About
infra.bea.ai is a systematic guide to AI infrastructure — from silicon to intelligence. Written and audited by engineers who build these systems.
The guide covers the full AI infrastructure stack, organized for progressive learning. Each module builds on the previous, and every topic is written to be practical and grounded in real systems.
Modules
- Foundations — Transformer architecture, hardware landscape, workload characterization, estimation
- CUDA — GPU programming, memory optimization, core kernels, FlashAttention, compilers
- Distributed — Collective communication, RDMA, parallelism strategies, interconnects, planning
- Training — Memory optimization, pipeline scheduling, fine-tuning, RLHF, fault tolerance
- Inference — Engine internals, quantization, speculative decoding, distributed serving, edge
- Apps & Agents — Multi-model routing, RAG infrastructure, agent execution and sandboxes
- Data — Training data pipelines, GPU feeding, storage systems, vector databases
- Platform — Scheduling, MLOps, observability, security, cost engineering, data center design