Building Blocks for Foundation Model Training and Inference on AWS

['For a long time, "scaling" in foundation models mostly meant one thing: spend more compute on pre-training and capabilities rise. That intuition was supported by empirical work such as Kaplan et al. (2020), which reported predictable power-law trends in loss as you scale model parameters, dataset size, and training compute.

In practice, these trends justified sustained investment in large-scale accelerator capacity and the surrounding distributed infrastructure needed to keep it efficiently utilized. But the frontier has evolved—and scaling is no longer a single curve. NVIDIA\'s "from one to three scaling laws" framing usefully emphasizes that, beyond pre-training, performance increasingly scales through post-training (e.g., supervised fine-tuning (SFT) and reinforcement learning (RL)-based methods) and through test-time compute ("long thinking," search/verification, multi-sample strategies).', 'Taken together, these scaling regimes push the foundation-model lifecycle—pre-training, post-training, and inference—toward convergent infrastructure requirements: tightly coupled accelerator compute, a high-bandwidth low-latency network, and a distributed storage backend.

They also raise the importance of orchestration for resource management, and of application- and hardware-level observability to maintain cluster health and diagnose performance pathologies at scale.', 'Another key trend is the increasing reliance of the foundation-model lifecycle on an open-source software (OSS) ecosystem that spans model development frameworks, cluster resource management, and operational tooling. At the cluster layer, resource management is typically provided by systems such as Slurm and Kubernetes. Model development and distributed training are commonly implemented in frameworks such as PyTorch and JAX.

Monitoring and visualization—that is, observability—are often achieved using Prometheus for metrics collection and Grafana for visualization and alerting, positioned as an operational layer atop infrastructure and resource management.', '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 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.', '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.