TorchServe is no longer maintained. This creates a situation where teams are responsible for managing the entire GPU inference stack. The AWS Ray Serve Deep Learning Container is a supported container that includes the framework, GPU drivers, and serving layer. It is pre-tested and assembled, simplifying deployment.
This post demonstrates deploying a vision-language model on Amazon EKS using the Ray Serve DLC on a single GPU node. The container provides a streamlined environment for running TorchServe models. This approach reduces operational overhead and simplifies the deployment process.
Using the Ray Serve DLC allows teams to focus on model development and inference optimization rather than infrastructure management. This container provides a consistent and supported environment for deploying TorchServe workloads.