Self-hosted AI coding assistants
Continue documents editor integrations and model-provider configuration; Tabby documents a self-hosted server and editor extensions. Ollama provides a local model-serving option. Use them as a starting point, then validate completion quality and responsiveness on your own codebase.
Updated July 11, 2026
The short answer
The model is half the setup
Model memory use varies with the model, quantization, context, and runtime. Check each model's documentation and measure the configuration you plan to run.
| Model | Memory guidance | Role |
|---|---|---|
| Qwen2.5-Coder family | Check the model card | A code-focused model family for local evaluation |
| Your selected local model | Measure on your hardware | Choose it after testing completion quality and responsiveness |
Responsiveness is the product
Inline suggestions need to arrive while they are still useful. Test with representative files, keep the context and model size practical for your hardware, and adjust one setting at a time when completions become slow.
Set expectations with a trial
A local setup is not a drop-in quality or workflow guarantee. Evaluate inline completion, chat, and larger edits separately against the work your team actually performs.
You can use different providers for different tasks, but make those choices deliberately and document the data-handling rules for each.
What local control changes
A local deployment gives you control over where requests are processed and which model runtime is used. It also makes you responsible for capacity, updates, and evaluation. Size the hardware using the 8 GB VRAM guide and test the complete editor workflow before standardizing on it.
Questions people actually ask
What hardware do I actually need?
Hardware needs depend on the model, quantization, context size, and editor workflow. Start with a model documented for local use, then measure memory use and completion responsiveness on your own machine.
Is local autocomplete really as good as Copilot?
It depends on the model, repository, editor integration, and hardware. Compare both on representative files and tasks rather than assuming a local setup will match or outperform a hosted service.
Can I mix local and paid?
Yes. Continue documents model-provider configuration, and Tabby documents a self-hosted server with editor extensions. Keep provider choices explicit and test each integration before relying on it for daily work.
Why is FIM support in the model table?
Fill-in-the-middle is a completion format in which a model generates code for a gap between surrounding context. Check a model's documentation for supported completion formats and test it in the editor workflow you use.
Sources
Every tool above is one row in the Awesome Open Source AI registry for open-source AI projects.
