---
summary: "Run Nodmix with LM Studio"
read_when:
  - You want to run Nodmix with open source models via LM Studio
  - You want to set up and configure LM Studio
title: "LM Studio"
---

LM Studio is a friendly yet powerful app for running open-weight models on your own hardware. It lets you run llama.cpp (GGUF) or MLX models (Apple Silicon). Comes in a GUI package or headless daemon (`llmster`). For product and setup docs, see [lmstudio.ai](https://lmstudio.ai/).

## Quick start

1. Install LM Studio (desktop) or `llmster` (headless), then start the local server:

```bash
curl -fsSL https://lmstudio.ai/install.sh | bash
```

2. Start the server

Make sure you either start the desktop app or run the daemon using the following command:

```bash
lms daemon up
```

```bash
lms server start --port 1234
```

If you are using the app, make sure you have JIT enabled for a smooth experience. Learn more in the [LM Studio JIT and TTL guide](https://lmstudio.ai/docs/developer/core/ttl-and-auto-evict).

3. If LM Studio authentication is enabled, set `LM_API_TOKEN`:

```bash
export LM_API_TOKEN="your-lm-studio-api-token"
```

If LM Studio authentication is disabled, you can leave the API key blank during interactive Nodmix setup.

For LM Studio auth setup details, see [LM Studio Authentication](https://lmstudio.ai/docs/developer/core/authentication).

4. Run onboarding and choose `LM Studio`:

```bash
nodmix onboard
```

5. In onboarding, use the `Default model` prompt to pick your LM Studio model.

You can also set or change it later:

```bash
nodmix models set lmstudio/qwen/qwen3.5-9b
```

LM Studio model keys follow a `author/model-name` format (e.g. `qwen/qwen3.5-9b`). Nodmix
model refs prepend the provider name: `lmstudio/qwen/qwen3.5-9b`. You can find the exact key for
a model by running `curl http://localhost:1234/api/v1/models` and looking at the `key` field.

## Non-interactive onboarding

Use non-interactive onboarding when you want to script setup (CI, provisioning, remote bootstrap):

```bash
nodmix onboard \
  --non-interactive \
  --accept-risk \
  --auth-choice lmstudio
```

Or specify the base URL, model, and optional API key:

```bash
nodmix onboard \
  --non-interactive \
  --accept-risk \
  --auth-choice lmstudio \
  --custom-base-url http://localhost:1234/v1 \
  --lmstudio-api-key "$LM_API_TOKEN" \
  --custom-model-id qwen/qwen3.5-9b
```

`--custom-model-id` takes the model key as returned by LM Studio (e.g. `qwen/qwen3.5-9b`), without
the `lmstudio/` provider prefix.

For authenticated LM Studio servers, pass `--lmstudio-api-key` or set `LM_API_TOKEN`.
For unauthenticated LM Studio servers, omit the key; Nodmix stores a local non-secret marker.

`--custom-api-key` remains supported for compatibility, but `--lmstudio-api-key` is preferred for LM Studio.

This writes `models.providers.lmstudio` and sets the default model to
`lmstudio/<custom-model-id>`. When you provide an API key, setup also writes the
`lmstudio:default` auth profile.

Interactive setup can prompt for an optional preferred load context length and applies it across the discovered LM Studio models it saves into config.
LM Studio plugin config trusts the configured LM Studio endpoint for model requests, including loopback, LAN, and tailnet hosts. Metadata/link-local origins still require explicit opt-in. You can opt out by setting `models.providers.lmstudio.request.allowPrivateNetwork: false`.

## Configuration

### Streaming usage compatibility

LM Studio is streaming-usage compatible. When it does not emit an OpenAI-shaped
`usage` object, Nodmix recovers token counts from llama.cpp-style
`timings.prompt_n` / `timings.predicted_n` metadata instead.

Same streaming usage behavior applies to these OpenAI-compatible local backends:

- vLLM
- SGLang
- llama.cpp
- LocalAI
- Jan
- TabbyAPI
- text-generation-webui

### Thinking compatibility

When LM Studio's `/api/v1/models` discovery reports model-specific reasoning
options, Nodmix exposes the matching OpenAI-compatible `reasoning_effort`
values in model compat metadata. Current LM Studio builds can advertise binary
UI options such as `allowed_options: ["off", "on"]` while rejecting those values
on `/v1/chat/completions`; Nodmix normalizes that binary discovery shape to
`none`, `minimal`, `low`, `medium`, `high`, and `xhigh` before sending requests.
Older saved LM Studio config that contains `off`/`on` reasoning maps is
normalized the same way when the catalog is loaded.

### Explicit configuration

```json5
{
  models: {
    providers: {
      lmstudio: {
        baseUrl: "http://localhost:1234/v1",
        apiKey: "${LM_API_TOKEN}",
        api: "openai-completions",
        models: [
          {
            id: "qwen/qwen3-coder-next",
            name: "Qwen 3 Coder Next",
            reasoning: false,
            input: ["text"],
            cost: { input: 0, output: 0, cacheRead: 0, cacheWrite: 0 },
            contextWindow: 128000,
            maxTokens: 8192,
          },
        ],
      },
    },
  },
}
```

## Troubleshooting

### LM Studio not detected

Make sure LM Studio is running. If authentication is enabled, also set `LM_API_TOKEN`:

```bash
# Start via desktop app, or headless:
lms server start --port 1234
```

Verify the API is accessible:

```bash
curl http://localhost:1234/api/v1/models
```

### Authentication errors (HTTP 401)

If setup reports HTTP 401, verify your API key:

- Check that `LM_API_TOKEN` matches the key configured in LM Studio.
- For LM Studio auth setup details, see [LM Studio Authentication](https://lmstudio.ai/docs/developer/core/authentication).
- If your server does not require authentication, leave the key blank during setup.

### Just-in-time model loading

LM Studio supports just-in-time (JIT) model loading, where models are loaded on first request. Nodmix preloads models through LM Studio's native load endpoint by default, which helps when JIT is disabled. To let LM Studio's JIT, idle TTL, and auto-evict behavior own model lifecycle, disable Nodmix's preload step:

```json5
{
  models: {
    providers: {
      lmstudio: {
        baseUrl: "http://localhost:1234/v1",
        api: "openai-completions",
        params: { preload: false },
        models: [{ id: "qwen/qwen3.5-9b" }],
      },
    },
  },
}
```

### LAN or tailnet LM Studio host

Use the LM Studio host's reachable address, keep `/v1`, and make sure LM Studio is bound beyond loopback on that machine:

```json5
{
  models: {
    providers: {
      lmstudio: {
        baseUrl: "http://gpu-box.local:1234/v1",
        apiKey: "lmstudio",
        api: "openai-completions",
        models: [{ id: "qwen/qwen3.5-9b" }],
      },
    },
  },
}
```

`lmstudio` automatically trusts its configured local/private endpoint for guarded model requests. Custom/local OpenAI-compatible provider entries also trust their exact configured `baseUrl` origin, except metadata/link-local origins; requests to different private ports or destinations still require `models.providers.<id>.request.allowPrivateNetwork: true`. Set `models.providers.<id>.request.allowPrivateNetwork: false` to opt out of exact-origin trust.

## Related

- [Model selection](/concepts/model-providers)
- [Ollama](/providers/ollama)
- [Local models](/gateway/local-models)
