---
title: "LM Studio on DGX Spark — Instructions"
canonical: "https://build.nvidia.com/spark/lm-studio/instructions.md"
---

# Step 1. Install llmster on the DGX Spark

**llmster** is LM Studio's terminal native, headless LM Studio ‘daemon’.

You can install it on servers, cloud instances, machines with no GUI, or just on your computer. This is useful for running LM Studio in headless mode on DGX Spark, then connecting to it from your laptop via the API.

**On your Spark, install llmster by running:**

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

For Windows:
```bash
irm https://lmstudio.ai/install.ps1 | iex
```

Once installed, follow the instructions in your terminal output to add `lms` to your PATH. Interact with LM Studio using the `lms` CLI or the SDK / LM Studio V1 REST API (new with [enhanced features](https://lmstudio.ai/docs/developer/rest)) / OpenAI-compatible REST API.

# Step 2. Download Required Ancillary Files

Run the following curl commands in your local terminal to download files required to complete later steps in this playbook. You may choose from Python, JavaScript, or Bash.

```bash
# JavaScript
curl -L -O https://raw.githubusercontent.com/lmstudio-ai/docs/main/_assets/nvidia-spark-playbook/js/run.js

# Python
curl -L -O https://raw.githubusercontent.com/lmstudio-ai/docs/main/_assets/nvidia-spark-playbook/py/run.py

# Bash
curl -L -O https://raw.githubusercontent.com/lmstudio-ai/docs/main/_assets/nvidia-spark-playbook/bash/run.sh
```

# Step 3. Start the LM Studio API Server

Use `lms`, LM Studio's CLI, to start the server from your terminal. Enable local network access, which allows the LM Studio API server running on your machine to be accessed by all other devices on the same local network (make sure they are trusted devices). To do this, run the following command:

```bash
lms server start --bind 0.0.0.0 --port 1234
```

To test the connectivity between your laptop and your Spark, run the following command in your local terminal

```bash
curl http://<SPARK_IP>:1234/api/v1/models 
```
where `<SPARK_IP>` is your device's IP address. You can find your Spark’s IP address by running this on your Spark:

```bash
hostname -I
```

# Step 4. (Optional) Connect with LM Link

**LM Link** lets you use your Spark’s models from your laptop (or other devices) as if they were local, over an end-to-end encrypted connection. You don’t need to be on the same local network or bind the server to `0.0.0.0`.

1. **Create a Link** — Go to [lmstudio.ai/link](https://lmstudio.ai/link) and follow **Create your Link** to set up your private LM Link network.
2. **Link both devices** — On your DGX Spark (llmster) and on your laptop, sign in and join the same Link. LM Link uses Tailscale mesh VPNs; devices communicate without opening ports to the internet.
3. **Use remote models** — On your laptop, open LM Studio (or use the local server). Remote models from your Spark appear in the model loader. Any tool that connects to `localhost:1234` — including the LM Studio SDK, Codex, Claude Code, OpenCode, and the scripts in Step 7 — can use those models without changing the endpoint.

LM Link is in **Preview** and is free for up to 2 users, 5 devices each. For details and limits, see [LM Link](https://lmstudio.ai/link).

# Step 5. Download a model to your Spark

As an example, download **NVIDIA Nemotron 3 Nano Omni** from the LM Studio catalog (`nvidia/nemotron-3-nano-omni`) so you can run it on Spark with plenty of unified memory.

```bash
lms get nvidia/nemotron-3-nano-omni
```

This download will take a while due to its large size. Verify that the model has been successfully downloaded by listing your models:

```bash
lms ls
```

# Step 6. Load the model 

Load the model on your Spark so that it is ready to respond to requests from your laptop.

```bash
lms load nvidia/nemotron-3-nano-omni
```

# Step 7. Set up a simple program that uses LM Studio SDK on the laptop

Install the LM Studio SDKs and use a simple script to send a prompt to your Spark and validate the response. To get started quickly, we provide simple scripts below for Python, JavaScript, and Bash. Download the scripts from the Overview page of this playbook and run the corresponding command from the directory containing it.

> [!NOTE]
> Within each script, replace `<SPARK_IP>` with the IP address of your DGX Spark on your local network.

## JavaScript

Pre-reqs: User has installed `npm` and `node`

```bash
npm install @lmstudio/sdk
node run.js
```

## Python

Pre-reqs: User has installed `uv`

```bash
uv run --script run.py
```

## Bash

Pre-reqs: User has installed `jq` and `curl`

```bash
bash run.sh
```

# Step 8. Next Steps

- Try downloading and serving different models from the [LM Studio model catalog](https://lmstudio.ai/models).
- Use [LM Link](https://lmstudio.ai/link) to connect more devices and use your Spark’s models from anywhere with end-to-end encryption.

# Step 9. Cleanup and rollback
Remove and uninstall LM Studio completely if needed. Note that LM Studio stores models separately from the application. Uninstalling LM Studio will not remove downloaded models unless you explicitly delete them.

If you want to remove the entire LM Studio application, quit LM Studio from the tray first, then move the application to trash.

To uninstall llmster, remove the folder `~/.lmstudio/llmster`.

To remove downloaded models, delete the contents of `~/.lmstudio/models/`.