Building Blocks for Foundation Model Training and Inference on AWS

This post introduces the foundational building blocks for training and inference of foundation models on AWS. It details the infrastructure components (compute, network, storage), resource orchestration (Slurm, Kubernetes), ML software stack (PyTorch, JAX), and observability tools (Prometheus, Grafana) that underpin large-scale distributed training and inference. It analyzes how AWS infrastructure interacts with common OSS stacks across the foundation model lifecycle, focusing on systems bottlenecks and scaling characteristics.