rGPU / Product documentation

Your code, here.
Your GPU, anywhere.

Keep Python on your laptop. Run PyTorch operations and hold tensors on a remote GPU, including from a Mac with no CUDA installation.

Start with PyTorch Compare the two paths
Local code · remote GPU
A laptop sends tensor operations through an encrypted tunnel to a remote GPU server and receives results
Python stays with you. Tensors stay on the GPU.
train.py · local Python
import rgpu, torch

x = torch.randn(1024, 1024, device="rgpu")
y = (x @ x).relu().sum()
print(y.item())
Local Python → SSH tunnel → GPU server

Train with PyTorch

Move a model, run autograd, and ship compiled graphs.

Use the CUDA shim

Keep device="cuda" with the Linux driver-remoting path.

Understand the cost

Measure host waits, transfers, and completed GPU work.

Built for trusted GPU hosts. Read the deployment guidance before connecting.