Figure 1: The layered architecture of open-source software stacks for foundation model training and inference

This post is intended for machine learning engineers and researchers involved in foundation model training and inference, with particular attention to workflows built atop OSS frameworks. It analyzes how AWS infrastructure—including multi-node accelerator compute, high-bandwidth low-latency networking, distributed shared storage, and associated managed services—interacts with common OSS stacks across the foundation model lifecycle. The primary goal is to provide a technical foundation for understanding systems bottlenecks and scaling characteristics spanning pre-training, post-training, and inference. This introductory post surfaces the overall system architecture, emphasizing the integration points between AWS infrastructure components and OSS tools that underpin large-scale distributed training and inference.

The remainder of this series examines how this layered architecture is realized on AWS, progressing through infrastructure, resource orchestration, the ML software stack, and observability. The following sections preview each layer.

As illustrated in Figure 1, infrastructure is anchored by three coupled building blocks—accelerated compute with large device memory, wide-bandwidth interconnect for collective communication, and scalable distributed storage for data and checkpoints.