Mastering GPU Resource Management in Kubernetes: Your 2025 Guide
The rise of artificial intelligence, machine learning (ML), and large-scale data processing has transformed GPUs from niche hardware into essential components of the modern data center. As companies increasingly rely on Kubernetes to orchestrate these demanding workloads, mastering GPU resource management has become a critical skill. Effectively scheduling, sharing, and monitoring these powerful accelerators is the key to unlocking performance, controlling costs, and ensuring stability.
Kubernetes, by itself, doesn’t natively understand how to manage GPUs. It requires specific configurations and tools to bridge the gap between container orchestration and specialized hardware. This guide provides a technical deep-dive into the strategies and tools you need to effectively manage GPU resources in your Kubernetes clusters today and into the future.
The Foundation: How Kubernetes Discovers and Manages GPUs
Out of the box, Kubernetes schedulers see CPUs and memory, but GPUs are invisible. To expose these specialized hardware resources to the cluster, we rely on the Kubernetes Device Plugin framework.
This framework allows hardware vendors to create plugins that run on each node, discovering specialized hardware (like GPUs) and advertising them to the kubelet. When a pod requests a GPU, the Kubernetes scheduler can then identify a node with an available GPU and assign the pod to it.
Key takeaway: The device plugin is the essential bridge that makes Kubernetes “GPU-aware.” Without it, your cluster has no way of knowing which nodes have GPUs or how to assign them to workloads.
The Ecosystem: Vendor-Specific Device Plugins
The three major GPU manufacturers provide their own official device plugins, which are the standard for enabling GPU access in Kubernetes.
- NVIDIA: The most common in AI/ML environments, the NVIDIA Device Plugin advertises
nvidia.com/gpuresources to the cluster. It requires NVIDIA drivers and the NVIDIA Container Toolkit to be installed on the host nodes, allowing containers to access the necessary drivers and CUDA libraries. - AMD: The AMD GPU Device Plugin exposes AMD GPUs to the cluster, enabling workloads that leverage the ROCm (Radeon Open Compute) platform for GPU computing.
- Intel: With its growing line of integrated and discrete GPUs, Intel provides the Intel Device Plugins for Kubernetes, which expose GPU, VPU, and other hardware resources to the container orchestrator.
Advanced Allocation: GPU Sharing and Partitioning
Assigning an entire high-end GPU to a single container is often wasteful, especially for development tasks, model inference, or workloads that don’t require 100% of the GPU’s power. This is where advanced GPU sharing techniques become invaluable for maximizing utilization and return on investment.
1. Time-Slicing
Time-slicing allows multiple containers to share a single GPU. The GPU’s processing time is divided into slices, and each container gets a turn to execute its tasks.
- How it works: This is typically enabled through the NVIDIA device plugin configuration. You can define a replication factor, effectively advertising one physical GPU as multiple
nvidia.com/gpuresources. - Best for: Workloads that are not performance-sensitive and have intermittent GPU needs, such as development environments or light model inference.
- Limitation: There is no memory isolation or performance guarantee. A “noisy neighbor” pod can consume an excessive amount of GPU memory, impacting other pods sharing the same device.
2. NVIDIA Multi-Instance GPU (MIG)
For more robust and predictable sharing, NVIDIA Multi-Instance GPU (MIG) is the gold standard. Available on Ampere architecture GPUs (like the A100) and newer, MIG partitions a single GPU into multiple, fully isolated GPU instances at the hardware level.
- How it works: Each MIG instance has its own dedicated compute engines, memory, and memory controllers. From Kubernetes’ perspective, each MIG instance appears as a distinct, assignable GPU resource.
- Best for: Production environments where predictable performance and strong isolation are required. It’s ideal for multi-tenant clusters, running different-sized inference models simultaneously, or ensuring quality of service (QoS) for critical applications.
- Key Advantage: MIG provides true hardware-level isolation, preventing the “noisy neighbor” problem entirely and delivering guaranteed performance.
Monitoring and Observability: Gaining Insight into GPU Usage
You can’t optimize what you can’t measure. Effective monitoring is crucial for understanding GPU utilization, identifying performance bottlenecks, and making informed decisions about resource allocation.
The standard for observability in the Kubernetes ecosystem is the combination of Prometheus and Grafana. To get GPU-specific metrics, you need an exporter that can read data from the GPU hardware.
For NVIDIA GPUs, the DCGM Exporter (Data Center GPU Manager) is the primary tool. It integrates seamlessly with Prometheus to expose critical metrics, including:
- GPU Utilization (
DCGM_FI_DEV_GPU_UTIL): The percentage of time the GPU cores were active. - GPU Memory Usage (
DCGM_FI_DEV_FB_USED): How much of the GPU’s frame buffer memory is in use. - Power Draw (
DCGM_FI_DEV_POWER_USAGE): The power consumption of the GPU in watts. - Temperature (
DCGM_FI_DEV_GPU_TEMP): The operating temperature of the GPU.
By feeding these metrics into Grafana, you can create powerful dashboards to visualize GPU health and usage across your entire cluster in real-time.
Actionable Best Practices for GPU Management
Use Node Labels and Taints: Apply labels to your GPU-equipped nodes (e.g.,
gpu-type=nvidia-a100) to schedule specific workloads to specific hardware. Use taints to prevent non-GPU pods from being scheduled on these expensive nodes.Right-Size Your GPU Requests: Don’t request a full GPU if you don’t need it. Leverage MIG or time-slicing to match the resource request to the actual workload needs. This is the single biggest factor in improving cluster utilization and reducing costs.
Automate with Operators: The NVIDIA GPU Operator automates the entire lifecycle management of NVIDIA software on Kubernetes. It handles the installation of drivers, the container toolkit, the device plugin, and monitoring components, significantly simplifying node setup and upgrades.
Implement Security Best Practices: GPU-enabled containers often require more privileges than standard containers. Follow the principle of least privilege. Use Pod Security Standards and security contexts to restrict container capabilities and prevent potential breakouts. Keep drivers and device plugins updated to patch known vulnerabilities.
By moving beyond basic allocation and embracing advanced techniques like MIG, robust monitoring, and automation, you can build a highly efficient, scalable, and cost-effective platform for running GPU-accelerated applications on Kubernetes.
Source: https://collabnix.com/kubernetes-gpu-resource-management-best-practices-complete-technical-guide-for-2025/
