Hello everyone! Have you ever wanted to run powerful AI models on your Windows machine without switching to Linux? Thanks to Windows Subsystem for Linux with GUI (WSLg), you can now set up a complete development environment with GPU acceleration—right from Windows! In this guide, I'll walk you through everything you need to know, step by step. Whether you're new to WSL or just looking to supercharge your AI workflows, this post is for you. Let’s dive in!
System Requirements and Preparation
Before jumping into the setup, let's make sure your system is ready to support WSLg and GPU-accelerated tasks. Here's a checklist to help you confirm if your system is compatible:
| Requirement | Details |
|---|---|
| Operating System | Windows 10 (21H2) or later / Windows 11 |
| WSL Version | WSL 2 with WSLg (GUI support built-in) |
| GPU Driver | NVIDIA Driver supporting CUDA in WSL (465 or newer) |
| Hardware | NVIDIA GPU (Ampere, Turing, or newer recommended) |
Tip: To check if WSLg is already available, open PowerShell and run wsl --update to ensure the latest version is installed.
Installing and Configuring WSLg
Setting up WSLg is simple, especially with Windows 11 where WSLg comes built-in. Follow these steps to get started:
- Open PowerShell as Administrator
- Run: wsl --install (this installs Ubuntu by default)
- Reboot your PC if prompted
- Open Ubuntu via the Start menu
- Set your username and password for Linux
After installation, WSLg enables you to run Linux GUI apps alongside Windows apps. Try running gedit or nautilus in your WSL terminal to test the GUI integration!
Enabling GPU Acceleration in WSL
GPU support in WSL is a game changer for AI developers. To enable GPU acceleration in WSL, follow these steps:
- Install the latest NVIDIA driver for WSL from the official NVIDIA website
- Install CUDA Toolkit in WSL using: sudo apt update && sudo apt install -y nvidia-cuda-toolkit
- Test GPU support: nvidia-smi If successful, you'll see your GPU and CUDA version listed
Note: Only supported on NVIDIA GPUs with driver 465+ and CUDA 11+.
Running AI Frameworks with GPU in WSL
Once your GPU is ready inside WSL, you can install popular AI frameworks like PyTorch or TensorFlow with GPU support.
Here’s how you can set up PyTorch (with CUDA):
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121Or TensorFlow:
pip install tensorflowMake sure to test them using simple GPU test code like:
import torch print(torch.cuda.is_available())If it returns True, your WSL environment is ready for GPU-powered AI workloads!
Troubleshooting Common Issues
Even with a solid setup, you might run into a few hiccups. Here are common issues and how to solve them:
- nvidia-smi not found: Make sure the driver is installed both on Windows and WSL.
- Permission denied: Check if you used sudo when installing packages.
- Framework can’t detect GPU: Confirm you installed the correct version with CUDA support.
- WSL version issues: Run wsl --set-version <distro> 2 to switch to WSL2.
Keeping both Windows and WSL updated minimizes most problems. Also, check Microsoft and NVIDIA forums if you're stuck.
Frequently Asked Questions
How do I know if I’m using WSLg?
If you can launch Linux GUI apps like gedit, then you're using WSLg.
Do I need Windows 11 for GPU support?
Not necessarily. Windows 10 (21H2) also supports GPU in WSL2, but WSLg is smoother on Windows 11.
Is WSLg safe to use?
Yes, it’s officially supported by Microsoft and updated regularly.
Can I use AMD GPU instead of NVIDIA?
Currently, WSL GPU acceleration primarily supports NVIDIA with CUDA.
Will this setup work for Stable Diffusion or LLMs?
Yes! As long as your framework supports CUDA, you can run those models inside WSL.
Do I need Docker to run AI in WSL?
No, but Docker can help if you're managing multiple environments or dependencies.
Wrapping Up
Setting up GPU-accelerated AI in Windows through WSLg is no longer a complicated task. With just a few commands, you can build and run powerful models right from your familiar Windows environment. I hope this guide helped you navigate the process clearly and confidently. If you tried it or have any questions, feel free to share in the comments!
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