> ## Documentation Index
> Fetch the complete documentation index at: https://docs.salad.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Running PyTorch on RTX 5090 and 5080 GPUs

> Use PyTorch with CUDA 12.8 on RTX 5090 and 5080 GPUs

*Last Updated: April 20, 2026*

## Overview

Nvidia's RTX 5090 and RTX 5080 GPUs (Blackwell architecture) require CUDA 12.8. PyTorch 2.7.0 was the first stable
release to add native sm\_120 support - shipping pre-built CUDA 12.8 wheels with updated cuDNN, NCCL, and Triton 3.3. The
current recommended version is **2.11.0**.

Any PyTorch image with the `-cuda12.8-` tag on version 2.7.0 or later will work with RTX 50-series GPUs.

## Recommended Images

Use the latest images from the [official PyTorch Docker Hub](https://hub.docker.com/r/pytorch/pytorch):

* `pytorch/pytorch:2.11.0-cuda12.8-cudnn9-runtime`
* `pytorch/pytorch:2.11.0-cuda12.8-cudnn9-devel`

Earlier versions with CUDA 12.8 support (2.7.0 minimum):

* `pytorch/pytorch:2.9.1-cuda12.8-cudnn9-runtime`
* `pytorch/pytorch:2.9.1-cuda12.8-cudnn9-devel`
* `pytorch/pytorch:2.8.0-cuda12.8-cudnn9-runtime`
* `pytorch/pytorch:2.7.0-cuda12.8-cudnn9-runtime`

## Example Dockerfile

```dockerfile theme={null}
FROM pytorch/pytorch:2.11.0-cuda12.8-cudnn9-runtime

WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .
CMD ["python", "train.py"]
```

<Info>
  If you intend to run a workload both on 50-series GPUs and older 40- or 30-series GPUs, you might need to maintain
  separate Docker images. Older GPUs might not support CUDA 12.8, so a 50-series image will not work on older hardware.
</Info>