Ibrar Aziz -Technology Enthusiast

 AI workloads demand significant computational resources, especially for training large models or performing real-time inference. Modern GPUs like NVIDIA's H100 and H200 are designed to handle these demands effectively, but maximizing their utilization requires careful management. This article explores strategies for managing AI workloads in Kubernetes and OpenShift with GPUs, focusing on features like MIG (Multi-Instance GPU), time slicing, MPS (Multi-Process Service), and vGPU (Virtual GPU). Practical examples are included to make these concepts approachable and actionable. 1. Why GPUs for AI Workloads? GPUs are ideal for AI workloads due to their massive parallelism and ability to perform complex computations faster than CPUs. However, these resources are expensive, so efficient utilization is crucial. Modern GPUs like NVIDIA H100/H200 come with features like: MIG (Multi-Instance GPU): Partitioning a single GPU into smaller instances. Time slicing: Efficiently sharing GPU res...