---
title: "Building Blocks for Foundation Model Training and Inference on AWS"
description: "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."
---

# Building Blocks for Foundation Model Training and Inference on AWS

[Hugging Face](https://yomu.fyi/company/hugging-face) · Keita Watanabe, Pavel Belevich, Aman Shanbhag · May 11, 2026

**Type:** Explainer

## 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.

## Context

Foundation-model scaling increasingly includes pre-training, post-training, and test-time compute. These regimes require tightly coupled accelerator compute, high-bandwidth low-latency networking, scalable distributed storage, resource orchestration, and observability, while performance can be constrained by issues such as collective communication, memory movement, driver configuration, or saturated network links.

## Approach / What changed

The post presents a four-layer AWS architecture for foundation-model training and inference: infrastructure using EC2 P instances, EFA networking, and tiered storage; resource orchestration using Slurm, Kubernetes, and SageMaker HyperPod; an ML software stack spanning drivers, CUDA, NCCL, and PyTorch; and observability using Prometheus, Grafana, and GPU health monitoring. It relates these AWS components to common open-source workflows and describes their scaling and integration points.

## Takeaways

- Foundation-model scaling is described through three regimes: pre-training, post-training, and test-time compute, with infrastructure requirements that overlap across all three.
- Within multi-GPU AWS instances, NVLink/NVSwitch provides scale-up communication, while EFA provides OS-bypass RDMA networking for cross-node collectives.
- 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.

**Tags:** [AWS](https://yomu.fyi/topic/aws), [Kubernetes](https://yomu.fyi/topic/kubernetes), [Machine Learning](https://yomu.fyi/topic/machine-learning), [Observability](https://yomu.fyi/topic/observability), [Performance](https://yomu.fyi/topic/performance)

- Source: [Hugging Face](https://huggingface.co/blog/amazon/foundation-model-building-blocks)
- Source URL: https://huggingface.co/blog/amazon/foundation-model-building-blocks
- Ingested by Yomu: 2026-08-29T01:48:49.262Z

[Read original post](https://huggingface.co/blog/amazon/foundation-model-building-blocks)