Building Blocks for Foundation Model Training and Inference on AWS

Source: HuggingFace Date: 2026-05-11 URL: https://huggingface.co/blog/amazon/foundation-model-building-blocks

Summary

A HuggingFace/Amazon joint reference guide covering the full infrastructure stack required to train and serve foundation models on AWS: accelerated compute (P5/P6 families with H100–B300 GPUs), high-bandwidth networking (EFA, UltraClusters, NVLink domains up to 72 GPUs), parallel storage (FSx for Lustre + S3), orchestration (Slurm and Kubernetes/Kueue with topology-aware placement), and the five-layer ML software stack from CUDA through PyTorch/FSDP2/Megatron/veRL to vLLM inference. The article frames pre-training, post-training, and test-time compute as converging onto a shared infrastructure requirement — cluster, network, storage — rather than separate concerns.

Implications

• Infrastructure cost floor thread. Documenting the full B200/B300 stack publicly normalizes the capital requirements for frontier training. Grace-Blackwell UltraServers (72 GPUs, 288GB HBM3e each, 14.4 TB/s NVLink) represent the current ceiling; this post is a calibration point for what “frontier infrastructure” actually costs to assemble.
• Open-source ecosystem maturity. The stack is almost entirely OSS: PyTorch, NCCL, FSDP2, vLLM, SGLang, veRL for RLHF. The commodity layer is solidifying — the competitive surface is shifting to who assembles the stack fastest, not who owns the pieces.
• Test-time compute thread. Framing pre-training, post-training, and inference as a unified infrastructure problem signals that test-time scaling (inference-time search, chain-of-thought) is now a first-class infrastructure concern, not an afterthought bolted onto a training cluster.