# Overview

In this self-paced exercise, you will first configure a GPU enabled MKS cluster. You will then follow that up by deploying a workload that will use the GPU and review the integrated dashboards for GPUs.

* * *

## What Will You Do by Part

| Part | What will you do? |
| --- | --- |
| 1 | [Setup](https://docs.rafay.co/learn/quickstart/mks/gpu/setup/) and Configuration |
| 2 | Create a Cluster [Blueprint](https://docs.rafay.co/learn/quickstart/mks/gpu/blueprint/) with the Nvidia GPU Operator |
| 3 | Deploy a Machine Learning [Workload](https://docs.rafay.co/learn/quickstart/mks/gpu/workload/) to the GPU enabled MKS Cluster |

* * *

## Assumptions

- You have already [provisioned](https://docs.rafay.co/learn/quickstart/mks/clusterlifecycle/overview/) a Rafay MKS Cluster (Upstream Kubernetes cluster) with at least one node comprising a compatible Nvidia GPU.

- You have a Git client on your laptop.