. Use when an engineer needs to audit or extend Mixtral AMD quantization, online INT4-to-FP8 MoE loading, AITER fused-MoE execution, or the registered GSM8K regression test.
npx skillsauth add BBuf/AI-Infra-Auto-Driven-SKILLS sglang-mixtral-quark-int4fp8-moe-optimizationInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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quark_int4fp8_moe is an AMD-only online quantization path used by MoE checkpoints such as mistralai/Mixtral-8x7B-Instruct-v0.1. It loads high-precision MoE expert weights, quantizes them to packed INT4, stores scales, and executes with FP8-style MoE math on ROCm.
Current evidence snapshot:
origin/main: bca3dd958 on 2026-04-24python/sglang/srt/layers/quantization/quark_int4fp8_moe.pytest/registered/quant/test_int4fp8_moe.pydocs_new/docs/advanced_features/quantization.mdxUse 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.
Capture:
mistralai/Mixtral-8x7B-Instruct-v0.1--quantization quark_int4fp8_moe, --attention-backend triton, --tp 2stage-b-test-2-gpu-large-amd0.56FusedMoE.__main__ handling.--quantization quark_int4fp8_moe --attention-backend triton --tp 2.test/registered/quant/test_int4fp8_moe.py on AMD and verify GSM8K score above 0.56.references/pr-history.md: diff-reviewed Mixtral/quark INT4-FP8 MoE PR cards.development
Perform SGLang code review in the style of human maintainers by consulting the full non-agent PR review episode corpus from project start through the latest refresh (June 2026), including inline review threads, top-level PR comments, review submissions, original multilingual text, and multi-round discussions. Use when reviewing SGLang PRs, diffs, patches, or local changes for correctness, tests, performance, GPU/runtime risks, API compatibility, and maintainability.
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.
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.