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...
In today’s digital world, businesses rely more and more on interconnected applications and services to operate effectively. This means integrating software and data across different environments is essential. However, achieving smooth connectivity can be tough because different application designs and the mix of on-premises and cloud systems often lead to inconsistencies. These issues require careful management to ensure everything runs well, risks are managed effectively, teams have the right skills, and security measures are strong. This article looks at three Red Hat technologies—Red Hat OpenShift Service Mesh and Red Hat Service Interconnect, as well as Submariner—in simple terms. It aims to help you decide which solution is best for your needs. OPENSHIFT Feature Service Mesh (Istio) Service Interconnect Submariner Purpose Manages service-to-service communication within a single cluster. Enables ...