Maximizing GPU utilization with NVIDIA’s Multi-Instance GPU (MIG) on Amazon EKS: Running more pods per GPU for enhanced performance
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This article discusses using NVIDIA's Multi-Instance GPU (MIG) on Amazon Elastic Kubernetes Service (Amazon EKS) to maximize GPU utilization by running more pods per GPU for enhanced performance, especially for GPU-intensive workloads like AI and high-performance computing (HPC).
Specifically, the article covers:
- Introduction to NVIDIA's Multi-Instance GPU (MIG) and its benefits like resource efficiency, predictable performance, flexibility, cost-efficiency, and enhanced security
- Prerequisites and solution overview for setting up MIG on Amazon EKS using the NVIDIA GPU Operator
- Step-by-step walkthrough for creating an EKS cluster, installing the GPU Operator, setting up MIG partitions using single and mixed strategies, deploying applications to utilize MIG devices, and verifying GPU allocations
- Conclusion highlighting the advantages of using MIG on Amazon EKS for optimized GPU utilization and better performance for machine learning and other GPU-intensive workloads
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