CUDA Support for WSL 2

For more efficient testing of LLAMA 2, I recommend taking advantage of GPU acceleration in WSL 2, available on notebooks. This approach significantly increases performance and efficiency when working with LLAMA 2. In my latest blog post, you will find a detailed guide on how to easily and quickly set up GPU acceleration in WSL 2 on your notebook.

  • At first install – The latest NVIDIA Windows GPU Driver will fully support WSL 2. With CUDA support in the driver, existing applications (compiled elsewhere on a Linux system for the same target GPU) can run unmodified within the WSL environment.
  • Once a Windows NVIDIA GPU driver is installed on the system, CUDA becomes available within WSL 2. The CUDA driver installed on Windows host will be stubbed inside the WSL 2 as libcuda.so, therefore users must not install any NVIDIA GPU Linux driver within WSL 2.
  • Installation of Linux x86 CUDA Toolkit using WSL-Ubuntu Package
wget https://developer.download.nvidia.com/compute/cuda/repos/wsl-ubuntu/x86_64/cuda-wsl-ubuntu.pin
sudo mv cuda-wsl-ubuntu.pin /etc/apt/preferences.d/cuda-repository-pin-600
wget https://developer.download.nvidia.com/compute/cuda/12.3.1/local_installers/cuda-repo-wsl-ubuntu-12-3-local_12.3.1-1_amd64.deb
sudo dpkg -i cuda-repo-wsl-ubuntu-12-3-local_12.3.1-1_amd64.deb
sudo cp /var/cuda-repo-wsl-ubuntu-12-3-local/cuda-*-keyring.gpg /usr/share/keyrings/
sudo apt-get update
sudo apt-get -y install cuda-toolkit-12-3

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Author: Daniel Micanek

Senior Service Architect, SAP Platform Services Team at Tietoevry | SUSE SCA | vExpert ⭐⭐⭐⭐ | vExpert NSX | VCIX-DCV | VCAP-NV Design | VCAP-DCV Design+Deploy | VCP-DCV/NV/CMA/AM/DTM | NCIE-DP | OCP | Azure Solutions Architect | Certified Kubernetes Administrator (CKA)