# Install cgpu
npm i -g cgpu
# First run will launch an interactive setup wizard
# Connect to a cloud GPU instance quickly without setup any time after that
cgpu connect
# Run a command on a cloud GPU instance without a persistent terminal (but mantaining file system state)
cgpu run nvidia-smi You can start a local server that proxies requests to Google Gemini using the cgpu serve command. This allows you to use Gemini with tools that expect an OpenAI-compatible API.
# Start the server on port 8080
cgpu serve
# Specify port and model
cgpu serve --port 3000 --default-model gemini-2.0-flashFor an example of using this with the OpenAI client, check out python_example. This requires you have the gemini cli installed.
The primary goal of this project to facilitate a high quality developer experience for those without GPUs who would like to learn CUDA C++ This means 3 main things:
- Free: Avoid having to pay while learning.
- Highly Available: Run quickly instead of having to wait in a queue so that users can compile quickly and learn faster.
- In User Terminal: Allows developers to use their own devtools/IDEs (Neovim, Cursor, etc) so they can be most productive.
I will continue to add to the CLI as I find more free compute sources and developer experience improvements. To see what I am currently planning to add check out the Issues tab on Github. Feel free to create new Issues for suggestions/problems you run into while learning!
