AWS Building Blocks for Foundation Model Training and Inference

A technical blog post by AWS and NVIDIA engineers details the infrastructure and software stack required for large-scale foundation model training and inference on AWS. The post, authored by Aman Shanbhag (NVIDIA), Pavel Belevich, and Keita Watanabe (both AWS), outlines a four-layer architecture: infrastructure (EC2 P-instances with NVIDIA GPUs, EFA networking, tiered storage), resource orchestration (Slurm, Kubernetes, SageMaker HyperPod), ML software stack (CUDA, NCCL, PyTorch, distributed frameworks), and observability (Prometheus, Grafana, DCGM). Key hardware includes P5 (H100), P5e (H200), P6 (B200/B300) instances, and P6e-GB200 UltraServers with up to 72 Blackwell GPUs. The post emphasizes convergent requirements across pre-training, post-training, and inference: tightly coupled compute, high-bandwidth low-latency networking, and distributed storage. It also covers open-source tools like Slurm, Kubernetes, Kueue, Volcano, and frameworks like Megatron Core, NeMo, vLLM, and SGLang. Observability is highlighted as critical for debugging at scale, with GPU health monitoring via DCGM-Exporter and Grafana dashboards.