sglang-diffusion-ako4all-kernel

To optimize an SGLang diffusion kernel using AKO4ALL, you bootstrap a complete tuning loop by ensuring repository hygiene, building a tailored microbenchmark harness, running iterative ncu-guided profiling, and porting the best version back to sglang.

The best way to set up ncu-guided kernel tuning for SGLang is to build and run a tailored AKO4ALL harness that mirrors the real kernel environment, perform baseline microbenchmarks, and iterate using ncu profiling data.

You validate kernel optimization results by running model-level denoise tests to ensure production readiness, then packaging the validated kernel version as PR-ready artifacts for integration back into SGLang.

Yes, you need a clean AKO4ALL repository to start kernel optimization. The process requires AKO4ALL preflight and hygiene to ensure a clean starting point, along with access to microbench and bench tooling.

You should use AKO4ALL for SGLang kernel optimization when a hotspot is confirmed and you require AKO4ALL hygiene, custom harness setup, and ncu-guided iteration to bootstrap a complete end-to-end tuning loop.