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Foundation Model Training and Inference on AWS

1 post · May 2026

What this trail covers

This post is the first in a series analyzing the building blocks for foundation model training and inference on AWS. It details the layered architecture of open-source software stacks, emphasizing the integration points between AWS infrastructure components (compute, network, storage) and OSS tools like Slurm, Kubernetes, PyTorch, JAX, Prometheus, and Grafana. It highlights the evolution of scaling laws and the convergent infrastructure requirements for pre-training, post-training, and inference.

All Hugging Face trails
  1. Building Blocks for Foundation Model Training and Inference on AWSThis post introduces the foundational building blocks for training and inference of foundation models on AWS. It details the infrastructure components (compute, network, storage), resource orchestration (Slurm, Kubernetes), ML software stack (PyTorch, JAX), and observability tools (Prometheus, Grafana) that underpin large-scale distributed training and inference. It analyzes how AWS infrastructure interacts with common OSS stacks across the foundation model lifecycle, focusing on systems bottlenecks and scaling characteristics.