turbopilot
Turbopilot is an open source large-language-model based code completion engine that runs locally on CPU
Star Growth
Overview
TurboPilot was an open-source, self-hosted code completion engine designed to provide GitHub Copilot-like functionality while running entirely on local CPU hardware. Built on the llama.cpp library, it could run large language models like the 6 billion parameter Salesforce Codegen model using only 4GB of RAM, making AI-powered code completion accessible without cloud dependencies or powerful GPUs. The project supported multiple state-of-the-art models including WizardCoder, StarCoder, SantaCoder, and StableCode, offering features like 'fill in the middle' completion and support for various programming languages. However, the project has been officially deprecated and archived as of September 30, 2023, with the creator citing the availability of other mature solutions that better meet community needs. While it demonstrated the feasibility of local code completion, TurboPilot was positioned as a proof-of-concept rather than a production-ready tool, with acknowledged performance limitations including slow autocompletion speeds.
Deep Analysis
vs GitHub Copilot: runs entirely locally on CPU with 4GB RAM minimum — no cloud dependency, no subscription, BSD licensed, but deprecated in favor of newer alternatives
⚡ Capabilities
- • Self-hosted GitHub Copilot alternative using llama.cpp
- • Local code completion without cloud dependency
- • Runs on consumer hardware (4GB RAM minimum)
- • VS Code integration via vscode-fauxpilot plugin
- • OpenAI-compatible API for standard Copilot plugin support
- • CUDA GPU acceleration support
🔗 Integrations
✓ Best For
- ✓ Privacy-focused local code completion without internet
- ✓ Air-gapped development environments
- ✓ Cost-free Copilot alternative on modest hardware
✗ Not Ideal For
- ✗ Active development use (project deprecated)
- ✗ Teams needing fast real-time completions
- ✗ Multi-GPU setups
Languages
Deployment
⚠ Known Limitations
- ⚠ Project deprecated as of September 2023
- ⚠ Single GPU device support only
- ⚠ Autocompletion is quite slow
- ⚠ Requires separate model downloads
Pros
- + Complete privacy and offline operation with no data sent to external servers
- + Efficient resource usage, capable of running large models in just 4GB RAM on CPU
- + Support for multiple advanced code models including WizardCoder and StarCoder with fill-in-the-middle capabilities
Cons
- - Officially deprecated and archived as of September 2023, no longer maintained
- - Slow autocompletion performance compared to cloud-based solutions
- - Was explicitly described as proof-of-concept rather than production-ready software
Use Cases
- • Privacy-conscious developers needing code completion without cloud dependency
- • Organizations with strict data governance requiring completely offline AI tools
- • Researchers and developers experimenting with local language model deployment