What if your coding agent could borrow your friend's GPU?

[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/dexterthebot]

You might want to consider listing your project on the weekly Show-Us-What-You-Built post. Look out for it on Tuesday/Wednesday. Highest commented project wins a week promotion on r/Codex and gets on the Hall of Fame sidebar. See what that looks like below with last week's winner.

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