Configurations for Intel® Processor Graphics (GPU) with OpenVINO™

To use the OpenVINO™ GPU plugin and offload inference to Intel® Processor Graphics (GPU), Intel® Graphics Driver must be properly configured on your system.

If Intel® Graphics Driver is already installed and you would like to keep it, you can skip the installation steps below.


To install the latest available Intel® Graphics Compute Runtime for oneAPI Level Zero and OpenCL™ Driver for your operating system, see its installation guide on GitHub.


If you are using RedHat 8, you can install the OpenCL library as a prerequisite by using the following command:

You may consider installing one of the earlier versions of the driver, based on your particular setup needs.

For instructions and recommendations on the installation of a specific GPU driver release, as well as the list of supported hardware platforms, refer to the Intel® Graphics Compute Runtime for oneAPI Level Zero and OpenCL™ Driver GitHub home page.

For instructions specific to discrete graphics platforms, refer to the dgpu guide, including installation guides for Intel® Arc™ A-Series Graphics, Intel® Data Center GPU Flex Series, Intel® Data Center GPU MAX Series, Intel® processor graphics Gen12, and Intel® Iris Xe MAX codename DG1.


To install the Intel Graphics Driver for Windows on your system, follow the driver installation guide.

To check if you have this driver installed:

  1. Type device manager in your Search Windows box and press Enter. The Device Manager opens.

  2. Click the drop-down arrow to view the Display adapters. You can see the adapter that is installed in your computer:

  3. Right-click the adapter name and select Properties.

  4. Click the Driver tab to see the driver version.


You are done updating your device driver and ready to use your GPU.

Additional info

For your reference, the following versions of Intel® Graphics Driver were used in the OpenVINO internal validation:

Operation System

Driver version

Ubuntu 22.04


Ubuntu 20.04


Ubuntu 18.04


CentOS 7




What’s Next?

You can try out the toolkit with:

Python Quick Start Example to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser.

C++ Quick Start Example for step-by-step instructions on building and running a basic image classification C++ application.

Visit the Samples page for other C++ example applications to get you started with OpenVINO, such as: