Building Blocks for Foundation Model Training and Inference on AWS

AWS published an overview of infrastructure building blocks for foundation model training and inference, addressing how its compute, networking, and storage resources integrate with open-source software stacks. Authors Keita Watanabe, Pavel Belevich, and Aman Shanbhag describe a layered architecture spanning pre-training, post-training, and inference. The article references scaling law research by Kaplan and NVIDIA's "from one to three scaling laws" framing. Hugging Face hosted the post, which targets machine learning engineers using OSS frameworks like PyTorch, JAX, Slurm, and Kubernetes.