ai-infra-curriculum/ai-infra-performance-learning

A curated resource list for learning AI performance engineering, from GPU fundamentals to production inference.

Code, labs, and resources for O'Reilly AI Systems Performance Engineering: GPU optimization, distributed training, inference scaling, and full-stack tuning.

AI Infrastructure Engineer Learning Track - Production ML infrastructure curriculum (2-4 years experience)

Curated collection of AI inference engineering resources — LLM serving, GPU kernels, quantization, distributed inference, and production deployment. Compiled from the AER Labs community.

AI Infrastructure Junior Engineer Learning Track - Comprehensive curriculum for entry-level ML infrastructure engineers (0-2 years experience)

100 days of LLM inference engineering — daily posts, experiments, and visualizations

An autonomous agent that profiles, optimizes, and validates AI training code.

A curated list of resources for ML Systems Engineering - hardware, compilers, distributed training, inference, and production operations.

Optimized GPU compiler for LLM inference. Choose from a list of optimized recipes or optimize your own model via kernel fusion, autotuning, and advanced scheduling. Run benchmarks across different GPU types and configurations, track results and share experiments with the community.