OpenShift AI Platform Guide

Platform Engineering, GPUs, and Air-Gapped Clusters with OpenShift

De: Luca Berton

Narrado por: Virtual Voice

OpenShift AI Platform Guide is a practical handbook for platform engineers who need to turn OpenShift into a real internal AI platform, not “just a Kubernetes cluster.”

Starting from the CNCF platform engineering whitepaper, the book shows how to apply those ideas on OpenShift: treating the platform as a product, reducing cognitive load for app teams, and building opinionated “golden paths” instead of one-off snowflakes.

From there, you’ll walk through end-to-end, production-grade scenarios:

• Installing OpenShift 4.20 in fully air-gapped environments with a local Quay registry

• Configuring cluster-wide proxies, NFS storage, and disconnected OperatorHub catalogs

• Deploying and managing key operators like Node Feature Discovery and the NVIDIA GPU Operator

• Enabling InfiniBand and RDMA networking with SR-IOV and the NVIDIA Network Operator

• Integrating observability with DCGM, Prometheus, and Grafana for GPU-aware monitoring

• Using GitOps (OpenShift GitOps / Argo CD + GitLab) for declarative, auditable platform config

• Running LLM performance benchmarks as code with Hugging Face’s Inference-Benchmarker and visualizing results with a Gradio dashboard

The guide is written in a “do this, then this” style, with YAML examples, command snippets, and explanations of why each piece matters for a modern AI platform.

If you are a platform engineer, SRE, or infrastructure-minded ML practitioner responsible for OpenShift-based GPU clusters—especially in regulated or disconnected environments—this book gives you a concrete, repeatable blueprint.