The Ultimate Guide to Kubernetes GPU Management for AI and Machine Learning
As artificial intelligence and machine learning models become increasingly complex, the demand for computational power has skyrocketed. Standard CPUs are no longer sufficient for the heavy lifting required for deep learning model training and large-scale inference. This is where Graphics Processing Units (GPUs) come in, offering the massive parallel processing capabilities needed to accelerate these demanding tasks.
For organizations looking to scale their AI/ML operations, combining the power of GPUs with the orchestration capabilities of Kubernetes is a game-changer. This guide provides a comprehensive overview of how to effectively manage and utilize GPUs within a Kubernetes environment, ensuring your AI workloads are both powerful and efficient.
Why Combine GPUs with Kubernetes?
Integrating GPUs into your Kubernetes cluster unlocks several critical advantages for MLOps and data science teams. This combination is more than just a trend; it’s a strategic move towards building a robust, scalable, and cost-effective AI infrastructure.
- Unmatched Scalability: Kubernetes excels at managing containerized applications across a fleet of machines. When you add GPU-enabled nodes, you can dynamically scale your AI training and inference workloads up or down based on demand, ensuring resources are always available when needed.
- Enhanced Resource Efficiency: GPUs are expensive resources that often sit idle. Kubernetes helps solve this problem by scheduling workloads to run on nodes with available GPUs, maximizing utilization and improving your return on investment. You can run different experiments or models on the same cluster, sharing resources without conflict.
- Portability and Consistency: By containerizing your AI applications, you create a consistent environment that runs the same way on a developer’s laptop as it does in a production cluster. This eliminates dependency headaches and streamlines the path from development to deployment.
- Resilience and Fault Tolerance: Kubernetes automatically manages the health of your workloads. If a pod or even an entire node running a GPU task fails, Kubernetes can automatically reschedule it on a healthy node, ensuring your long-running training jobs are not lost.
The Core Components: Enabling GPUs in Your Cluster
Making GPUs available to your containerized workloads in Kubernetes isn’t automatic. It requires a few key components working together to bridge the gap between the physical hardware and the Kubernetes scheduler.
NVIDIA GPU Drivers: The first step is to install the appropriate NVIDIA drivers directly on the host operating system of your worker nodes. These drivers are essential for the kernel to recognize and interact with the physical GPU hardware. This must be done on the node itself, not within a container.
NVIDIA Container Toolkit: To allow containers to access the GPU, you need a container runtime that is GPU-aware. The NVIDIA Container Toolkit integrates with container runtimes like Docker and containerd, allowing it to automatically configure containers to use NVIDIA GPUs. It essentially exposes the necessary device files and libraries from the host node into the container at runtime.
The Kubernetes Device Plugin: This is the critical piece that makes Kubernetes “GPU-aware.” The NVIDIA Device Plugin for Kubernetes is a DaemonSet that runs on each node in your cluster. Its primary jobs are to:
- Discover the number and type of GPUs available on the node.
- Report these GPU resources to the Kubernetes API server.
- Monitor the health of the GPUs.
Once the device plugin is running, Kubernetes can see nvidia.com/gpu as a schedulable resource, just like CPU and memory.
Scheduling and Monitoring GPU Workloads
With the foundation in place, you can start running GPU-accelerated applications. This is done by specifying GPU resource requirements in your pod’s YAML configuration.
To request a single GPU for your pod, you simply add it to the resource limits:
apiVersion: v1
kind: Pod
metadata:
name: gpu-pod-example
spec:
containers:
- name: cuda-container
image: nvidia/cuda:12.1.1-base-ubuntu22.04
resources:
limits:
nvidia.com/gpu: 1 # Requesting 1 GPU
The Kubernetes scheduler will then find a node with an available GPU and place the pod there.
Effective monitoring is crucial for optimization and troubleshooting. To gain visibility into GPU performance, you should leverage tools like the NVIDIA Data Center GPU Manager (DCGM). By deploying the dcgm-exporter in your cluster, you can scrape detailed GPU metrics—such as utilization, memory usage, and temperature—and visualize them in monitoring dashboards like Prometheus and Grafana.
Advanced GPU Management: Sharing and Virtualization
For many workloads, especially inference or development tasks, dedicating an entire powerful GPU to a single container is wasteful. Modern techniques allow for more granular control and sharing.
- Multi-Instance GPU (MIG): Available on NVIDIA Ampere architecture and newer GPUs (like the A100), MIG allows a single GPU to be partitioned into multiple, fully isolated GPU instances. Each instance has its own dedicated compute and memory resources, appearing to Kubernetes as a separate, smaller GPU. This is ideal for running multiple, smaller models in parallel with guaranteed performance.
- GPU Time-Slicing: For GPUs that do not support MIG, time-slicing is an alternative. This technique allows multiple containers to share a single GPU by allocating slices of execution time to each one. While it doesn’t provide the same performance isolation as MIG, it is highly effective for increasing utilization for workloads that don’t need the GPU 100% of the time.
Best Practices for Security and Optimization
As you scale your GPU-enabled Kubernetes cluster, follow these best practices to maintain a secure, stable, and cost-effective environment.
Isolate GPU Workloads: Use Kubernetes namespaces and node taints/tolerations to create logical separation. You can taint your GPU-enabled nodes so that only pods specifically requesting GPUs (and having the corresponding toleration) can be scheduled on them. This prevents general-purpose workloads from consuming valuable GPU node resources.
Right-Size Your Requests: Analyze your workload’s actual GPU needs. If using MIG, request a specific instance size rather than a whole GPU. Over-provisioning leads to wasted resources and increased costs.
Keep Components Updated: Regularly update your NVIDIA drivers, the container toolkit, and the device plugin. These updates often contain critical security patches, performance improvements, and bug fixes that are essential for a stable production environment.
Implement Robust Alerting: Set up alerts based on your monitoring data. You should be notified of critical events like high GPU temperatures, low utilization (indicating waste), or pods that are stuck in a pending state because no GPU resources are available.
Source: https://collabnix.com/kubernetes-and-gpu-the-complete-2025-guide-to-ai-ml-acceleration/
