# Tabby — Self-Hosted AI Code Completion

> An open-source, self-hosted AI coding assistant that provides IDE-integrated code suggestions and chat without sending data to third-party services.

## Install

Save as a script file and run:

# Tabby — Self-Hosted AI Code Completion

## Quick Use
```bash
docker run -it --gpus all -p 8080:8080 -v $HOME/.tabby:/data tabbyml/tabby serve --model StarCoder-1B --device cuda
```
Visit http://localhost:8080 and install the VS Code or JetBrains extension to get completions.

## Introduction
Tabby is a self-hosted AI code completion server that runs entirely on your own infrastructure. It lets teams use LLM-powered coding assistance while keeping source code private and under full organizational control.

## What Tabby Does
- Provides real-time code completions inside VS Code, JetBrains, Vim, and Emacs
- Runs a built-in chat interface for code Q&A and generation
- Indexes your repository to deliver context-aware suggestions
- Supports multiple open-weight models including StarCoder, CodeLlama, and DeepSeek
- Works with NVIDIA CUDA, Apple Metal, and CPU-only setups

## Architecture Overview
Tabby consists of a Rust-based HTTP server that loads a quantized code model and exposes a completions API. IDE extensions communicate with this server over HTTP. A background indexer crawls configured Git repositories to build a retrieval-augmented context window, improving suggestion relevance for private codebases.

## Self-Hosting & Configuration
- Deploy via Docker, Docker Compose, or bare-metal binary
- GPU acceleration with CUDA or Metal for fast inference
- Configure models through the admin web UI at port 8080
- Add Git repositories for codebase-aware completions
- Set up team accounts with LDAP or OAuth integration

## Key Features
- Full data privacy with no code leaving your network
- Sub-200ms latency on modern GPUs
- Repository-level code context through RAG indexing
- Multi-user support with admin dashboard and usage analytics
- Answer Engine for codebase-aware chat and code search

## Comparison with Similar Tools
- **GitHub Copilot** — cloud-hosted, requires sending code to Microsoft servers
- **Cody by Sourcegraph** — strong context engine but partially SaaS-dependent
- **Continue** — open-source IDE extension but requires separate model hosting
- **Codeium** — free tier available but proprietary backend
- **FauxPilot** — early self-hosted alternative, now largely unmaintained

## FAQ
**Q: What hardware do I need?**
A: A machine with an NVIDIA GPU (8 GB+ VRAM) is recommended. Smaller models like StarCoder-1B run on 4 GB VRAM or even CPU-only, though with higher latency.

**Q: Which IDEs are supported?**
A: VS Code, JetBrains (IntelliJ, PyCharm, etc.), Vim/Neovim, and Emacs all have official extensions.

**Q: Can I use my own fine-tuned model?**
A: Yes. Tabby supports GGML and HuggingFace model formats, so you can serve a custom fine-tuned checkpoint.

**Q: Is Tabby free?**
A: The core server and all IDE extensions are open-source under the Apache 2.0 license. An optional Tabby Cloud offering exists for managed hosting.

## Sources
- https://github.com/TabbyML/tabby
- https://tabby.tabbyml.com/docs/getting-started/

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Source: https://tokrepo.com/en/workflows/asset-0f6772bd
Author: Script Depot