# Nvidia GPU and CUDA Notes

**SD.Next** auto-detects **nVidia GPUs** and attempts to install a stable CUDA-enabled `torch` build at launch.  

> [!IMPORTANT]
> Do not manually install CUDA on your system as `torch` will install the appropriate CUDA version in its own environment  
> System-wide installation of CUDA may cause conflicts and is not required for SD.Next to utilize nVidia GPUs.
>
> [!TIP]
> Disable system memory fallback to avoid massive performance degradation when exhausting GPU memory: [Instructions](https://nvidia.custhelp.com/app/answers/detail/a_id/5490)

## Older GPUs

If you have an older nVidia GPU that is not supported by recent `torch` releases, you may need an older `torch` and CUDA combination.

*Example*: **Pascal** architecture (for example, RTX 10xx series) or older may require `torch==2.9` with `cuda==12.6`.  
Use the following before first startup to force a specific torch/CUDA combination:  

> `set TORCH_COMMAND='torch==2.9.1 torchvision==0.24.1 torchaudio==2.9.1 --index-url https://download.pytorch.org/whl/cu126'`  

## Older Drivers

If your system has older nVidia drivers that do not support newer CUDA versions, you may need an older `torch`/CUDA pair compatible with those drivers.

This is common when running on cloud instances which do not update drivers frequently.

*Example*: `cuda==13.0` requires newer nVidia drivers.  
Use the following before first startup to force installation from CUDA 12.8 wheels:  

> `set TORCH_COMMAND='torch torchvision --index-url https://download.pytorch.org/whl/cu128'`