On May 11, 2026, AWS publishes a technical post by Keita Watanabe, Pavel Belevich and Aman Shanbhag that lays out

On May 11, 2026, AWS engineers Keita Watanabe, Pavel Belevich and Aman Shanbhag published a technical post that maps the infrastructure and open‑source software building blocks required to scale foundation‑model pre‑training, post‑training and inference. The article aims squarely at machine‑learning engineers and researchers who use open‑source stacks, and it frames the work as a practical inventory intended to surface system bottlenecks and scaling characteristics across the full model lifecycle. For practitioners, the post signals where integration effort and operational attention are most likely to pay off when moving large models from experiment to production.

At the software level the authors describe a layered open‑source architecture. The cluster layer is anchored by resource managers such as Slurm and Kubernetes; the model and training layer centers on frameworks like PyTorch and JAX for development and distributed training; and an observability layer commonly uses Prometheus for metrics and Grafana for visualization and alerting. A diagram in the post shows how these layers interact and emphasizes that observability must span infrastructure, orchestration and ML frameworks to reveal cross‑layer failures and performance degradations.

The post situates that layered architecture within evolving scaling regimes. It cites Kaplan et al. (2020) for the classic compute‑driven view and invokes NVIDIA’s “from one to three scaling laws” framing to argue that performance gains now also come from post‑training methods — supervised fine‑tuning and RL‑based approaches — and from spending compute at inference time through long‑thinking, search/verification and multi‑sample strategies. In short, scaling is presented as multi‑dimensional rather than a single compute curve, which changes the priorities for where operators must invest time and resources.

Those multi‑regime demands produce convergent infrastructure requirements across the lifecycle: tightly coupled accelerators to keep parallel workloads efficient; high‑bandwidth, low‑latency networking to support collective communication primitives; and distributed storage backends capable of moving large datasets and frequent checkpoints. The authors stress that orchestration grows in importance as clusters scale, and that both application‑level and hardware‑level observability are essential to maintain cluster health and diagnose performance pathologies at scale.

This article is the first in a planned series. Subsequent posts, the authors say, will walk through how the layered architecture is realized on AWS by progressing through infrastructure, resource orchestration, the ML software stack and observability. For engineers and researchers the series promises concrete integration points between AWS components and open‑source tools and practical guidance to identify and mitigate bottlenecks across pre‑training, post‑training and inference workloads.