Wafer launches comprehensive AI performance engineering repo, starting with deep dive on Transformer inference
garrytan · x · 2026-09-12
Wafer.ai launched what it bills as the most comprehensive AI performance engineering resource repo, retweeted by Garry Tan, who says fully understanding the first resource puts you ahead of 90% of people on inference.
Part 1, "All About Transformer Inference" (from How To Scale Your Model), covers:
- arithmetic intensity of linear layers and attention across prefill and decode
- KV cache sizing by layer count, KV heads, head dimension, sequence length, and precision
- the compute/HBM bandwidth crossover and how batch size and quantization shift it
- decode latency and serving decisions
More resources from the repo will be posted sequentially.
Related event: Wafer Launches Comprehensive AI Performance Engineering Resource Library(2 posts)→
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