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Nabilhassan12345
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sglang-diffusion-ako4all-kernel

Optimizes existing SGLang diffusion kernels using AKO4ALL harnesses with benchmarking and validation workflows.

Updated Apr 7, 2026
Search Tags:#benchmarking#ncu#diffusion#sglang#kernel-optimization#ako4all
One-click install
npx skills add https://github.com/Nabilhassan12345/voice-ai-workspace --skill sglang-diffusion-ako4all-kernel
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sglang-diffusion-ako4all-kernel
Source: https://github.com/Nabilhassan12345/voice-ai-workspace/tree/main/sglang/python/sglang/multimodal_gen/.claude/skills/sglang-diffusion-ako4all-kernel
Command: npx skills add https://github.com/Nabilhassan12345/voice-ai-workspace --skill sglang-diffusion-ako4all-kernel

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill streamlines the end-to-end optimization of an existing SGLang diffusion kernel by coordinating AKO4ALL workflows, preflight hygiene, and model-level validation to deliver faster, more reliable denoise performance. It also guides when a sibling AKO4ALL repo must be cloned or refreshed before starting kernel tuning work.

Core Features & Use Cases

  • AKO4ALL preflight and repo hygiene checks to ensure a clean, synchronized workspace.
  • Workflow guidance for scoping the kernel, bootstrapping an AKO4ALL harness, establishing a baseline, iterating with AKO4ALL, porting improvements back to SGLang, and validating with real-model denoise runs.
  • End-to-end validation gates, including syntax/import checks, unit tests, kernel benchmarks, and PR artifact preparation to support production-grade changes.

Quick Start

Clone or refresh the AKO4ALL repo under your base directory, then run the AKO4ALL kernel harness against your SGLang diffusion kernel to start the optimization loop.

Frequently Asked Questions about sglang-diffusion-ako4all-kernel

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I optimize an SGLang diffusion kernel using AKO4ALL?▼

Optimizing an SGLang diffusion kernel with AKO4ALL involves bootstrapping a custom harness, establishing a baseline, iterating on kernel changes, and porting validated improvements back to the SGLang repository for enhanced denoise performance.

What is the workflow for tuning diffusion kernels with an AKO4ALL harness?▼

Tuning diffusion kernels with an AKO4ALL harness requires running preflight hygiene checks, establishing baselines, iterating with microbenchmarks and NCUs, and completing end-to-end denoise validation to ensure production-ready improvements.

Do I need to clone the AKO4ALL repository before tuning SGLang kernels?▼

Yes, cloning or refreshing the sibling AKO4ALL repository under your base directory is required before starting kernel tuning to ensure a clean, synchronized workspace for the optimization loop.

What validation gates are required when porting kernel improvements back to SGLang?▼

Required validation gates for porting kernel improvements back to SGLang include syntax and import checks, unit tests, kernel benchmarks, real-model denoise validation, and PR artifact generation to support production-grade changes.

When should I use NCU benchmarks for SGLang kernel optimization?▼

NCU benchmarks are used during the iterative AKO4ALL tuning phase to profile and validate kernel improvements before transferring the optimized wins back to the SGLang diffusion kernel for final validation.

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