Building Blocks for Foundation Model Training and Inference on AWS

2026-05-11 · Source: Hugging Face - Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Advanced, extended

Summary

This article details the foundational infrastructure and software stack for training and inferencing large-scale foundation models on Amazon Web Services (AWS). It outlines how the evolution of AI scaling, beyond just pre-training, now encompasses post-training and test-time compute, necessitating tightly coupled accelerator compute, high-bandwidth low-latency networking, and distributed storage. The post introduces AWS's building blocks, including Amazon EC2 P-instance families (P5, P5e, P5en, P6, P6e-GB200) featuring NVIDIA H100, H200, B200, and B300 GPUs, with HBM capacities up to 288 GB and aggregate NVLink bandwidths up to 14.4 TB/s. It also covers Elastic Fabric Adapter (EFA) for inter-node communication, offering up to 800 GB/s aggregate bandwidth, and tiered storage solutions like local NVMe SSDs, Amazon FSx for Lustre, and Amazon S3. The article further describes resource orchestration with Slurm and Kubernetes via Amazon SageMaker HyperPod, the ML software stack (kernel drivers, CUDA, NCCL, PyTorch, and distributed frameworks like Hugging Face Transformers, NVIDIA Megatron Core, veRL, vLLM, SGLang), and observability using Prometheus and Grafana for GPU, network, and application telemetry.

Key takeaway

For MLOps Engineers and AI Scientists building large-scale foundation models, understanding the intricate interplay between AWS infrastructure and the open-source ML stack is crucial. Your ability to diagnose bottlenecks and optimize performance hinges on familiarity with EC2 P-instances, EFA networking, tiered storage, and orchestration tools like SageMaker HyperPod with Slurm or Kubernetes. Prioritize robust observability with Prometheus and Grafana to proactively identify and resolve performance issues across the model lifecycle.

Key insights

Foundation model scaling now requires integrated infrastructure for pre-training, post-training, and inference, emphasizing compute, network, and storage.

Principles

• Scaling involves pre-training, post-training, and test-time compute.
• Observability is critical for diagnosing distributed training performance.
• Efficient communication is key for multi-GPU training and inference.

Method

AWS infrastructure supports a layered open-source software stack, from hardware enablement and accelerator runtimes to ML frameworks and distributed training/inference, orchestrated by Slurm or Kubernetes.

In practice

• Utilize NVIDIA H100/H200/B200/B300 GPUs on EC2 P-instances.
• Deploy EFA for low-latency inter-node communication.
• Implement Prometheus/Grafana for cluster health monitoring.

Topics

• Foundation Models
• AWS Infrastructure
• GPU Accelerators
• Distributed Training
• Resource Orchestration

Code references

• aws/aws-parallelcluster
• NVIDIA/KAI-Scheduler
• NVIDIA/k8s-device-plugin
• NVIDIA/gdrcopy
• amzn/amzn-drivers

Best for: Machine Learning Engineer, AI Scientist, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Hugging Face - Blog.