Building Blocks for Foundation Model Training and Inference on AWS

This article explains the infrastructure building blocks on AWS for training and inferencing foundation models at scale.

• •Foundation model scaling now encompasses three regimes: pre-training, post-training (SFT, RL), and test-time compute, each with converging infrastructure requirements.
• •AWS provides accelerated compute instances (P5, P6 families) with NVIDIA GPUs (H100, H200, B200, B300) offering varying peak throughput and memory capacity.
• •Multi-GPU communication relies on NVLink/NVSwitch for intra-node connectivity and Elastic Fabric Adapter (EFA) for inter-node RDMA communication to minimize latency.
• •Distributed storage hierarchy uses local NVMe for hot data, Amazon FSx for Lustre for shared high-throughput access, and S3 for durable persistence.
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