PyTorch* Prerequisites for Intel® GPUs

Refer to the Client GPU installation instructions for latest Ubuntu for installing the Intel GPU drivers.

Hardware verified with Ubuntu 26.04 & 25.10 & 24.04

• Intel® Arc™ A-Series Graphics (CodeName: Alchemist)
• Intel® Arc™ B-Series Graphics (CodeName: Battlemage)
• Intel® Core™ Ultra Processors with Intel® Arc™ Graphics (CodeName: Meteor Lake-H)
• Intel® Core™ Ultra Mobile Processors (Series 2) with Intel® Arc™ Graphics (CodeName: Lunar Lake)
• Intel® Core™ Ultra Processors (Series 2) with Intel® Arc™ Graphics (CodeName: Arrow Lake-H)

Hardware verified with Ubuntu 26.04 & 25.10

• Intel® Core™ Ultra Mobile Processors (Series 3) with Intel® Arc™ Graphics (CodeName: Panther Lake)

Optionally, follow these instructions to verify expected Intel GPU hardware is working.

Follow the instructions in the Intel® & Iris® Xe Graphics - Windows documentation to download and run the installer to update your WHQL Certified graphics driver to version 32.0.101.8801(Latest) or higher. Please include LevelZeroSDK in the installation package for `torch.compile` usage on Windows.

Hardware verified with Windows 11

• Intel® Arc™ A-Series Graphics (CodeName: Alchemist)
• Intel® Arc™ B-Series Graphics (CodeName: Battlemage)
• Intel® Core™ Ultra Processors with Intel® Arc™ Graphics (CodeName: Meteor Lake-H)
• Intel® Core™ Ultra Mobile Processors (Series 2) with Intel® Arc™ Graphics (CodeName: Lunar Lake)
• Intel® Core™ Ultra Processors (Series 2) with Intel® Arc™ Graphics (CodeName: Arrow Lake-H)
• Intel® Core™ Ultra Mobile Processors (Series 3) with Intel® Arc™ Graphics (CodeName: Panther Lake)

Windows Subsystem for Linux 2 runs a Linux distribution as a guest on a Windows host.

Hardware verified with WSL2 (Ubuntu 26.04 & 24.04)

• Intel® Arc™ A-Series Graphics (CodeName: Alchemist)
• Intel® Arc™ B-Series Graphics (CodeName: Battlemage)
• Intel® Core™ Ultra Processors with Intel® Arc™ Graphics (CodeName: Meteor Lake-H)
• Intel® Core™ Ultra Mobile Processors (Series 2) with Intel® Arc™ Graphics (CodeName: Lunar Lake)
• Intel® Core™ Ultra Processors (Series 2) with Intel® Arc™ Graphics (CodeName: Arrow Lake-H)
• Intel® Core™ Ultra Mobile Processors (Series 3) with Intel® Arc™ Graphics (CodeName: Panther Lake)

The driver stack is split: the Windows host driver owns the GPU and exposes it to the guest, and the guest only needs the Intel user-mode compute runtime.

There is no need for Linux kernel-mode GPU driver inside WSL2 guest. Follow the steps below, to install the driver stack for WSL2.

1. On Windows host: Install the Windows GPU driver, as described in Install for Windows.
2. On Windows host: List the Ubuntu distributions available for installation, then install a distribution from the validated list:

   
   wsl --list --online
   wsl --install -d Ubuntu-26.04

3. On WSL2 guest: Add the Intel client GPU package repository, following the Client GPU installation instructions for latest Ubuntu. Follow only the repository setup steps.
4. On WSL2 guest: Install the user-mode compute runtime packages in the guest:

   
   sudo apt update
   sudo apt install -y libze1 intel-level-zero-gpu intel-opencl-icd clinfo libze-dev intel-ocloc

5. On WSL2 guest: Verify that the guest sees the GPU:

   The /dev/dxg must exist, and clinfo -l must list an Intel platform with at least one device.

   
   ls -l /dev/dxg
   clinfo -l

   If /dev/dxg is missing, the host is not exposing the GPU. Update the Windows host driver and restart WSL2:

   
   wsl --shutdown
   wsl -d Ubuntu-26.04

   If /dev/dxg exists but no device is listed, the runtime packages from step 4 are missing or incomplete.

For RPM-based distributions such as Red Hat Enterprise Linux Server, YUM is the usual choice. You’ll need to configure YUM to install software packages that aren’t available in the default repositories. These instructions show how to add access to the appropriate Intel repository, along with the public key used to authenticate the downloaded packages.​​​​​​

1. Create an Intel YUM repository information file and move it to the YUM configuration directory:

   
   tee > /tmp/oneAPI.repo << EOF
   
   [oneAPI]
   
   name=Intel® oneAPI repository
   
   baseurl=https://yum.repos.intel.com/oneapi
   
   enabled=1
   
   gpgcheck=1
   
   repo_gpgcheck=1
   
   gpgkey=https://yum.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
   
   EOF
   
   sudo mv /tmp/oneAPI.repo /etc/yum.repos.d
   

    

2. Use YUM/Zypper to install Intel® Deep Learning Essentials:

   
   sudo yum install intel-deep-learning-essentials-2026.1
   or
   sudo zypper install intel-deep-learning-essentials-2026.1

    

For Debian-based Linux distributions such as Ubuntu Server, APT is the usual choice. You’ll need to configure APT to install software packages that aren’t available in the default repositories. These instructions show how to add access to the appropriate Intel repository, along with the public key used to authenticate the downloaded packages.

1. Make sure the necessary tools to add repository access are available:

   
   sudo apt update
   sudo apt install -y gpg-agent wget gnupg

2. Download the Intel APT repository’s public key and put it into the /usr/share/keyrings directory:

   
   # download the key to system keyring
   
   wget -qO- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | sudo gpg --dearmor -o /usr/share/keyrings/oneapi-archive-keyring.gpg
   
   # add signed entry to apt sources and configure the APT client to use Intel repository:
   
   echo "deb [signed-by=/usr/share/keyrings/oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main" | sudo tee /etc/apt/sources.list.d/oneAPI.list
   

3. Update the APT package list and repository index:

   
   sudo apt update
   

4. Use APT to install Intel® Deep Learning Essentials:

   
   sudo apt install intel-deep-learning-essentials-2026.1

Instead of using a package manager, you can install Intel® Deep Learning Essentials using offline installation scripts. Each installation script is a file containing all the needed files together with a script that extracts and installs the development package.

Important Use sudo to install files in system directories so they're available globally. Without sudo, files are installed in the current user's home directory.

1. Make sure the necessary tools are available:

   
   sudo apt update
   sudo apt install -y wget

    

2. Download the Intel® Deep Learning Essentials offline installation script and install:

   
   wget https://registrationcenter-download.intel.com/akdlm/IRC_NAS/c109e1ae-e02c-48a6-917b-b03b90d33f77/intel-deep-learning-essentials-2026.1.2.25_offline.sh
   sudo sh ./intel-deep-learning-essentials-2026.1.2.25_offline.sh -a --silent --eula accept

1. Make sure the necessary tools to add repository access are available:

   
   sudo apt update
   sudo apt install -y gpg-agent wget gnupg
   

    

2. Download the Intel APT repository’s public key and put it into the /usr/share/keyrings directory:

   
   # download the key to system keyring
   
   wget -qO- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | sudo gpg --dearmor -o /usr/share/keyrings/oneapi-archive-keyring.gpg
   
   # add signed entry to apt sources and configure the APT client to use Intel repository:
   
   echo "deb [signed-by=/usr/share/keyrings/oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main" | sudo tee /etc/apt/sources.list.d/oneAPI.list

    

3. Update the APT package list and repository index:

   
   sudo apt update
   

    

4. Use APT to install Intel® Deep Learning Essentials:

   
   sudo apt install intel-deep-learning-essentials-2026.1

Set Up Intel Deep Learning Environment Variables

Note If you installed PyTorch from Binaries, you already have the required packages in Intel Deep Learning Essentials. Do not configure the following variable to avoid conflicts.

For building PyTorch from source, use the following commands to configure the build environment:

source /opt/intel/oneapi/compiler/latest/env/vars.sh

source /opt/intel/oneapi/pti/latest/env/vars.sh

source /opt/intel/oneapi/umf/latest/env/vars.sh

source /opt/intel/oneapi/ccl/latest/env/vars.sh

source /opt/intel/oneapi/mpi/latest/env/vars.sh

For running PyTorch after the wheel is built, source the above variables and additionally source tcm:

source /opt/intel/oneapi/compiler/latest/env/vars.sh

source /opt/intel/oneapi/pti/latest/env/vars.sh

source /opt/intel/oneapi/umf/latest/env/vars.sh

source /opt/intel/oneapi/ccl/latest/env/vars.sh

source /opt/intel/oneapi/mpi/latest/env/vars.sh

source /opt/intel/oneapi/tcm/latest/env/vars.sh

Consider adding the runtime commands to your ~/.bashrc file so they run every time you log in or create a new shell session.

Note If you build PyTorch from source on Windows, here is required steps to enable Kineto before compiling.

Click on the following to download Intel® Deep Learning Essentials package. Then double-click on the downloaded exe files to run it and follow the instructions to install: intel-deep-learning-essentials-2026.1.2.22_offline.exe.

Set Up Intel Deep Learning Environment Variables

Use this command to configure environment variables, important folders, and command settings.

call "C:\Program Files (x86)\Intel\oneAPI\compiler\latest\env\vars.bat"

call "C:\Program Files (x86)\Intel\oneAPI\ocloc\latest\env\vars.bat"

These commands must be run every time you log in or create a new shell session.