Optimizing GPU resources with NVIDIA Multi-Instance GPU (MIG) in Red Hat OpenShift AI and Kubernetes

The articles in this series represent a comprehensive guide of key challenges in AI infrastructure and more importantly, how to optimize GPU resources for AI and ML workloads in containerized environments.

This article series describes key challenges in AI infrastructure: optimizing resource usage (particularly GPUs), reducing costs, and enabling scalability. Also, this series introduces NVIDIA Multi-Instance GPU (MIG) technology, which enables more efficient utilization of GPUs resources for AI and ML workloads in Red Hat OpenShift AI and Kubernetes.

Articles in this series

• Optimizing GPU resources with NVIDIA MIG in Red Hat OpenShift AI

  This article introduces MIG and OpenShift AI, the challenges of GPU utilization and the role of MIG technology, the power of MIG in OpenShift AI, and why MIG makes sense.

• Implementing NVIDIA MIG in Red Hat OpenShift to optimize GPU resources in containerized environments

  This comprehensive guide explores how to harness NVIDIA's MIG technology within Red Hat OpenShift to achieve GPU optimization and utilization. It introduces GPU optimization techniques and how to Implement MIG in Red Hat OpenShift, Optimization strategies for MIGs in OpenShift, Workload-specific optimizations for AI/ML pipelines and multi-tenant environments, Performance monitoring and tuning, and Advanced MIG configurations for heterogeneous environments and integration with OpenShift Virtualization.

• Boost GPU efficiency in Kubernetes with NVIDIA Multi-Instance GPU

  This article explains the foundation of the GPU MIG partition, the efficiency challenges of different partition approaches, and how to improve GPU efficiency in Kubernetes with NVIDIA Multi-Instance GPU with tools like MIG-Adapter.