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Run GenAI inference across environments with Amazon EKS Hybrid Nodes
- Amazon EKS Hybrid Nodes allow running generative AI inference workloads across cloud and on-premises environments, offering consistent management and reduced operational complexity.
- EKS Hybrid Nodes support various AI/ML use cases like real-time edge inference, on-premises data residency, and running inference workloads closer to source data.
- Proof of concept involved deploying a single EKS cluster for AI inference on-premises with EKS Hybrid Nodes and in the AWS Cloud with Amazon EKS Auto Mode.
- Prerequisites include setting up Amazon VPC, AWS Site-to-Site VPN connection, on-premises nodes with NVIDIA drivers, and tools like Helm, kubectl, eksctl, and AWS CLI.
- Steps include creating an EKS cluster with EKS Hybrid Nodes and Auto Mode enabled, preparing hybrid nodes, installing NVIDIA device plugin for Kubernetes, and deploying NVIDIA NIM for inference.
- Validating nodes connectivity, deploying GPU-specific solutions for Auto Mode, such as g6 instance family, and testing NIM for inference were part of the deployment process.
- Cleanup steps were outlined to avoid long-term costs, and the post emphasized how EKS Hybrid Nodes simplify AI workload management by unifying Kubernetes footprint.
- For more detailed guidance, users are directed to the EKS Hybrid Nodes user guide and re:Invent 2024 session (KUB205) explaining hybrid nodes functionality, features, and best practices.
- Explore the Data on EKS project for further information on running AI/ML workloads on Amazon EKS.
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