AI ML GenAI on Data Center-Class GPUs with Red Hat OpenShift

About this course

Unlock the power of enterprise-grade AI in your own data center—step-by-step, from bare-metal to production-ready inference. In this hands-on workshop, you’ll learn how to transform a single high-performance GPU server and a lightweight virtualization host into a fully featured Red Hat OpenShift cluster running OpenShift AI, the GPU Operator, and real LLM workloads (Mistral-7B with Ollama). We skip the theory slides and dive straight into keyboards and terminals—every YAML, every BIOS toggle, every troubleshooting trick captured on video.

What you’ll buildA three-node virtual control plane + one bare-metal GPU worker, deployed via the new Agent-based InstallerGPU Operator with MIG slicing, UUID persistence, and live metrics in GrafanaOpenShift AI (RHODS) with Jupyter and model-serving pipelinesA production-grade load balancer, DNS zone, and HTTPS ingress—no managed cloud needed

Hands-on every step: you’ll inspect firmware through iDRAC, patch BIOS settings, generate a custom Agent ISO, boot the cluster, join the GPU node, and push an LLM endpoint you can curl in under a minute. Along the way, we’ll upgrade OpenShift, monitor GPU temps, and rescue a “Node Not Ready” scenario—because real life happens.

Who should enroll

DevOps engineers, SREs, and ML practitioners who have access to a data center-grade GPU server and want a repeatable, enterprise-compatible install path. Basic Linux and kubectl skills are assumed; everything else is taught live.

By course end, you’ll have a battle-tested Git repository full of manifests, a private Agent ISO pipeline you can clone for new edge sites, and the confidence to stand up—or scale out—your own GPU-accelerated OpenShift AI platform. Join us and ship your first on-prem LLM workload today.

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