To achieve maximum performance for AI inference, machine learning training, and high-performance computing (HPC), deploying workloads on bare-metal servers is the industry standard. Virtualized environments introduce overhead; bare-metal hardware allows direct access to the PCIe bus, ensuring your NVIDIA GPUs operate at 100% efficiency . This tutorial explains how to configure a bare-metal Kubernetes (K8s) cluster for GPU orchestration. By integrating the NVIDIA Container Toolkit and the Kubernetes Device Plugin, you can automatically schedule, allocate, and manage GPU resources across your containerized workloads. Prerequisites Before beginning, ensure your environment meets the following requirements: Operating System: Ubuntu 22.04 LTS (Jammy Jellyfish). Hardware: A bare-metal server with at least one physical NVIDIA GPU attached. Access: Root or sudo privileges. Kubernetes: A running K8s cluster (v1.25+) initialized via kubeadm , k3s , or similar, with the kubectl CLI tool configured.…