# Using LM Studio with Kilo Code | Local LLMs

> Run local LLMs in Kilo Code by connecting to LM Studio's OpenAI-compatible server. Setup guide for VS Code and the CLI.

- 网址：https://funcoding.ai/agents/kilo-code/ai-providers/lmstudio/
- 来源：Kilo Code 官方文档原文（英文），MIT 许可，同步于 2026-10-11
- 官方原文：https://kilo.ai/docs/ai-providers/lmstudio

---
Kilo Code supports running models locally using LM Studio. LM Studio provides a user-friendly interface for downloading, configuring, and running local language models. It also includes a built-in local inference server that emulates the OpenAI API, making it easy to integrate with Kilo Code.

**Website:** [https://lmstudio.ai/](https://lmstudio.ai/)

## Setting Up LM Studio

1.  **Download and Install LM Studio:** Download LM Studio from the [LM Studio website](https://lmstudio.ai/).
2.  **Download a Model:** Use the LM Studio interface to search for and download a model. Some recommended models include:
    - CodeLlama models (e.g., `codellama:7b-code`, `codellama:13b-code`, `codellama:34b-code`)
    - Mistral models (e.g., `mistralai/Mistral-7B-Instruct-v0.1`)
    - DeepSeek Coder models (e.g., `deepseek-coder:6.7b-base`)
    - Any other model that is supported by Kilo Code, or for which you can set the context window.

    Look for models in the GGUF format. LM Studio provides a search interface to find and download models.

3.  **Start the Local Server:**
    - Open LM Studio.
    - Click the **"Local Server"** tab (the icon looks like `<->`).
    - Select the model you downloaded.
    - Click **"Start Server"**.

## Configuration in Kilo Code

**VSCode**

Open **Settings** (gear icon) and go to the **Providers** tab to add LM Studio. No API key is needed since LM Studio runs locally. You can configure the base URL if LM Studio is running on a different host or port.

The extension stores this in your `kilo.json` config file. You can also edit the config file directly — see the **CLI** tab for the file format.

**CLI**

LM Studio runs locally, so no API key is needed. Configure the base URL if LM Studio is running on a different host or port:

**Config file** (`~/.config/kilo/kilo.jsonc` or `./kilo.jsonc`):

```jsonc
{
  "provider": {
    "lmstudio": {
      "options": {
        "baseURL": "http://localhost:1234/v1",
      },
    },
  },
}
```

Then set your default model:

```jsonc
{
  "model": "lmstudio/codellama-7b",
}
```

## Using Custom or Unlisted Models

If the model you loaded in LM Studio doesn't appear in the Kilo model picker, you can register it as a custom model in your config file:

```jsonc
{
  "model": "lmstudio/my-custom-model",
  "provider": {
    "lmstudio": {
      "models": {
        "my-custom-model": {
          "name": "My Custom Model",
        },
      },
    },
  },
}
```

The model key (`my-custom-model`) must match the model identifier that LM Studio serves. If the display name you want differs from the API identifier, use the `id` field to set the API-facing name separately:

```jsonc
{
  "provider": {
    "lmstudio": {
      "models": {
        "my-llama": {
          "id": "meta-llama-3.1-8b-instruct",
          "name": "Llama 3.1 8B (Local)",
        },
      },
    },
  },
}
```

See [Custom Models](https://funcoding.ai/agents/kilo-code/code-with-ai/agents/custom-models/) for the full list of configuration fields and more examples.

## Tips and Notes

- **Resource Requirements:** Running large language models locally can be resource-intensive. Make sure your computer meets the minimum requirements for the model you choose.
- **Model Selection:** LM Studio provides a wide range of models. Experiment to find the one that best suits your needs.
- **Local Server:** The LM Studio local server must be running for Kilo Code to connect to it.
- **LM Studio Documentation:** Refer to the [LM Studio documentation](https://lmstudio.ai/docs) for more information.
- **Troubleshooting:** If you see a "Please check the LM Studio developer logs to debug what went wrong" error, you may need to adjust the context length settings in LM Studio.