Are you a Windows user looking to harness your NVIDIA GPU for AI development using PyTorch, TensorFlow, or Hugging Face? You no longer need to dual-boot or switch to Linux to run high-performance AI workloads. With the right setup, Windows can be your ultimate AI development powerhouse.
This tech concept, walks you through a bulletproof WSL + CUDA + PyTorch setup for high-performance deep learning right inside Ubuntu on Windows 11 — with full support for new-generation GPUs like the RTX 40/50 series. I’ve spent 20+ years empowering businesses, especially startups, to achieve extraordinary results through strategic technology adoption and transformative leadership. My experience, from writing millions of lines of code to leading major initiatives, is dedicated to helping them realise their full potential.

Why Use WSL for AI?

WSL (Windows Subsystem for Linux) allows developers to run Linux distributions like Ubuntu directly inside Windows, offering the best of both worlds:

• Access to full Linux-based AI tools (pip, conda, PyTorch, CUDA)
• Native GPU acceleration via WSL 2 GPU passthrough
• No need to dual-boot or use clunky virtual machines

Step-by-Step: Setup AI Workflow in WSL with NVIDIA GPU

1. Prerequisites

• Windows 11 with WSL 2
• NVIDIA RTX 40/50 Series GPU
• Latest NVIDIA driver (e.g., version 576.57 or higher)
• Ubuntu 22.04 or later installed via WSL

Check driver (in windows powershell):

nvidia-smi

2. Verify CUDA Support in WSL

Check if your GPU is detected inside WSL:

nvidia-smi

If it works, you should see something like:

GPU 0: NVIDIA GeForce RTX 50...

3. Avoid Outdated Toolkit: Don’t Use apt install nvidia-cuda-toolkit

❌ This installs older CUDA versions (e.g., 10.x), which are incompatible with newer GPUs and modern frameworks.

Instead, install from NVIDIA’s official repository.

4. Install CUDA Toolkit 12.3 (Recommended)

Run the following inside WSL (Ubuntu):

# Add NVIDIA's CUDA repository
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/cuda-ubuntu2204.pin
sudo mv cuda-ubuntu2204.pin /etc/apt/preferences.d/cuda-repository-pin-600

sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/3bf863cc.pub

sudo add-apt-repository "deb https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64/ /"

# Install CUDA Toolkit
sudo apt update
sudo apt install -y cuda-toolkit-12-3

Add CUDA to your path:

echo 'export PATH=/usr/local/cuda/bin:$PATH' >> ~/.bashrc
echo 'export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc
source ~/.bashrc

Verify:

nvcc --version

5. Install PyTorch with Correct CUDA Version

Use the nightly PyTorch wheel for newer architectures (sm_89, sm_90):

pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu121

This ensures support for RTX 40/50 and similar GPUs.

6. Verify PyTorch GPU Support

Test with Python:

import torch
print(torch.cuda.is_available())        # Should be True
print(torch.cuda.get_device_name(0))    # Should print RTX GPU name

This setup is ideal for:

• ML researchers who prefer Linux but work on Windows laptops
• Hobbyists trying LLMs, GenAI, diffusion models
• Developers running Hugging Face transformers, LangChain, or RLHF pipelines

My Tech Advice: The outdated notion that Windows is only for novice developers is long gone. Today, with WSL, you get the full power of a professional-grade Linux development environment—right inside Windows, without the hassle of dual-booting or running heavy virtual machines.

Stop letting OS switching slow your workflow. Build smarter. Run Linux-based AI workloads with native GPU acceleration—all within Windows. It’s time to upgrade how you develop.
Build smarter — run Linux AI with GPU speed, all inside Windows.

Ready to build your own tech solution ? Try the above tech concept, or contact me for a tech advice!

#AskDushyant

Note: The names and information mentioned are based on my personal experience; however, they do not represent any formal statement.

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