AI Infrastructure
From Silicon to Intelligence
A systematic guide to AI infrastructure — from GPU kernels to production platforms, written and audited by engineers who build these systems.
24 / 214 chapters published
Modules
Foundations
Transformer architecture, hardware landscape, workload characterization, and back-of-envelope estimation.
CUDA & Kernel Engineering
From writing your first CUDA kernel to implementing FlashAttention — making one GPU go fast.
Distributed GPU Systems
Making many GPUs work together — communication primitives, parallelism strategies, and network interconnects.
Training Systems
End-to-end training infrastructure from optimizer math to fault tolerance at 10K GPUs.
Inference Systems
The core of modern AI infrastructure — from KV cache management to edge deployment.
Data & Storage
The forgotten half of AI infrastructure — training data pipelines, GPU feeding, storage systems, and vector databases.
Platform & Infrastructure
Running AI at scale — scheduling, MLOps, observability, security, cost engineering, and physical data center design.
Apps & Agents
Building with models — multi-model routing, RAG pipelines, and agent execution infrastructure.