@gpusteve: want to learn the fundamentals of how we serve extremely fast oss models? start here link in thread for deep ball knowl…

X AI KOLs Timeline News
ai-performance-engineering gpu-optimization resource-list open-source machine-learning tutorial

Summary

The tweet announces a major update to an AI performance engineering resource list, providing comprehensive materials for learning GPU and AI optimization techniques.

want to learn the fundamentals of how we serve extremely fast oss models? start here link in thread for deep ball knowledge
Original Article
View Cached Full Text

Cached at: 08/24/26, 11:49 AM

want to learn the fundamentals of how we serve extremely fast oss models? start here

link in thread for deep ball knowledge

ofc

sacred text

ofc

:o

computah, transfer ball knowledge

just released the most comprehensive resource list for learning gpu and ai perf engineering btw

AI Performance Engineering v2 (link in thread )

this version starts with how a single inference request works, then builds through the cuda execution model, roofline, transformer arithmetic, ttft/tpot/goodput, and kernel optimization.

these are the resources we use to deeply understand how to optimize inference systems.

also added:

  • flashattention-4, blackwell tensor memory, and low-precision tensor cores

  • continuous batching, kv-cache systems, quantization, speculative decoding, and structured decoding

  • moe serving, collectives, topology, and prefill/decode disaggregation

  • blackwell ultra, mi350/cdna 4, ironwood, and trainium3

  • kernelbench-verified and sol-execbench, plus a separate watchlist for rubin and cdna 5

Similar Articles

@zostaff: https://x.com/zostaff/status/2065069139341742588

X AI KOLs Timeline

This article maps the optimal AI-augmented path to becoming a GPU/CUDA engineer, highlighting compensation ranges and the growing demand for inference optimization specialists. It provides a realistic timeline and emphasizes the use of AI tools to accelerate learning.