# Amazon — Simplify and support your TorchServe workloads using Ray Serve Deep Learning Containers

- Company: Amazon (amazon.com)
- Announced: 2026-09-09T15:51:29+00:00
- Category: developer-tool-release
- Coverage: not counted
- Announcement: yes
- Group: announcements
- Source: https://aws.amazon.com/blogs/machine-learning/simplify-and-support-your-torchserve-workloads-using-ray-serve-deep-learning-containers/
- Record: https://forck.live/items/9588-simplify-and-support-your-torchserve-workloads-using-ray-serve-deep-learning
- Subject: Bedrock / Nova

AWS announced the Ray Serve Deep Learning Container (DLC), a pre-built Docker image for model inference that bundles PyTorch, Ray Serve, and the GPU stack. The post demonstrates deploying a Qwen3-VL-2B vision-language model on Amazon EKS using the new container.

## Evidence

Verbatim from https://aws.amazon.com/blogs/machine-learning/simplify-and-support-your-torchserve-workloads-using-ray-serve-deep-learning-containers/:

> With the launch of the Ray Serve DLC , that same approach now extends to inference. You get a container purpose-built for serving models behind an HTTP endpoint, maintained and tested by AWS, with the full inference stack already assembled.

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