Deploy RunPod’s official PyTorch template
In RunPod, go to Pods → Deploy and choose the official Runpod PyTorch template on an Ubuntu 22.04 or 24.04 image. Click Edit Template and apply the settings below, then pick a GPU and deploy. Maestro lists an NVIDIA GPU with 6 GB+ of VRAM; a 24 GB card such as an RTX 4090 leaves room for most Pinokio apps. Maestro’s environment uses PyTorch built for CUDA 12.8, so filter for CUDA 12.8 or newer (13.x for RTX 50-series), and it asks for 150 GB of free disk (500 GB recommended).
| Setting | Value |
|---|---|
| Template | Runpod PyTorch (Ubuntu 22.04 or 24.04) |
| Expose HTTP ports | 6080 (keep the template’s existing ports) |
| Volume disk | 150 GB or more, mounted at /workspace |
| CUDA version filter | 12.8 or newer |