Deploying Qwen3.8-2.4T-A95B on Amazon SageMaker HyperPod with vLLM | Amazon Web Services
On August 12, 2026, Alibaba’s Qwen team released Qwen3.8-2.4T-A95B. This is the first time a Qwen-Max-class model has been made available as open weights. With 2.4 trillion total parameters (95 billion activated per token), a hybrid linear-plus-full-attention architecture, and native context up to 262K tokens (extensible to 1M), Qwen3.8 targets the most demanding agentic and reasoning workloads. These include multi-step coding, long-horizon planning, and autonomous tool use.
Open weights models give you full control. Data stays within your infrastructure, inference behavior can be customized, and there are no per-token API fees at scale. The trade-off is operational: hosting a 2.4T-parameter model requires purpose-built GPU infrastructure and an optimized serving stack.
In this post we show how to deploy Qwen3.8-2.4T-A95B on Amazon SageMaker HyperPod using vLLMon a ml.p6-b300 instance (8× NVIDIA B300 Blackwell Ultra GPUs). We cover the full path from cluster provisioning to an OpenAI-compatible endpoint, including vLLM...
Copyright of this story solely belongs to aws.amazon.com. To see the full text click HERE
