Building Blocks for Foundation Model Training and Inference on AWS

The landscape of foundation models has significantly advanced, moving beyond the traditional emphasis on pre-training computation. Scaling now involves post-training techniques like supervised fine-tuning and reinforcement learning, as well as test-time compute strategies such as "long thinking" and multi-sample verification. This evolution necessitates a convergent infrastructure focused on tightly coupled accelerator compute, high-bandwidth low-latency networking, and robust distributed storage. Effective orchestration for resource management and comprehensive observability are also critical for maintaining cluster health and diagnosing performance issues.

A key driver in this evolving ecosystem is the increasing reliance on open-source software (OSS). This includes frameworks for model development (e.g., PyTorch, JAX), cluster resource management (e.g., Slurm, Kubernetes), and operational tooling for monitoring and visualization (e.g., Prometheus, Grafana). This layered architecture, with hardware infrastructure supporting resource orchestration and ML frameworks, underscores the importance of seamless integration.

This article focuses on how AWS infrastructure integrates with these common OSS stacks throughout the foundation model lifecycle. It highlights AWS's offerings, including multi-node accelerator compute, high-bandwidth low-latency networking, and distributed shared storage, along with associated managed services. The primary aim is to provide a technical foundation for understanding system bottlenecks and scaling characteristics across pre-training, post-training, and inference.

AWS offers a range of NVIDIA GPUs through its Amazon EC2 accelerated computing instances, such as the P5 and P6 instance families. These instances provide significant peak Tensor throughput, high HBM capacity and bandwidth, and advanced interconnect bandwidth. This allows for scalable compute resources essential for large-scale foundation model development.

For multi-GPU instances, efficient communication is crucial. Internal scale-up via NVLink/NVSwitch provides high-bandwidth, low-latency GPU-to-GPU connectivity within a node, ensuring optimal performance for demanding workloads. This comprehensive approach to infrastructure and software integration on AWS provides a robust environment for the next generation of AI model development and deployment.