1
Prerequisites: System Requirements and Docker Setup
Before deploying Tabby, ensure your machine meets the hardware requirements to run local LLMs efficiently. Tabby requires a GPU (NVIDIA with CUDA support is recommended) for optimal performance, though CPU inference is possible for smaller models. Verify you have Docker and Docker Compose installed. For NVIDIA GPUs, ensure the NVIDIA Container Toolkit is configured so Docker can access the GPU. Check your installation by running `docker run --rm --gpus all nvidia/cuda:12.1.0-base-ubuntu22.04 nvidia-smi`. If this command outputs GPU details, your environment is ready. If you are on macOS, ensure you have sufficient RAM (16GB+ recommended) as Metal acceleration will be used. This step is critical because running AI models locally is resource-intensive; under-provisioned hardware will result in slow response times or out-of-memory errors.
Pro Tip
If you are using an older GPU or limited RAM, consider starting with a smaller model like StarCoder2-3B instead of the default 7B or 13B variants to ensure smoother performance.
2
Deploy Tabby Server Using Docker
The easiest and most robust way to start Tabby is using Docker. Open your terminal and run the following command to pull and start the latest stable release: `docker run -it -p 8080:8080 --gpus all -v $HOME/.tabby:/root/.tabby tabbyml/tabby serve --model StarCoder2-7B`. This command maps port 8080 to your local machine, mounts a volume for persistent data (like user indices and chat history), and starts the server with the StarCoder2-7B model. The `--gpus all` flag is essential for Linux users with NVIDIA cards. On macOS, you might omit the GPU flag if using the default Metal backend, or specify it depending on your Docker setup. Once the logs show 'Listening on 0.0.0.0:8080', open your browser and navigate to `http://localhost:8080`. You will see the Tabby Web UI, confirming the server is running. This local instance acts as your private AI backend, ensuring no code snippets leave your machine.
Pro Tip
The first launch may take several minutes as Tabby downloads the model weights. Ensure your internet connection is stable during this initial download phase.
3
Install and Configure the IDE Extension
To integrate Tabby into your workflow, install the official extension for your preferred IDE. For VS Code users, open the Extensions Marketplace and search for 'Tabby'. Click 'Install'. For JetBrains IDEs (IntelliJ, PyCharm, etc.), go to Settings > Plugins > Marketplace and search for 'Tabby'. After installation, restart your IDE. Open the Tabby settings within your IDE. You will see a field for 'Server Endpoint'. Enter `http://localhost:8080` (or the IP address if running on a different machine). Click 'Test Connection' to verify the link. If successful, you will see a green checkmark. Next, switch the backend from 'Tabby Cloud' to 'Self-Hosted'. This crucial step ensures your IDE communicates exclusively with your local instance, bypassing any external API calls. Save the settings. You may need to reload the window for changes to take effect.
Pro Tip
If you are connecting from a remote machine, ensure your firewall allows inbound traffic on port 8080, and replace 'localhost' with your server's IP address in the IDE settings.
4
Enable Real-Time Inline Code Completion
With the extension connected, Tabby is ready to provide inline completions. Start typing a function signature or a comment in your code editor. You should see gray text appear automatically, suggesting the next lines of code. This is Tabby's inline completion feature, powered by the local model. Press `Tab` to accept the suggestion or `Escape` to dismiss it. To test this, create a new Python file and type `def calculate_fibonacci(n):`. Tabby should suggest the loop structure and return statement. The suggestions are context-aware, analyzing your current file and potentially other open files depending on your configuration. This feature works offline and in real-time, providing a seamless coding experience without latency spikes associated with cloud APIs. Experiment with different languages to see how well the local model handles syntax and logic patterns specific to your stack.
Pro Tip
If completions feel slow, check your system resources. High CPU/GPU usage by other applications can degrade inference speed. Consider adjusting the 'Completion Timeout' in the IDE settings if you are on a lower-end GPU.
5
Utilize the Chat Interface for Code Assistance
Beyond inline completions, Tabby offers a chat interface for complex tasks like refactoring, explanation, and debugging. In VS Code, open the Command Palette (`Ctrl+Shift+P` or `Cmd+Shift+P`) and run 'Tabby: Open Chat'. Alternatively, look for the Tabby icon in the activity bar. Here, you can ask questions like 'How can I optimize this SQL query?' or 'Explain this regular expression'. Since the model is local, your code context is never sent to third parties. You can also use the 'Edit' feature by selecting code in your editor and right-clicking to 'Tabby: Edit'. Type your instruction, such as 'Convert this class to TypeScript', and Tabby will generate the refactored code directly in your editor. This interaction loop allows for rapid iteration and learning without leaving your development environment. The chat history is stored locally in the `~/.tabby` directory, giving you full control over your development logs.
Pro Tip
For better chat responses, provide context by mentioning specific file names or pasting relevant code snippets before asking your question. Local models benefit greatly from explicit context due to smaller parameter counts compared to massive cloud LLMs.
6
Configure Repository Indexing for Enhanced Context
To improve the accuracy of completions and chat responses, you can index your repository code. This allows Tabby to understand cross-file dependencies and project-specific patterns. In the Tabby Web UI (`http://localhost:8080`), navigate to the 'Settings' or 'Repository' section. Add your local repository path. Tabby will begin indexing the codebase, creating embeddings that the model can reference. This process is CPU-intensive and may take time for large projects. Once indexed, Tabby will pull relevant code snippets from other files when generating completions, significantly improving relevance. For example, if you are working on a backend service, Tabby will suggest imports and function calls based on your existing API definitions. This feature transforms Tabby from a simple autocompleter into a true project-aware assistant, reducing boilerplate and ensuring consistency across your codebase.
Pro Tip
Exclude large `node_modules`, `venv`, or build directories from indexing to save time and disk space. Use a `.tabbyignore` file similar to `.gitignore` to control what gets indexed.
7
Optimize Performance and Security Settings
Finally, fine-tune your Tabby instance for your specific needs. In the Web UI, you can switch models. If the 7B model is too heavy, try `StarCoder2-3B` for faster inference on weaker hardware. Conversely, if you have a powerful GPU (24GB+ VRAM), you might try larger models for higher quality. You can also enable authentication by setting an admin password in the `config.yaml` file located in `~/.tabby/config`, securing your instance if exposed to a network. Regularly check the Docker logs (`docker logs <container_id>`) for any warnings. Tabby is designed for data sovereignty, so remember that all data remains on your machine. Back up your `~/.tabby` directory to preserve your indexed repositories and chat history. By mastering these configurations, you achieve a balance between performance, privacy, and cost-effectiveness, establishing a robust, self-reliant AI coding workflow.
Pro Tip
Restart the Docker container after any changes to `config.yaml`. Tabby does not automatically reload configuration files without a restart, which is a common pitfall for new users.