Building Blocks for Foundation Model Training and Inference on AWS

Foundation model scaling has evolved beyond pre-training to include post-training and test-time compute, creating convergent infrastructure requirements. These requirements include tightly coupled accelerator compute, high-bandwidth networking, and distributed storage, often managed by an open-source software ecosystem. This analysis explores how AWS infrastructure, featuring NVIDIA GPUs like the H100 and B200, integrates with tools such as Kubernetes, PyTorch, and Prometheus. The goal is to provide a technical foundation for understanding system bottlenecks and scaling characteristics for large-scale distributed training and inference.