Rancher for AI and ML Workloads: GPU KUBERNETES AT SCALE: Deploy AI Infrastructure with Multi-Cluster GPU Management. Virtual Clusters, Resource Optim (Paperback) (ISBN-13: 9798275212006)

Build a reliable multi cluster GPU platform for real AI workloads with Rancher RKE2 and K3s.

AI and ML teams now expect shared GPU infrastructure that is fast, secure, and cost aware, not a pile of hand built servers that only a few people understand. If you run Kubernetes with GPUs, you need more than marketing diagrams. You need concrete patterns for Rancher managed clusters, GPU Operator, virtual clusters, observability, and governance that work in production.

Rancher for AI and ML Workloads: GPU Kubernetes at Scale shows platform engineers, SREs, and senior ML practitioners how to design, build, and operate GPU fleets using Rancher, RKE2, K3s, and the surrounding SUSE and NVIDIA ecosystem. From the first GPU node to a multi cluster AI platform, it gives you clear, realistic guidance instead of vague promises.

• Understand when GPU Kubernetes is the right choice and when simpler alternatives are better for AI and ML teams.
• Design RKE2 based GPU clusters, including hardware, networking, storage, node labels, and taints suitable for training and inference.
• Install and operate the NVIDIA GPU Operator on RKE2, K3s, and K3d to manage drivers, container runtimes, and device plugins safely.
• Use virtual clusters with vCluster and SUSE Virtual Clusters to build multi tenant GPU platforms with strong isolation and clear tenancy models.
• Run AI workloads with correct scheduling and scaling using GPU requests and limits, Kueue batch scheduling, HPAs, cluster autoscalers, and Karpenter.
• Collect and visualize GPU metrics with DCGM exporter, Prometheus, and OpenTelemetry, including dashboards for GPU utilization and vLLM performance.
• Control cost and efficiency with MIG, time slicing, NVIDIA KAI, Federator ai, and Avesha Elastic GPU Service across Rancher managed clusters.
• Secure AI workloads using hardened RKE2 control planes, NeuVector runtime and network controls, and SUSE AI governance for data boundaries and audit trails.
• Apply proven reference architectures such as FlexPod based Rancher GPU clusters, virtual cluster based GPU clouds inspired by CoreWeave, and SUSE AI blueprints.
• Adopt a GitOps operating model with Fleet, structured repositories, upgrade strategies for Kubernetes and GPU Operator, and a migration roadmap from a single cluster to a managed multi cluster AI platform.

This is a code heavy, practical guide that uses real YAML manifests, Helm values, and kubectl workflows so you can adapt working patterns directly into your own clusters and pipelines.

Grab your copy today and build a GPU platform your AI teams can trust.