High-performance AI coding on consumer hardware.
Project description
🏠 LocalCode
High-performance AI coding on consumer hardware.
No cloud, no API keys, no data leaving your machine.
Install
pip install localcode
Run
cd your-project
localcode
That's it. First launch builds the inference server and downloads the model (~5 min, one time). After that, startup is ~15 seconds.
What it does
- Reads and edits files - understands your codebase, makes surgical edits
- Runs commands - tests, builds, git, shell
- Searches code - by pattern, content, or semantic meaning
- Fast mode - for routine coding tasks
- Reasoning mode - deep thinking for complex multi-step problems
- Uses tools automatically - the model picks its own tools
> refactor the auth module to use JWT and make sure the tests pass
LocalCode reads the files, plans the refactor, edits the code, runs the tests, and fixes failures - all locally.
Why local?
We are building for a world of truly democratized AI - where everyone has access to powerful, personalized, prompt AI anywhere, on any device, and in any location. True empowered local-first AI. LocalCode is the first step toward that vision.
How LocalCode compares
| LocalCode | Claude Code | OpenCode | Codex CLI | |
|---|---|---|---|---|
| Runtime | 100% on-device | Cloud (Anthropic API) | Cloud (any provider) | Cloud (OpenAI API) |
| Privacy | Code never leaves your machine | Code sent to Anthropic | Code sent to provider | Code sent to OpenAI |
| Cost | Free forever | $100+/mo (Max) or API credits | API credits (varies) | Free (included with ChatGPT) |
| Internet required | No | Yes | Yes | Yes |
Requirements
- Mac with Apple Silicon (M1/M2/M3/M4)
- 16GB RAM minimum
- Python 3.11+
- ~12GB free disk (10GB model + server)
How LocalCode works
LocalCode runs a custom llama.cpp fork with TurboQuant KV cache compression - a technique from Google's ICLR 2026 paper that we patched into llama.cpp for Apple Silicon. This compresses the KV cache 3.8x, fitting 32K context in 355 MiB on a 16GB MacBook.
The model (Gemma 4 26B-A4B) is a Mixture-of-Experts architecture - 25.2B total parameters but only 3.8B active per token. That's what makes 27 tok/s possible on a laptop.
Sponsors
If you'd like to sponsor LocalCode, reach out.
Contributing
See CONTRIBUTING.md.
License
Apache-2.0. See LICENSE.
Project details
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