## [Custom GPU Resource Classes in Kubernetes](https://docs.rafay.co/blog/2025/07/11/custom-gpu-resource-classes-in-kubernetes/)

In the modern era of containerized machine learning and AI infrastructure, GPUs are a critical and expensive asset. Kubernetes makes scheduling and isolation easier—but managing GPU utilization efficiently requires more than just assigning something like

```
nvidia.com/gpu: 1
```

In this blog post, we will explore what custom GPU resource classes are, why they matter, and when to use them for maximum impact. Custom GPU resource classes are a powerful technique for fine-grained GPU management in multi-tenant, cost-sensitive, and performance-critical environments.

If you are new to GPU sharing approaches, we recommend reading the following introductory blogs: [Demystifying Fractional GPUs in Kubernetes](https://docs.rafay.co/blog/2025/07/08/demystifying-fractional-gpus-in-kubernetes-mig-time-slicing-and-custom-schedulers/) and [Choosing the Right Fractional GPU Strategy](https://docs.rafay.co/blog/2025/07/10/choosing-the-right-fractional-gpu-strategy-for-cloud-providers/).