The AWS Machine Learning Blog presents a walkthrough for deploying Qwen3.8-2.4T-A95B, described as a 2.4-trillion-parameter open-weight model, on Amazon SageMaker HyperPod using vLLM. The guide addresses cluster provisioning, NVFP4 quantization, and setting up an OpenAI-compatible endpoint. It notes that the deployment supports built-in reasoning, tool calling, and native MTP speculative decoding.
Why it matters
Running very large open-weight models requires specific infrastructure and optimization steps. By documenting the process on SageMaker HyperPod with vLLM, the post outlines a path for serving such a model with a standard, OpenAI-compatible interface.
Who should care
Machine learning engineers and teams evaluating how to provision clusters and serve large open-weight models on AWS may find the walkthrough directly relevant.