GUIDE · MODELS · #182
Deploying Qwen3.8-2.4T-A95B on Amazon SageMaker HyperPod with vLLM
AWS Machine Learning provides a walkthrough for deploying Qwen3.8-2.4T-A95B, a 2.4‑trillion‑parameter open‑weight model, on Amazon SageMaker HyperPod using vLLM. The guide covers cluster provisioning, NVFP4 quantization, and creating an OpenAI‑compatible endpoint with built‑in reasoning, tool calling, and native MTP speculative decoding.
KEY POINTS
- AWS Machine Learning provides a walkthrough for deploying Qwen3.8-2.4T-A95B, a 2.4‑trillion‑parameter open‑weight model, on Amazon SageMaker HyperPod using vLLM.
- The guide covers cluster provisioning, NVFP4 quantization, and creating an OpenAI‑compatible endpoint with built‑in reasoning, tool calling, and native MTP speculative decoding.
- Shows a practical path to run a very large open‑weight model on managed AWS infrastructure with performance optimizations and OpenAI‑style serving features, lowering barriers to production deployment.
WHY IT MATTERS
Shows a practical path to run a very large open‑weight model on managed AWS infrastructure with performance optimizations and OpenAI‑style serving features, lowering barriers to production deployment.