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 Pipeline

If you are setting this up, here is the high-level deployment pipeline:

  1. Update the Host: Install the proprietary NVIDIA GPU drivers directly on the bare-metal node.

  2. Install Toolkit: Deploy the NVIDIA Container Toolkit to bridge the GPU with container runtimes.

  3. Configure Runtime: Modify containerd configurations to recognize the nvidia runtime class.

  4. Deploy Plugin: Apply the NVIDIA Device Plugin DaemonSet to your K8s cluster.

  5. Verify: Deploy a test Pod requesting nvidia.com/gpu resources.

Step 1: Install NVIDIA Drivers on the Host Node

Kubernetes cannot interact with the GPU hardware without the host machine first having the correct drivers installed. First, update your package lists and install the build tools:

Bash
sudo apt-get update
sudo apt-get install -y build-essential linux-headers-$(uname -r)
sudo apt-get install -y nvidia-driver-535

After a reboot, you can verify the installation by checking the GPU status using the nvidia-smi command.

Ready to configure containerd and deploy the K8s Device Plugin?

Setting up the container runtime correctly is crucial to avoid kernel panics and CrashLoopBackOff errors.

Click here to read the full Step-by-Step Guide on our website

Configuring containerd for GPU Support.

  • Deploying the NVIDIA Device Plugin DaemonSet YAML.

  • Testing GPU Allocation with a Pod.

  • Troubleshooting common K8s scheduling errors.

For enterprise-grade reliability and uncompromised raw computing power, consider deploying your next Kubernetes cluster on GPUYard.

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