Build ultra-low latency multimodal generative AI applications using sticky session routing in Amazon SageMaker

This article introduces a new feature in Amazon SageMaker called sticky session routing, which helps improve the performance and user experience of generative AI applications by leveraging previously processed information.

Specifically, the article covers:

• How sticky session routing works in SageMaker, allowing all requests from the same session to be routed to the same instance for reusing cached data
• How to build and deploy a multimodal model (LLaVA) using TorchServe and sticky session routing on SageMaker
• The step-by-step process of creating a SageMaker endpoint, running inference, and managing sessions
• A hands-on example with code and a notebook for deploying the LLaVA model
• Conclusion highlighting the benefits of this solution for low-latency multimodal AI applications