Building Blocks for Foundation Model Training and Inference on AWS

As foundation model scaling expands beyond pre-training to include post-training and test-time compute, infrastructure requirements increasingly converge on tightly coupled accelerator compute, high-bandwidth low-latency networking, and distributed storage. According to a Hugging Face post examining building blocks on AWS, the foundation model lifecycle relies heavily on open-source software ecosystems spanning ML frameworks, cluster resource management, and observability tools like PyTorch, JAX, Slurm, Kubernetes, Prometheus, and Grafana. To support these workloads, AWS offers accelerated computing instances through its Amazon EC2 P-instance families, which feature NVIDIA H100, H200, and Blackwell B200 or Ultra B300 architectures. These multi-node systems utilize internal scale-up via NVLink and NVSwitch alongside external scale-out using Elastic Fabric Adapter (EFA). EFA provides OS-bypass remote direct memory access across multiple versions, progressively reducing latency and boosting collective communication performance for workloads spanning thousands of instances. Additionally, data management for streaming corpora, multi-terabyte checkpoints, and large-scale inference relies on a tiered storage hierarchy comprising local NVMe instance store, Lustre shared file systems, and Amazon S3 for durable persistence. This integration of AWS infrastructure and open-source tools aims to provide a technical foundation for addressing systems bottlenecks and scaling characteristics across training and inference workflows.