How to Configure Bare-Metal Kubernetes for GPU Orchestration (Zero Virtualization Overhead)
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. If you want to automatically schedule, allocate, and manage GPU resources across your containerized workloads, you need to integrate the NVIDIA Container Toolkit with the Kubernetes Device Plugin. Here is what you need to get started. Prerequisites Before diving into the configuration, 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. Kubernetes: A running K8s cluster (v1.25+) initialized via kubeadm, k3s, or similar. Container Runtime: containerd installed and running. Quick Summary / TL;DR of the Pipelin...