AWS publishes technical guide to foundation model training and inference infrastructure, analyzing how AWS compute, networking, and storage interact with open-source software stacks across the model lifecycle.

AWS outlines a layered architecture spanning hardware infrastructure (multi-node accelerator compute, high-bandwidth low-latency networking, distributed shared storage), resource orchestration (Slurm and Kubernetes), ML software frameworks (PyTorch and JAX), and observability tools (Prometheus and Grafana).

The guide details AWS accelerated computing instances including the P5 family with NVIDIA H100 and H200 GPUs, and the P6 family with NVIDIA Blackwell B200 and B300 architectures, specifying per-GPU peak Tensor throughput (ranging from 0.9895 PFLOPS for H100 to 13.5 PFLOPS for B300 in FP4), HBM capacity, and intra-node and inter-node bandwidth specifications.

The article targets machine learning engineers and researchers building foundation models on open-source frameworks, providing technical foundations for understanding systems bottlenecks and scaling characteristics across pre-training, post-training, and inference phases.