Simplifying Advanced AI Model Serving on Kubernetes Using Helm... Ajay Vohra & Tianlu Caron Zhang скачать в хорошем качестве

Simplifying Advanced AI Model Serving on Kubernetes Using Helm... Ajay Vohra & Tianlu Caron Zhang

Don't miss out! Join us at our next Flagship Conference: KubeCon + CloudNativeCon events in Amsterdam, The Netherlands (23-26 March, 2026). Connect with our current graduated, incubating, and sandbox projects as the community gathers to further the education and advancement of cloud native computing. Learn more at https://kubecon.io Simplifying Advanced AI Model Serving on Kubernetes Using Helm Charts - Ajay Vohra, Amazon & Tianlu Caron Zhang, Apple The AI model serving landscape on Kubernetes presents practitioners with an overwhelming array of technology choices: From inference servers like Ray Serve and Triton Inference Server, inference engines like vLLM, and orchestration platforms like Ray Cluster and KServe. While this diversity drives innovation, it also creates complexity. Teams often prematurely standardize on limited technology stacks to manage this complexity. This talk introduces an innovative Helm-based approach that abstracts the complexity of AI model serving while preserving the flexibility to leverage the best tools for each use case. Our solution is accelerator agnostic, and provides a consistent YAML interface for deploying and experimenting with various serving technologies. We'll demonstrate this approach through two concrete examples of multi-node, multi-accelerator model serving with auto scaling: 1/ Ray Serve + vLLM + Ray Cluster, and 2/ LeaderWorkerSet + Triton Inference Server + vLLM + Ray Cluster + HPA.