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AI ML GenAI on Data Center-Class GPUs with Red Hat OpenShift
Rating: 4.3 out of 5(11 ratings)
659 students

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

OpenShift & OpenShift AI on High-Performance GPUs: From Bare-Metal to Production in One Day
Created byLuca Berton
Last updated 8/2025
English

What you'll learn

  • Stand up a bare-metal data center-grade GPU node, validate firmware & BIOS, and register it in a fresh OpenShift cluster
  • Install and tune the GPU Operator with Multi-Instance GPU (MIG) profiles for maximum utilisation
  • Deploy Red Hat OpenShift AI (RHOAI) and run a real Mistral LLM workload with Ollama
  • Monitor, troubleshoot, upgrade, and scale the platform in production

Course content

5 sections11 lectures1h 28m total length
  • Course overview6:15

    Ready to go from bare-metal hardware to a GPU-powered AI platform in one busy afternoon? In this fast-paced walkthrough you’ll watch us transform a single NVIDIA H100 server—and a small virtualisation host—into a fully fledged Red Hat OpenShift 4.18 cluster running OpenShift AI. We start by inspecting firmware in iDRAC, flip the must-have BIOS toggles, and generate a custom Agent ISO that bootstraps a three-node control plane with zero external provisioning network.

    Once the masters are healthy we attach a bare-metal H100 worker, install the NVIDIA GPU Operator, and slice the card into MIG partitions for multi-tenant workloads. You’ll see Ollama spin up with Mistral-7B, curl an inference endpoint, and then watch live GPU metrics flow into Grafana dashboards. Finally, we roll through an OpenShift upgrade—proving the stack can survive real-world maintenance without downtime.

    Whether you’re a machine-learning engineer shipping models, a DevOps pro automating infra, or a curious Python developer taking your first steps into AI operations, this video will show you every YAML, command, and troubleshooting trick you need to replicate the build in your own lab. Grab a coffee, open your terminal, and let’s launch!

    Sources

    Ask ChatGPT

  • Infra architecture3:51

    Ever wondered how all the moving parts of an on-prem AI platform fit together? In this concise visual tour we break down the four-component lab architecture that powers our entire course — from first oc command to blazing-fast GPU inference.

    • Jump Host See how a lean RHEL VM becomes the control tower for every OpenShift and iDRAC operation.

    • NVIDIA H100 Server Peek inside a Hopper GPU with 80 GB of HBM3 and learn why MIG slicing is the secret to multi-tenant performance.

    • OpenShift Control Plane Watch three high-availability masters (one acting as the rendezvous host) bootstrap themselves from a single Agent ISO.

    • Network Fabric Understand the API VIP, Ingress VIP, and Layer-4 load balancer that keep north-south and east-west traffic humming—even when pods fail.

    We’ll trace traffic flows, highlight fail-over paths, and show where the local mirror registry plugs into an air-gapped deployment. By the end, you’ll see exactly how these pieces click together to form a scalable, production-ready foundation for GPU-accelerated workloads. If you’re prepping an AI cluster—or just curious how enterprise Kubernetes is wired—this video gives you the blueprint in under ten minutes.

    Press play, grab your favourite whiteboard marker, and map your own path to AI-ready infrastructure!

Requirements

  • One server with a high-performance, data center-class GPU—physical or virtualised
  • A workstation that can SSH into the node and run the "oc" CLI
  • (Optional) A Red Hat account to pull mirrored images

Description

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 build

  • A three-node virtual control plane + one bare-metal GPU worker, deployed via the new Agent-based Installer

  • GPU Operator with MIG slicing, UUID persistence, and live metrics in Grafana

  • OpenShift AI (RHODS) with Jupyter and model-serving pipelines

  • A 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.

Who this course is for:

  • Machine Learning Engineers
  • DevOps Engineers
  • Site Reliability Engineers (SREs)
  • Python Developers Exploring Infrastructure
  • First Steppers into AI Operations