Local coding assistants

Local coding assistants bring code completion and chat into your editor without sending your source code to an outside service. That matters for companies with strict rules about where code may go, for contractors working on client repositories, and for anyone who simply prefers to keep their work on their own machine.

There are two common setups. Editor extensions connect to a model runner on your own computer and use it for completion, chat and edits. Self-hosted servers run on a stronger machine, often one with a good GPU, and serve completions to a whole team through editor plugins. Both keep the code inside your own walls.

Expect a different experience from the largest hosted assistants. Models small enough to run on a laptop give quick single-line and short multi-line completions and handle explanations well, but they are weaker at large refactors and long reasoning. Many developers use a small fast model for completion and a larger one, on the same machine or a team server, for chat. Review every suggestion the way you would review a colleague's change.

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4 local AI tools in local coding assistants

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An open-source assistant for VS Code and JetBrains that can use a local model for chat, edits and autocomplete.

  • Open source

Tabby

by TabbyML

A self-hosted code completion server you run on your own GPU, with editor plugins for common IDEs.

  • Open source
  • Local API server

Refact.ai

An open-source coding assistant and agent that can be self-hosted with your own models.

  • Open source

llama.vscode

by ggml-org

A VS Code extension for code completion from a llama.cpp server running on your machine.

  • Open source
  • Works offline after setup

Latency shapes how an assistant feels more than raw quality does. Completion has to arrive before you type the next few characters, so a smaller model that answers instantly is usually better for that job than a larger one that pauses. Run completion and chat on different models if your hardware allows, and turn completion off in files where it gets in the way.

Check how each tool gathers context. Better context means better suggestions, so tools read open files, project structure or documentation. Make sure you understand what is indexed and where that index is stored, and check the licenses of the code models you load, since some restrict commercial use.

Questions about local coding assistants

Can a local model replace a hosted coding assistant?

For completion and everyday questions, often yes. For complex multi-file changes, the largest hosted models still do better. Many teams use local models for private code and hosted ones only where rules allow.

What hardware do I need for local code completion?

A recent laptop can run a small completion model. Larger chat models benefit from a GPU with plenty of video memory or an Apple silicon Mac with generous RAM. A shared team server avoids buying that for everyone.

Does the assistant send my code anywhere?

Not when it is pointed at a local or self-hosted model. Some extensions also support hosted models or send usage data, so check the configuration and turn off anything you do not want.

Can a team share one local coding model?

Yes. Run a self-hosted completion server or runner on a machine with a capable GPU, put it behind authentication, and point everyone's editor plugins at it. Watch memory and response times as more people connect.

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