# Show HN: Rgpu – a PyTorch device whose tensors live on a remote GPU

> Source: <https://github.com/ymcrcat/rgpu>
> Published: 2026-10-07 05:15:44+00:00

rGPU runs GPU work on a remote NVIDIA machine while the application stays on the client. It currently offers two paths:

| Path | Use it for | Interface | 
|---|---|---|
| PyTorch device | PyTorch programs that can opt into an `rgpu` device | `torch` operations over TCP | 
| CUDA shim | Existing Linux CUDA programs, including stock CUDA PyTorch | `libcuda` , CUDA Runtime, cuBLAS, cuBLASLt, and cuDNN shims | 

The PyTorch device is the simpler integration. The CUDA shim covers existing binaries but has a larger compatibility surface.

The Fumadocs site in [`website/`](https://github.com/ymcrcat/rgpu/blob/main/website) is the product documentation:

Engineering records and experiments are indexed in [`docs/README.md`](https://github.com/ymcrcat/rgpu/blob/main/docs/README.md).

Install rGPU with `pip install rgpu`, or `pip install -e ./python` from this
checkout, then follow the [quickstart](https://github.com/ymcrcat/rgpu/blob/main/website/content/docs/quickstart.mdx) to
deploy the server. Save this as `smoke.py` in your workload directory:

``` python
import torch
import rgpu

x = torch.ones(4, device="rgpu")
print((x * 2).sum().item())  # 8.0
```

Run it in the environment where rGPU is installed, using your server's SSH destination and options:

```
rgpu-run --host user@gpu-host --ssh-port 2222 -i ~/.ssh/gpu_key \
  python smoke.py
```

The program selects the device; `rgpu-run` opens the tunnel and configures the
connection. The expected output is `8.0`.

For existing Linux CUDA programs, follow the
[CUDA shim guide](https://github.com/ymcrcat/rgpu/blob/main/website/content/docs/cuda-shim.mdx), starting with
`./scripts/build_client.sh`.

Neither protocol authenticates or encrypts connections. Keep `rgpu-opserver`
on its default localhost bind and use SSH. The CUDA server listens on all IPv4
interfaces: restrict port 9713 with host/cloud firewall rules before starting
it, even when using an SSH tunnel. See [deployment](https://github.com/ymcrcat/rgpu/blob/main/website/content/docs/operations.mdx).

```
# C++ client and fake-driver tests
./scripts/build_client.sh

# Python tests
python -m pip install -e './python[test]'
python -m pytest python/tests

# Static documentation
npm --prefix website ci
npm --prefix website run build
```

See [`scripts/README.md`](https://github.com/ymcrcat/rgpu/blob/main/scripts/README.md) for the remaining build, cloud,
and hardware commands. Generated C++ is committed; its policy and regeneration
steps are in [`codegen/README.md`](https://github.com/ymcrcat/rgpu/blob/main/codegen/README.md).

| Path | Purpose | 
|---|---|
| `client/` | CUDA client shims and transport | 
| `server/` | CUDA server and dispatch | 
| `common/` | Shared protocol and generated API metadata | 
| `python/` | PyTorch device and launcher | 
| `tests/` | C++, Python, CUDA, and hardware checks | 
| `codegen/` | CUDA header parser and source generators | 
| `website/` | Fumadocs product documentation | 
| `docs/` | Design records, measurements, and experiment reports | 
| `jax/` | Experimental JAX work; not a supported product path | 
| `scripts/` | Build, deployment, cloud, and test helpers | 
| `skills/` | Installable agent guidance for using rGPU | 

Historical implementation notes and experimental results are indexed in
[`docs/README.md`](https://github.com/ymcrcat/rgpu/blob/main/docs/README.md).

Licensed under the [Apache License 2.0](https://github.com/ymcrcat/rgpu/blob/main/LICENSE).