Building Blocks for Foundation Model Training and Inference on AWS

Summary

The post explains how foundation-model scaling now spans pre-training, post-training, and test-time compute, creating shared infrastructure requirements rather than a single pre-training curve. It examines how AWS infrastructure and open-source tools support these workloads across accelerated compute, networking, storage, orchestration, ML frameworks, and observability. Technical detail includes NVIDIA H100, H200, B200, and B300 GPU characteristics, NVLink/NVSwitch scale-up communication, and EFA-based scale-out networking for distributed collectives. The architecture also incorporates EC2 P instances, tiered storage, Slurm, Kubernetes, SageMaker HyperPod, CUDA, NCCL, PyTorch, Prometheus, and Grafana. The conclusion presents integration points between these layers as the basis for diagnosing bottlenecks and making scaling decisions across the foundation-model lifecycle.

Takeaways

1. 1

   Foundation-model scaling is described through three regimes: pre-training, post-training, and test-time compute, with infrastructure requirements that overlap across all three.

2. 2

   Within multi-GPU AWS instances, NVLink/NVSwitch provides scale-up communication, while EFA provides OS-bypass RDMA networking for cross-node collectives.

3. 3

   The proposed architecture treats observability as spanning infrastructure, orchestration, and ML software, with Prometheus, Grafana, and DCGM-based GPU health monitoring used to identify performance and hardware problems.