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

This article introduces AWS Deep Learning Containers for Ray Serve, a pre-built Docker image that simplifies model inference deployment as an alternative to the deprecated TorchServe.

• TorchServe is no longer maintained; Ray Serve DLC provides a supported, tested alternative with GPU stack and dependencies pre-assembled
• DLC includes PyTorch, Ray Serve, FastAPI, Uvicorn, and utilities for vision, audio, and multimodal workloads with NVIDIA hardware acceleration
• Demonstrates deploying Qwen3-VL-2B vision-language model on Amazon EKS using a single g5.xlarge GPU instance
• Ray Serve uses Python class decorators instead of TorchServe handlers, eliminating model archiver and configuration files
• Includes automated deployment scripts for EKS cluster setup, GPU node provisioning, and Ray Serve application deployment
• Eliminates version drift, simplifies upgrades via tag swaps, and provides AWS-managed security patches

Ray Serve DLC enables teams to migrate from TorchServe while focusing on model code rather than infrastructure maintenance and dependency management.