How to Install PyTorch for CUDA 12.6

As of March 2025, PyTorch’s stable releases (up to 2.5.1) support CUDA 12.4 natively, but CUDA 12.6 isn’t officially listed yet. However, you can still use PyTorch with CUDA 12.6 by installing a compatible version or a nightly build. I’ll show you two main methods: using a stable release with CUDA 12.4 (which works with 12.6) and trying a nightly build for potential 12.6 support.

Method 1: Install Stable PyTorch with CUDA 12.4 (Compatible with 12.6)

PyTorch binaries ship with their own CUDA runtime, so a CUDA 12.4 build often works with CUDA 12.6 as long as your NVIDIA drivers support it. Here’s how to do it:

Step 1: Choose Your Package Manager

You can use pip or conda. I recommend conda for easier dependency management, but both work well.

Step 2: Set Up a Virtual Environment (Optional but Recommended)

To avoid conflicts, create a fresh environment:

With conda:

conda create -n pytorch_env python=3.11
conda activate pytorch_env

With pip:

python -m venv pytorch_env
source pytorch_env/bin/activate  # On Windows: pytorch_env\Scripts\activate

Step 3: Install PyTorch with CUDA 12.4

Run one of these commands based on your package manager:

Using conda:

conda install pytorch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 pytorch-cuda=12.4 -c pytorch -c nvidia

Using pip:

pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu124

Step 4: Verify the Installation

Check if PyTorch detects your GPU:

import torch
print(torch.__version__)  # Should show 2.5.1+cu124
print(torch.cuda.is_available())  # Should return True
print(torch.version.cuda)  # Should show 12.4

If torch.cuda.is_available() returns True, you’re good to go! CUDA 12.6 should work seamlessly with this setup thanks to driver compatibility.

Method 2: Install PyTorch Nightly Build for CUDA 12.6

For bleeding-edge support, try a PyTorch nightly build. These experimental releases often include support for newer CUDA versions like 12.6 before stable releases catch up.

Step 1: Update Your Package Manager

Ensure pip or conda is current:

pip: python -m pip install --upgrade pip
conda: conda update conda

Step 2: Install the Nightly Build

Nightly builds are hosted at a different URL. Use these commands:

Using pip:
```
pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu124
```
Note: Replace cu124 with cu126 if nightly builds for CUDA 12.6 become available—check PyTorch’s nightly wheel index.

Using conda:

conda install pytorch torchvision torchaudio pytorch-cuda=12.4 -c pytorch-nightly -c nvidia

Step 3: Verify CUDA 12.6 Support

Run this in Python:

import torch
print(torch.__version__)  # E.g., 2.6.0.dev20250322+cu124
print(torch.cuda.is_available())  # Should return True
print(torch.version.cuda)  # Check the CUDA version

If it works, you’re running a pre-release version with potential CUDA 12.6 support!

Fixing Common Errors When You Install PyTorch for CUDA 12.6

Sometimes, things don’t go smoothly. Here are solutions to common issues:

Error: “CUDA Version Mismatch”

If you see something like RuntimeError: CUDA version mismatch, it means PyTorch’s CUDA runtime doesn’t match your system’s CUDA toolkit. Since PyTorch bundles its own CUDA, uninstall the local CUDA toolkit (or ignore it) and rely on PyTorch’s version.

Error: “torch.cuda.is_available() Returns False”

Check Drivers: Ensure your NVIDIA drivers support CUDA 12.6 (e.g., version 560+).
Reinstall: Uninstall PyTorch (pip uninstall torch torchvision torchaudio) and reinstall with the correct command.
Clear Cache: Run pip cache purge to avoid old files.

Error: “No Matching Distribution Found”

This happens if the CUDA version isn’t available. Double-check the URL or switch to a stable CUDA 12.4 build.

Table: Installation Commands by OS and Method

Operating System	Method	Command
Windows	Stable (conda)	`conda install pytorch==2.5.1 pytorch-cuda=12.4 -c pytorch -c nvidia`
Windows	Nightly (pip)	`pip install --pre torch --index-url https://download.pytorch.org/whl/nightly/cu124`
macOS	Stable (pip)	`pip install torch==2.5.1 --index-url https://download.pytorch.org/whl/cu124`
Linux	Stable (conda)	`conda install pytorch==2.5.1 pytorch-cuda=12.4 -c pytorch -c nvidia`
Linux	Nightly (pip)	`pip install --pre torch --index-url https://download.pytorch.org/whl/nightly/cu124`

Tips to Avoid Problems

Use Virtual Environments: Keep your projects isolated to prevent conflicts.
Check Compatibility: Visit PyTorch’s official site for the latest supported CUDA versions.
Update Drivers: Newer drivers often fix bugs and improve CUDA support.

Conclusion: Ready to Roll with PyTorch and CUDA 12.6

You’ve now got the know-how to install PyTorch for CUDA 12.6! Whether you stick with the stable CUDA 12.4 version or experiment with a nightly build, your GPU is ready to tackle deep learning tasks. Test your setup with a simple model, and enjoy the speed boost CUDA brings.

Need more help? Drop a comment or question—I’d love to assist! Happy coding!

How to Install PyTorch for CUDA 12.6

Table of contents

What Is PyTorch and Why Use CUDA 12.6?

Before You Start: Check Your Setup