# Building Blocks for Foundation Model Training and Inference on AWS

DevFeed: [Building Blocks for Foundation Model Training and Inference on AWS](<https://devfeed.tech/articles/building-blocks-for-foundation-model-training-and-inference-on-aws-7088.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/amazon/foundation-model-building-blocks>)

Author: Keita Watanabe; Pavel Belevich; Aman Shanbhag

Published: 2026-05-11T23:18:26Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [foundation-models](<https://devfeed.tech/topics/foundation-models.md>), [Amazon Web Services](<https://devfeed.tech/topics/aws.md>), [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [distributed-training](<https://devfeed.tech/topics/distributed-training.md>), [cloud-infrastructure](<https://devfeed.tech/topics/cloud-infrastructure.md>), [Orchestration](<https://devfeed.tech/topics/orchestration.md>), [PyTorch](<https://devfeed.tech/topics/pytorch.md>), [Kubernetes](<https://devfeed.tech/topics/kubernetes.md>), [Grafana](<https://devfeed.tech/topics/grafana.md>), [Prometheus](<https://devfeed.tech/topics/prometheus.md>), [Monitoring](<https://devfeed.tech/topics/monitoring.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [aws](<https://devfeed.tech/tags/aws.md>), [building](<https://devfeed.tech/tags/building.md>), [distributed-training](<https://devfeed.tech/tags/distributed-training.md>), [frameworks](<https://devfeed.tech/tags/frameworks.md>), [grafana](<https://devfeed.tech/tags/grafana.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [inference](<https://devfeed.tech/tags/inference.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [kubernetes](<https://devfeed.tech/tags/kubernetes.md>), [latency](<https://devfeed.tech/tags/latency.md>), [low-latency](<https://devfeed.tech/tags/low-latency.md>), [model](<https://devfeed.tech/tags/model.md>), [model-development](<https://devfeed.tech/tags/model-development.md>), [monitoring](<https://devfeed.tech/tags/monitoring.md>), [networking](<https://devfeed.tech/tags/networking.md>), [observability](<https://devfeed.tech/tags/observability.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [prometheus](<https://devfeed.tech/tags/prometheus.md>), [training](<https://devfeed.tech/tags/training.md>)

## AI overview

An introductory technical article about the infrastructure and open-source software building blocks required for foundation-model pre-training, post-training, and inference on AWS. It discusses accelerator compute, low-latency networking, distributed storage, orchestration, ML frameworks, and observability tools.

## Source excerpt

Figure: Adapted from "AI's Three Scaling Laws, Explained" (NVIDIA Blog). 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.