RGPU – Use a remote Nvidia GPU from PyTorch on a machine that has none

RGPU launched as a tool that lets developers run PyTorch operations and hold tensors on a remote Nvidia GPU from a machine with no CUDA installation, including a Mac, by keeping Python local and routing work over an SSH tunnel to a GPU server. The product supports moving models, running autograd, shipping compiled graphs, and a CUDA shim that preserves device="cuda" via a Linux driver-remoting path, and it documents measuring host waits, transfers, and completed GPU work. rGPU states it is built for trusted GPU hosts and directs users to its deployment guidance before connecting.

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. Local code · remote GPU Python stays with you. Tensors stay on the GPU. train.py · local Python 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 https://rgpu.dev/docs/operations/ before connecting.