Connect a Local Coding Model to VS Code

Updated 10 November 2026 · Standard formula, shown below

Connecting a local coding model to VS Code lets you ask for code suggestions and explanations while the model runs on your own computer. You will need a model runner that exposes a local API and a VS Code extension that can connect to it; the exact setup depends on which runner and extension you choose.

What You’ll Need

A computer capable of running your chosen model. Larger models generally need more memory and take longer to respond.
A local model runner, such as Ollama or LM Studio, installed from its official source.
A coding model downloaded in a format supported by that runner.
Visual Studio Code and an extension that supports local model providers or OpenAI-compatible API endpoints.
Internet access for the initial downloads. Afterward, local inference can usually work offline, though extensions may have separate online features.

Step-by-Step

Choose a runner and model. Pick a runner that supports your operating system and a coding model it can load. Start with a model that fits your computer’s available memory; a model that is too large may fail to load or respond very slowly. Check the runner’s documentation for installation instructions and supported model names.
Install and start the runner. Follow the official setup instructions, then launch the runner. Some runners start a local service automatically; others require you to open the application or run a command. Keep the runner open while you use VS Code.
Download a model. Use the runner’s model library or documented command to fetch a model. For example, Ollama users can download a listed model from a terminal with ollama pull MODEL_NAME, replacing MODEL_NAME with an exact name from its library. Model names and commands can change, so verify them in the runner’s current documentation.
Confirm the model runs locally. Use the runner’s chat interface or documented command to send a short test prompt. For Ollama, ollama run MODEL_NAME starts an interactive session for a downloaded model. If this fails, resolve it before configuring VS Code: the editor cannot use a model the runner has not loaded successfully.
Check the local API endpoint. Find the runner’s API address and whether it uses an API key in its documentation or settings. Ollama commonly serves a local API at http://localhost:11434. LM Studio provides a local server that you start from its interface; its displayed address and port are the ones to use. Do not assume every runner uses the same address or API format.
Install a compatible VS Code extension. In VS Code, open Extensions using the square Extensions icon in the Activity Bar or Ctrl+Shift+X on Windows/Linux and Cmd+Shift+X on macOS. Search for an extension whose current documentation explicitly supports your runner or an OpenAI-compatible local endpoint. Review its publisher, permissions, and configuration instructions before installing.
Set the provider and model in the extension. Open the extension’s settings or its documented configuration file. Select the local provider if available; otherwise enter the runner’s base URL, model name, and any required API key. For a local server that does not require a key, follow the extension’s instructions for leaving that field blank or using a placeholder. Do not paste credentials into a public repository or shared settings file.
Reload or reconnect if requested. Some extensions apply settings immediately; others need you to reload the VS Code window using the Command Palette (Ctrl+Shift+P or Cmd+Shift+P) and the extension’s documented reload action, or simply restart VS Code. Ensure the runner’s server is still running.
Test inside a project. Open a small coding project, select a short piece of code, and use the extension’s documented chat or explain command. Then try a simple edit or completion. Check that the displayed model is the one you configured and that responses arrive without errors.
Check privacy and workspace behavior. Confirm that requests are sent to the local endpoint, not a remote provider. Review extension settings for telemetry, cloud fallback, or remote features, and disable them if you do not want them. A local model does not guarantee that every extension feature or diagnostic stays local.

Common Mistakes

Using a model name the runner does not recognize: Copy the exact name shown in its library or downloaded-model list.
Entering the wrong URL: Use the API base address, not a browser page or chat interface address. Check whether the extension expects a base URL or a full API route.
Mixing up API formats: A runner may offer an OpenAI-compatible endpoint, but that does not mean every extension can use it. Confirm compatibility in both products’ documentation.
Forgetting to start the server: A downloaded model is not necessarily available to VS Code until its runner or API service is running.
Choosing a model that exceeds available resources: Close unnecessary applications or select a smaller model if loading is slow, fails, or causes the computer to run out of memory.
Expecting every coding feature to work: Chat, inline completion, and code edits may use different model settings or require separate support in the extension.

Tips

Configure one model and one feature first. Add inline suggestions or additional models only after basic chat works.
Keep the runner’s model name and endpoint handy; extensions often ask for both separately.
Use a small, non-sensitive test project to check latency, code quality, and whether context from open files is being included.
Read the extension’s data-handling notes. Local inference describes where the model runs, but extensions may still offer optional online services.
Update the runner, model, and extension separately when needed, and recheck their compatibility after a change.

Troubleshooting

VS Code reports that it cannot connect: Confirm the runner is open and its local server is enabled. Check the configured host and port against the runner’s current settings. If another application is using the port, use the runner’s supported port setting and update the extension to match.

The model is missing or rejected: Verify that it has finished downloading and that you entered its exact identifier. If the extension offers a model list, refresh it or use the extension’s documented manual-entry option.

Responses are very slow or the runner stops: Try a smaller model, reduce the context or generation limits if the extension exposes those settings, and close memory-intensive applications. Performance depends on the model, computer, and settings.

Chat works but inline completion does not: Check whether the extension supports local models for inline suggestions, not just chat. Enable the completion feature and select its model separately if required.

Errors continue: Read the extension’s output or log panel and the runner’s logs for a specific cause. Recheck endpoint format, model support, and API-key requirements in both products’ documentation. Avoid exposing a local server to the public internet as a workaround.

Frequently Asked Questions

Can I use a local coding model without an internet connection?
Usually, yes, after downloading the runner, model, and extension. Some extensions may still use online services for optional features or updates.

Do I need an API key?
Not always. Some local runners accept requests without one; others or the VS Code extension may require a configured value. Follow their documentation rather than inventing a credential.

Will this work with every VS Code coding assistant?
No. The extension must support your runner’s API or a compatible endpoint. Check its provider list and setup guide before installing.

Does a local model see my whole project?
Not automatically. The extension typically sends selected code or context it chooses to include. Review its context and privacy settings, and verify what is sent.

Why does the local model give weaker suggestions than an online service?
Models differ in capability, and local hardware may limit model size or context. Try a suitable coding model and adjust the extension’s context settings if available.

Method: the standard formula for this calculation, rounded as each result states. See how the calculators work or report an error.