Connect your GPU
This gets a node running on your machine so it can take AI training jobs. Most of it is one command — the rest is Windows prerequisites you only ever do once.
Heads up: this is a technical setup and you will be using a terminal. Nothing here is irreversible, and every step can be re-run safely if something goes wrong.
Why contribute a GPU?
- Put idle hardware to work. A gaming GPU sits unused most of the day. Pooled with others it adds up to real training capacity.
- Nothing reaches into your machine. Jobs run locally, inside a container, on open-source code you can read. The network hands your node a job; it never gets a shell.
- You set the terms. The node only takes work while you have it switched on, and it stops on its own if the card gets too hot.
The point is to make serious compute reachable for independent developers and researchers who have the data but not the hardware, without going through a large cloud provider.
Before you start Windows, once per machine
Install the latest driver on Windows. Do not install a Linux driver inside WSL — the Windows one is what the container ends up using.
This runs Linux tooling on Windows without a dual boot. Everything after this point happens inside Ubuntu.
Then run one command inside Ubuntu
This installs Docker and the NVIDIA Container Toolkit, checks that a container can really see your GPUs, then fetches and starts the node. It is safe to re-run — every step checks before it acts.
Leave this as it is unless you are pointing the node at a different coordinator.
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That is the whole setup. When it finishes, head to the front door to register the node. The steps below are only for doing it by hand.
If you would rather do it by hand optional
Manual installation The script above does all of this for you
Open Ubuntu from the Start menu and run:
curl -fsSL https://get.docker.com | sh
sudo apt install -y wget
sudo systemctl restart docker
This is the step worth not skipping — it is where a broken driver or toolkit shows up.
docker run --rm --gpus all nvidia/cuda:12.2.0-base-ubuntu22.04 nvidia-smi
You should see a table listing your card.
You can skip this. The node runs in a container built
on nvidia/cuda, which already carries the CUDA runtime. On
the host you only need the driver, Docker and the container toolkit.
Install the full toolkit only if you want to compile CUDA code
yourself — it is several GB.
wget https://developer.download.nvidia.com/compute/cuda/repos/wsl-ubuntu/x86_64/cuda-wsl-ubuntu.pin
sudo mv cuda-wsl-ubuntu.pin /etc/apt/preferences.d/cuda-repository-pin-600
wget https://developer.download.nvidia.com/compute/cuda/12.8.1/local_installers/cuda-repo-wsl-ubuntu-12-8-local_12.8.1-1_amd64.deb
sudo dpkg -i cuda-repo-wsl-ubuntu-12-8-local_12.8.1-1_amd64.deb
sudo cp /var/cuda-repo-wsl-ubuntu-12-8-local/cuda-*-keyring.gpg /usr/share/keyrings/
sudo apt-get update
sudo apt-get install -y cuda-toolkit-12-8
And the compiler, if you want to build CUDA programs:
sudo apt-get install -y cuda-compiler-12-8
The installer finishes by printing an address. Open it on the machine you just set up: that dashboard runs beside the agent, and it is where you register the node and watch it work.
It is http://localhost:3000 unless something was
already using that port, in which case the installer says which
one it picked.