AWS and Hugging Face Map the Full Model Stack

AWS engineers published a comprehensive reference architecture on Hugging Face covering every layer needed to train and serve foundation models – from P6 Blackwell instances with 14.4 TB/s NVLink to orchestration via SageMaker HyperPod, and inference through vLLM, SGLang, and NVIDIA Dynamo. The guide treats infrastructure, software, and observability as tightly coupled, arguing that a misconfigured driver or network layer bottlenecks performance as much as bad parallelism. It also frames the shift from pure pre-training scaling to three regimes: pre-training, post-training, and test-time compute. This kind of end-to-end systems documentation has been notably absent from cloud providers until recently – Google published similar Vertex AI architecture guides in Q1, and the pattern reflects growing enterprise demand for operational clarity rather than just API access.