Deploying Qwen3.8-2.4T-A95B on Amazon SageMaker HyperPod with vLLM
Deploy 2.4T parameters on SageMaker: Qwen3.8 + vLLM walkthrough with reasoning and tool calling.
Why it matters
A vendor how-to that operationalizes a major open-weight model on managed infrastructure. Practitioners who run large open models will find the quantization and speculative decoding specifics immediately useful; the deployment pattern (HyperPod + vLLM) is becoming standard for production inference at scale.
The key facts
12 to knowQwen3.8-2.4T-A95B (2.4-trillion-parameter open-weight model)
Amazon SageMaker HyperPod deployment target
NVFP4 quantization technique
vLLM serving engine
Native MTP speculative decoding optimization
OpenAI-compatible endpoint
Built-in reasoning and tool calling capabilities
Model: Qwen 3.8 2.4T-parameter open-weight
Platform: Amazon SageMaker HyperPod with vLLM
Quantization: NVFP4
Features: OpenAI-compatible endpoint, native reasoning, tool calling, MTP speculative decoding
Published: Sep 09 2026
Go to the source
AWS Machine Learning Blogaws.amazon.com
Publisher excerpt: Learn how to deploy Qwen3.8-2.4T-A95B, a 2.4-trillion-parameter open-weight model, on Amazon SageMaker HyperPod with vLLM. This walkthrough covers cluster provisioning, NVFP4 quantization, and an OpenAI-compatible endpoint with built-in reasoning, tool calling, and native MTP speculative decoding.