wafer-ai/gpu-perf-engineering-resources resource

Health v2 · maintenance only

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Adoption not part of the score

2080 stars · 229 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A curated list of resources for learning AI/GPU performance engineering, ordered from GPU fundamentals through kernel optimization, inference engines, and distributed inference systems. It links to original papers, official documentation, and creator repositories rather than being a software tool itself.

Use cases

• learn gpu performance engineering from scratch
• find resources on cuda kernel optimization
• understand how llm inference engines work
• study distributed inference and model parallelism
• learn triton and cutlass programming
• prepare for a gpu performance engineering role
• find papers on kv cache and speculative decoding

When to choose

• you want a structured, ordered learning path from GPU basics to production inference
• you prefer primary sources like papers and official docs over tutorials
• you need a reference map of the inference serving stack (kernels, engines, distributed systems)

When to avoid

• you need runnable code or a library rather than a reading list
• you want beginner-friendly step-by-step tutorials with exercises
• you are looking for general machine learning or data science resources outside GPU performance

Facets

learning-resource · maturity active

gpu-computing llm-inference benchmarking machine-learning developer-tools gpu-computing large-language-models deep-learning tutorials performance artificial-intelligence python cross-platform awesome-list curated-resources cuda triton kernel-optimization inference-serving distributed-systems profiling gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="wafer-ai/gpu-perf-engineering-resources")