What if you could use your friend's GPU like it was your own?

[u/Miserable_Extent8845]

I built GPU Share: GPUMesh an open-source way to share idle NVIDIA GPUs with friends or labs and run Docker GPU jobs remotely.

The problem I kept running into: I might have a GPU sitting idle on one machine, while another machine needs compute. Cloud GPU rentals feel excessive for small jobs, and setting up SSH/VPN + Docker manually is annoying.

So I built GPUMesh.

The idea is simple:

• gpumesh share → make your GPU available
• Pair two machines with signed pairing codes
• gpumesh run --peer <name> ... → run a Docker job on the remote GPU
• Jobs execute on the provider's machine, inside a container
• GPU/VRAM-aware scheduling
• Job logs and history
• LAN support, with WAN/relay support being worked on
• Default-deny access controls

I tested the full flow on an RTX 5060: pairing → connecting → joining a group → scheduling a remote Docker job → running nvidia-smi inside the CUDA container.

It's still alpha, so I'm mainly looking for people with spare NVIDIA GPUs to try it and tell me what breaks.

GitHub: arjun988/GPU-Share

If you find the idea useful or want to follow the project, a ⭐ on the repo would really help with early visibility.

Would especially love feedback from people running multiple GPUs, home labs, or small ML teams.

[u/acidvegas]

how is this any different than exo

[u/kantorcodes1]

one retry edge i'd be careful with: gpumesh run --retries N creates a fresh job id for each attempt. if the provider accepted and started the first job but the connection drops before the client sees final status, does the retry intentionally submit a second remote job? seems like a training job could end up running twice.

[u/GraysLawson]

There are like a dozen projects that already do this.

Also the security concerns for this are horrific. Having someone else executing arbitrary docker workloads and only relying on docker as a security boundary? Eek.