How to Fix Torch/CUDA Version Mismatch in ComfyUI
Fix ComfyUI errors caused by mismatched PyTorch and CUDA versions, including wrong CUDA toolkit, driver incompatibility, and mixed wheel installs.
Community Knowledge
This page is based on common ComfyUI troubleshooting patterns and has not been fully tested across all environments. Back up your environment before changing packages.
If ComfyUI fails with CUDA errors despite having an NVIDIA GPU, the problem is often a mismatch between your PyTorch build, CUDA runtime version, and NVIDIA driver.
This is different from a missing CUDA install. The packages are present, but they were built for different CUDA versions and cannot work together.
Fast answer
Check your current combination:
.\python_embeded\python.exe -c "import torch; print(f'torch={torch.__version__}'); print(f'cuda_build={torch.version.cuda}'); print(f'available={torch.cuda.is_available()}')"Then run nvidia-smi and compare the CUDA version shown by the driver with the CUDA version PyTorch was built for.
What the errors look like
Version mismatches can surface as several different error messages:
RuntimeError: The NVIDIA driver on your system is too old (found version 11070).
Please update your GPU driver by downloading and installing a new version from:
https://www.nvidia.com/Download/index.aspxCUDA error: no kernel image is available for execution on the devicetorch.cuda.is_available() returns FalseUserWarning: CUDA initialization: The NVIDIA driver on your system is too oldWhy it happens
- Driver too old: Your NVIDIA driver supports CUDA 11.x but PyTorch was built for CUDA 12.x
- Mixed pip installs: A custom node installed a different torch build, mixing CUDA 11 and CUDA 12 wheels
- Wrong index URL: PyTorch was installed from the default PyPI index (CPU-only) instead of the CUDA-specific index
- New GPU with old environment: You upgraded your GPU but kept the old Python environment
- Portable package mismatch: The portable package was built for a different CUDA version than your driver supports
Understanding the version chain
Three versions must be compatible:
| Component | Where to check | Example |
|---|---|---|
| NVIDIA driver | nvidia-smi top right | CUDA Version: 12.6 |
| PyTorch CUDA build | torch.version.cuda | 12.4 |
| Actual CUDA availability | torch.cuda.is_available() | True or False |
The rule: the driver's CUDA version must be equal to or newer than the PyTorch CUDA build version. A driver showing CUDA 12.6 can run PyTorch built for CUDA 12.4, but not the other way around.
Step-by-step fix
Step 1: Identify your current versions
nvidia-smiNote the CUDA Version in the top-right corner. Then check PyTorch:
.\python_embeded\python.exe -c "import torch; print(f'torch={torch.__version__}'); print(f'cuda_build={torch.version.cuda}'); print(f'cudnn={torch.backends.cudnn.version() if torch.backends.cudnn.is_available() else None}'); print(f'available={torch.cuda.is_available()}')"Step 2: Update your NVIDIA driver (if too old)
If your driver's CUDA version is lower than what PyTorch needs, update the driver:
- Go to NVIDIA Driver Downloads
- Select your GPU model and OS
- Download and install the latest Game Ready or Studio driver
- Reboot your computer
After updating, run nvidia-smi again to confirm the new CUDA version.
Step 3: Reinstall PyTorch with the correct CUDA version
If the driver is current but PyTorch has the wrong build, reinstall PyTorch matching your driver:
.\python_embeded\python.exe -s -m pip uninstall -y torch torchvision torchaudio xformers
.\python_embeded\python.exe -s -m pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128Replace cu128 with the appropriate version for your driver. Common options:
| Driver CUDA | PyTorch index |
|---|---|
| 12.6+ | cu126 or cu128 |
| 13.0+ | cu130 |
Check the PyTorch install page for the latest supported combinations.
Step 4: Verify the fix
.\python_embeded\python.exe -c "import torch; print(torch.__version__); print(torch.version.cuda); print(torch.cuda.is_available()); print(torch.cuda.get_device_name(0))"All four lines should show valid values. Then restart ComfyUI.
When not to run pip install blindly
- Do not install
torchfrom the default PyPI index — it gives you CPU-only builds - Do not install CUDA Toolkit separately unless you are compiling custom extensions. PyTorch wheels bundle their own CUDA runtime
- Do not let custom node
requirements.txtfiles reinstall torch without checking the index URL - If
pip checkshows torch-related conflicts after a custom node install, fix torch first before installing more packages
How Wonderful Launcher can help
Wonderful Launcher detects your GPU and driver version, then helps match the correct PyTorch CUDA build for your system. It helps prevent custom nodes from accidentally replacing your working torch installation with an incompatible version.
Download Wonderful Launcher — it's free and can recover your environment without reinstalling.
Related errors
- Torch Not Compiled With CUDA Enabled
- CUDA Out of Memory in ComfyUI
- ComfyUI GPU Compatibility
- ComfyUI Dependency Conflicts
- ComfyUI Torch Missing
Source References
Start with Wonderful Launcher if this issue touches your real ComfyUI environment. Use the docs to understand the fix, and use the app to inspect the machine you already have.
Download Wonderful LauncherDid this fix your issue?
Your answer helps prioritize verified ComfyUI repairs.