## [Using GPUs in Kubernetes](https://docs.rafay.co/blog/2024/10/19/using-gpus-in-kubernetes/)

Unlike CPU and Memory, GPUs are **not natively supported** in Kubernetes. Kubernetes manages CPU and memory natively. This means it can automatically schedule containers based on these resources, allocates them to Pods, and handles resource isolation and over-subscription.

GPUs are considered specialized hardware and require the use of [**device plugins**](https://kubernetes.io/docs/concepts/extend-kubernetes/compute-storage-net/device-plugins/) to support GPUs in Kubernetes. Device Plugins help make Kubernetes **GPU-aware** allowing it to Discover, Allocate and Schedule GPUs for containerized workloads. Without a device plugin, Kubernetes is unaware of the GPUs available on the nodes and cannot assign them to Pods. In this blog, we will discuss why GPUs are not natively supported and understand how device plugins help address this gap.