sglang-sota-performance
SGLang SOTA Performance
Overview
Use this skill as the top-level optimization loop for one model at a time. It composes two lower-level skills:
llm-serving-auto-benchmark: search and compare best deployment commands across SGLang, vLLM, and TensorRT-LLM.llm-torch-profiler-analysis: capture or analyze torch-profiler traces and produce kernel, overlap-opportunity, and fuse-pattern tables.
This skill's goal is not "run one benchmark." Its goal is a reproducible SGLang improvement loop: tune every framework fairly, prove whether SGLang is behind, explain the gap with profiler evidence, patch SGLang, and re-run the same model workload until the result is SOTA for the target environment.
Treat "SOTA" as "best observed, reproducible performance under the recorded model, workload, hardware, framework commits, precision, and SLA." Do not claim global SOTA without enough external evidence.
More from bbuf/sglang-auto-driven-skills
h100
SSH into host `h100_sglang`, enter Docker container `sglang_bbuf`, work in `/sgl-workspace/sglang`, and use the ready H100 remote environment for SGLang development and validation. Use when a task needs remote CUDA work, GPU-backed smoke tests, diffusion checks, or a safe remote copy instead of local-only execution.
34h100-sglang-diffusion
SSH into host `h100_sglang`, enter Docker container `sglang_bbuf`, work in `/data/bbuf/repos/sglang`, and use the ready H100 remote environment for SGLang **diffusion** development and validation. Use when a task needs diffusion model smoke tests, Triton/CUDA kernel validation, torch.compile diffusion checks, or a safe remote copy for diffusion-specific SGLang changes.
33sglang-prod-incident-triage
Replay-first debug flow for SGLang serving problems. Use when a live or recent server shows health-check failures, latency or throughput regressions, queue growth, timeouts, distributed stalls, crash dumps, wrong outputs after deploys, or PD/EP/HiCache issues, and the job is to turn the problem into a replay plus the right next debug tool.
33llm-serving-auto-benchmark
Framework-independent LLM serving benchmark skill for comparing SGLang, vLLM, TensorRT-LLM, or another serving framework. Use when a user wants to find the best deployment command for one model across multiple serving frameworks under the same workload, GPU budget, and latency SLA.
19llm-torch-profiler-analysis
Unified LLM torch-profiler triage skill for `sglang`, `vllm`, and `TensorRT-LLM`. Use it to inspect an existing `trace.json(.gz)` or profile directory, or to drive live profiling against a running server and return one three-table report with kernel, overlap-opportunity, and fuse-pattern tables.
18sglang-minimax-m2-series-optimization
PR-backed and current-main optimization manual for the `MiniMaxAI/MiniMax-M2` series, including M2, M2.1, M2.5, M2.7, and M2.7-highspeed. Use when Codex needs to recover, extend, or audit MiniMax-specific optimizations, TP QK norm/all-reduce behavior, parser contracts, distributed runtime behavior, quantized loading, or backend-specific validation.
15