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README.md
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@ -28,7 +28,6 @@ Each playbook includes prerequisites, step-by-step instructions, troubleshooting
- [CUDA-X Data Science](nvidia/cuda-x-data-science/)
- [DGX Dashboard](nvidia/dgx-dashboard/)
- [FLUX.1 Dreambooth LoRA Fine-tuning](nvidia/flux-finetuning/)
- [Develop and Deploy Healthcare Robots with Isaac For Healthcare](nvidia/i4h-so-arm/)
- [Install and Use Isaac Sim and Isaac Lab](nvidia/isaac/)
- [Optimized JAX](nvidia/jax/)
- [Live VLM WebUI](nvidia/live-vlm-webui/)

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@ -1,488 +0,0 @@
# Develop and Deploy Healthcare Robots with Isaac For Healthcare
> End-to-end development and deployment of healthcare robots on DGX Spark
## Table of Contents
- [Overview](#overview)
- [Part 1: Preparation](#part-1-preparation)
- [Set Up Conda Environment](#set-up-conda-environment)
- [Set Up Docker Environment](#set-up-docker-environment)
- [Set Up the Scene](#set-up-the-scene)
- [Calibrate the Robot](#calibrate-the-robot)
- [Test Teleoperation](#test-teleoperation)
- [Part 2: Synthetic Data Generation](#part-2-synthetic-data-generation)
- [Part 3: Real-World Data Collection](#part-3-real-world-data-collection)
- [Part 4: GR00T N1.5 Fine-Tuning](#part-4-gr00t-n15-fine-tuning)
- [Part 5: Deploying Trained Robotic Policy](#part-5-deploying-trained-robotic-policy)
---
## Overview
## Basic idea
Robotics and physical AI are driving the next wave of AI breakthroughs. Developing physical AI requires [3 computers](https://blogs.nvidia.com/blog/three-computers-robotics/) — 1. A simulation computer to generate synthetic data and digital twins, bridging the data gap. 2. A training computer to build the necessary foundation and world models. 3. A runtime computer to handle real-time robotic inference and intelligent interactions.
This tutorial demonstrates the development and deployment of an autonomous healthcare robot using [NVIDIA Isaac For Healthcare](https://developer.nvidia.com/blog/introducing-nvidia-isaac-for-healthcare-an-ai-powered-medical-robotics-development-platform/) on a single [DGX Spark](https://www.nvidia.com/en-us/products/workstations/dgx-spark/), consolidating the 3-computers developer workflow onto one hardware platform. The example focuses on the [SO-101 robot](https://github.com/TheRobotStudio/SO-ARM100?tab=readme-ov-file) acting as a scrub nurse—a specialized nursing professional working directly in the sterile field during surgical procedures—to perform a crucial pick-and-place task — autonomously picking up a pair of surgical scissors and placing them into a surgical tray.
## What you'll accomplish
You'll complete the full development lifecycle of an autonomous healthcare robot on DGX Spark, covering the following stages:
- **Part 1 — Preparation.** Set up the hardware, software environments, and task environment.
- **Part 2 — Generating synthetic data with Isaac Sim.** Collect synthetic pick-and-place demonstrations using teleoperation in a simulated environment.
- **Part 3 — Collecting real-world data.** Collect real-world teleoperation data with the physical SO-101 robot.
- **Part 4 — Fine-tuning the GR00T N1.5 model.** Fine-tune a pretrained GR00T N1.5 model using the collected data.
- **Part 5 — Deploying trained robotic policy.** Deploy the fine-tuned model in both simulated and real-world environments.
## What to know before starting
- Experience with Linux command line
- Basic understanding of Docker containers
- Familiarity with Python and conda environments
- Basic knowledge of robotics concepts (teleoperation, calibration)
- Familiarity with machine learning concepts (helpful but not required)
## Prerequisites
**Hardware Requirements:**
- [NVIDIA DGX Spark](https://www.nvidia.com/en-us/products/workstations/dgx-spark/) with FastOS version 1.91.+ (verify with `cat /etc/fastos-release`; upgrade if necessary following [steps here](https://docs.nvidia.com/dgx/dgx-spark/system-recovery.html#recovery-process-steps))
- [SO-101 Robot](https://github.com/TheRobotStudio/SO-ARM100?tab=readme-ov-file) with both leader & follower arms and wrist camera module (ensure mounting/fixation tools are included or acquired separately)
- USB-C splitter (needed since 4 USB connections are required and DGX Spark has only 3 available USB-C ports; use a high-quality splitter to minimize latency)
- OpenCV compatible USB web camera (for the room camera)
- Surgical tray (dimensions 24cm x 16cm x 5cm)
- Surgical scissors (length 18cm)
- Scene setup accessories — table, table cloth, and a camera stand/holder for the room camera
**Software Requirements:**
- NVIDIA DGX OS
- Miniconda: [installation guidelines](https://www.anaconda.com/docs/getting-started/miniconda/install#aws-graviton2%2Farm64)
- Docker (pre-installed on DGX OS)
## Ancillary files
All required assets can be found in the [NVIDIA Isaac-For-Healthcare-Workflows repository](https://github.com/isaac-for-healthcare/i4h-workflows).
- `workflows/so_arm_starter/` - Source code for the robotic scrub nurse example workflow
- `tools/env_setup_so_arm_starter.sh` - Environment setup script for the conda environment
- `workflows/so_arm_starter/docker/dgx.Dockerfile` - Dockerfile for the Docker environment
## Time & risk
* **Estimated time:** Approximately 2 days (GR00T N1.5 fine-tuning at 30,000 steps takes around 24 hours on DGX Spark; data collection and other setup steps require several additional hours)
* **Risk level:** Medium
* Robot calibration must remain consistent throughout the tutorial; re-calibrating after data collection or training may require restarting the entire process
* Large downloads and Docker builds may take significant time
* Leader and follower arm power cords have different voltages—do not mix them up
* **Rollback:** Conda environment and Docker image can be removed to revert software changes. Collected datasets can be deleted from `~/.cache/huggingface/lerobot/`.
## Part 1: Preparation
## Step 1. Prepare Hardware and Accessories
Required components:
* [**NVIDIA DGX Spark**](https://www.nvidia.com/en-us/products/workstations/dgx-spark/) — Verify that FastOS version is 1.91.+ with `cat /etc/fastos-release`; upgrade if necessary following [steps here](https://docs.nvidia.com/dgx/dgx-spark/system-recovery.html#recovery-process-steps).
* [**SO-101 Robot**](https://github.com/TheRobotStudio/SO-ARM100?tab=readme-ov-file) — Requires both leader & follower arms with wrist camera module. Ensure mounting/fixation tools are included or acquired separately.
* **USB-C Splitter** — Needed since 4 USB connections (2 USB-C for arms, 2 USB-A for cameras) are required and DGX Spark has only 3 available USB-C ports. Use a high-quality splitter to minimize latency.
* **OpenCV compatible USB web camera** — For the room camera.
* **Surgical Tray** — Dimensions 24cm x 16cm x 5cm.
* **Surgical Scissors** — Length 18cm.
* **Scene Setup Accessories** — Table, table cloth, and a camera stand/holder for the room camera.
## Step 2. Set Up Software Environments
Power on DGX Spark and open a terminal window.
Create a folder named `workspace` under your home directory, and clone the NVIDIA Isaac-For-Healthcare-Workflows repository `i4h-workflows` from GitHub:
```shell
mkdir ~/workspace
cd ~/workspace && git clone https://github.com/isaac-for-healthcare/i4h-workflows.git
```
The source code for several Isaac For Healthcare example workflows is in this repository, including the robotic scrub nurse example at `<path-to-i4h-workflows>/workflows/so_arm_starter`.
This tutorial requires two separate software environments on DGX Spark:
1. A conda environment for most of the tasks.
2. A docker environment for all tasks that require Isaac-GR00T.
A separate docker environment was needed primarily because of the complexity in installing certain Isaac-GR00T dependencies, like `flash_attn`, on the DGX Spark's native arm64 OS.
### Set Up Conda Environment
First, ensure Miniconda is installed on DGX Spark. If not, follow the [installation guidelines here](https://www.anaconda.com/docs/getting-started/miniconda/install#aws-graviton2%2Farm64). Then, create a new conda environment and install the necessary dependencies for this tutorial:
```shell
conda create -n so_arm_starter python=3.11 -y
conda activate so_arm_starter
cd <path-to-i4h-workflows> && bash tools/env_setup_so_arm_starter.sh
```
Installation takes about 20 minutes and, when complete, prints a success message to the terminal.
```shell
==========================================
Environment setup script finished.
==========================================
```
After installation, **deactivate and reactivate the `so_arm_starter` environment** to apply configurations:
```shell
conda deactivate
conda activate so_arm_starter
```
After reactivating the conda environment, set the following environment variable:
```shell
export PYTHONPATH=<path-to-i4h-workflows>/workflows/so_arm_starter/scripts
```
To avoid manually setting the environment variable each time you activate `so_arm_starter`, optionally add the command to `~/.bashrc`. Source the file immediately after adding it to activate it in the current session.
### Set Up Docker Environment
To set up the docker environment, build a docker image using the `dgx.Dockerfile` provided under `<path-to-i4h-workflows>/workflows/so_arm_starter/docker`:
```shell
cd <path-to-i4h-workflows>/workflows/so_arm_starter/docker
docker build -t soarm-dgx -f dgx.Dockerfile .
```
The build takes about 20 minutes, creating a docker image named `soarm-dgx`.
## Step 3. Set Up the Task Environment
### Set Up the Scene
To set up the scrub nurse pick-and-place scene:
1. **Mount Arms:** Firmly mount the follower arm on the table and the leader arm nearby for comfortable teleoperation.
2. **Set Scene:** Place the table cloth, surgical tray, and scissors on the table. Use a non-reflective, dark table cloth to minimize reflections and maintain consistent background color. Fixate the table cloth to the table to prevent movement when the follower's gripper touches it. Ensure the tray and scissors are within easy reach of the follower arm's gripper.
3. **Mount Camera:** Mount the room camera above the table for a top-down view. While other positions (like a side-view) might offer better object localization, the top-down view minimizes environmental elements, focusing only on task-relevant objects for a more robust setup.
To finally adjust the table and room camera stand for optimal wrist and room camera views, power on the robot and cameras. Connect the following to the DGX Spark:
* Leader and follower arms (2x USB-C)
* Wrist camera (1x USB-A)
* Room camera (1x USB-A or USB-C)
Due to limited DGX Spark USB-C ports, a USB-C splitter (and optional USB-A/C converters) is needed. Power the leader and follower arms, **taking care not to mix up the power cords as voltages differ.** Use a camera tool (e.g., Cheese on DGX Spark) to check live feeds and finalize positioning.
### Calibrate the Robot
First, identify the device IDs for the two robot arms and the two cameras.
Open a new terminal on DGX Spark. Activate the `so_arm_starter` conda environment:
```shell
conda activate so_arm_starter
```
Execute the following command and follow the on-screen instructions to identify the device IDs of the leader arm and the follower arm:
```shell
python -m lerobot.find_port
```
On a Linux-based system, the device IDs are usually `/dev/ttyACM0` and `/dev/ttyACM1`.
Execute the following command to identify the wrist and room camera indices:
```shell
python -m lerobot.find_cameras
```
The console should list 2 cameras with their indices (e.g., `/dev/video0` and `/dev/video2`). This command also captures and saves the current camera frames as distinct PNG images in `outputs/captured_images/`, using camera indices in the filename for easy identification and verification of feeds.
Set access permissions for the robot arms before calibration by running:
```shell
sudo chmod 666 /dev/ttyACM0
sudo chmod 666 /dev/ttyACM1
```
Adjust device IDs as needed. **Execute these commands every time the robot disconnects from and reconnects to DGX Spark.**
Run the following commands in the terminal to calibrate the leader arm and the follower arm:
```shell
## Leader arm:
python -m lerobot.calibrate --teleop.type=so101_leader --teleop.port=/dev/ttyACM0 --teleop.id=so101_leader
## Follower arm:
python -m lerobot.calibrate --robot.type=so101_follower --robot.port=/dev/ttyACM1 --robot.id=so101_follower
```
Adjust device IDs and customize `--teleop.id` and `--robot.id` to set different device names if needed. Then, follow on-screen instructions and refer to the [video here](https://huggingface.co/docs/lerobot/so101#calibration-video) for proper calibration.
> [!WARNING]
> Maintain *one* single follower arm calibration for this tutorial. Re-calibrating after collecting data or training the GR00T model risks needing to restart everything, as subsequent steps rely on the initial calibration.
### Test Teleoperation
To complete the preparation, teleoperate the follower arm using the leader arm.
Run the following command to teleoperate without camera feeds:
```shell
python -m lerobot.teleoperate \
--robot.type=so101_follower \
--robot.port=/dev/ttyACM1 \
--robot.id=so101_follower \
--teleop.type=so101_leader \
--teleop.port=/dev/ttyACM0 \
--teleop.id=so101_leader
```
Adjust the `--robot.port`, `--teleop.port`, `--robot.id` and `--teleop.id` arguments if needed.
Run the following command to teleoperate with camera feeds:
```shell
python -m lerobot.teleoperate \
--robot.type=so101_follower \
--robot.port=/dev/ttyACM1 \
--robot.id=so101_follower \
--robot.cameras="{wrist: {type: opencv, index_or_path: 2, width: 640, height: 480, fps: 30}, room: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
--teleop.type=so101_leader \
--teleop.port=/dev/ttyACM0 \
--teleop.id=so101_leader \
--display_data=true
```
Adjust device IDs, names and camera indices if needed.
During teleoperation with camera feeds, the [Rerun viewer](https://rerun.io/) UI appears, showing real-time views from both cameras and the robot's motor action data.
## Part 2: Synthetic Data Generation
## Step 1. Launch Isaac Sim for Data Collection
Ensure the leader arm is powered on and connected to DGX Spark. Open a new terminal on DGX Spark, activate the `so_arm_starter` conda environment and set the `PYTHONPATH`:
```shell
conda activate so_arm_starter
export PYTHONPATH=<path-to-i4h-workflows>/workflows/so_arm_starter/scripts
```
Then, run the following command in the terminal:
```shell
python -m simulation.environments.teleoperation_record \
--port=/dev/ttyACM0 \
--enable_cameras \
--record \
--dataset_path=./data-collection-sim/dataset.hdf5
```
If needed, adjust the leader arm device ID and modify the `--dataset_path` argument to save data elsewhere.
The command launches [Isaac Sim](https://developer.nvidia.com/isaac/sim), loading a scene with a follower arm, table, surgical scissors, and a tray. The initial load may take about 2 minutes; if Isaac Sim seems unresponsive, do not force quit—wait for it to load fully.
To change the simulated follower arm's color to match your physical robot, go to the `Stage` panel (right side of Isaac Sim) → `World` → `envs` → `env_0` → `robot` → `Looks` → `material_a_3d_printed`, then under the `Property` tab, adjust the `Albedo Color`.
The first command run requires leader arm calibration, even if previously done, due to a different program-specific calibration file. Your existing calibration remains unchanged.
## Step 2. Collect Synthetic Pick-and-Place Demonstrations
To teleoperate the robot in Isaac Sim and collect synthetic pick-and-place demonstrations:
* Press "B" to begin teleoperation; the robot moves to the initial position.
* Use the physical leader arm to control the virtual follower arm for the pick-and-place task.
* Press "N" to save a successful episode.
* Press "R" to restart without saving.
* Scissors position and angle are slightly randomized per new episode.
* Press Ctrl + C to quit.
Use these shortcuts for Isaac Sim viewport navigation:
* "F" key after clicking the robot to auto-focus.
* Middle mouse wheel to zoom.
* "ALT" + left mouse drag to change the view angle.
* Middle mouse wheel click + drag to move in the viewport.
Collecting around 70 synthetic episodes is sufficient for this tutorial.
## Step 3. Convert Data to LeRobot Format
After collecting the synthetic data, convert them to the Hugging Face [LeRobot](https://github.com/huggingface/lerobot) dataset format for fine-tuning the Isaac GR00T model:
```shell
python -m training.hdf5_to_lerobot \
--repo_id=spark/scrub-nurse-sim \
--hdf5_path=./data-collection-sim/dataset.hdf5 \
--task_description="Grip the scissors and put them into the tray."
```
Modify `--repo_id` and `--task_description` as needed, but ensure a meaningful task description. The resulting dataset, containing motor actions, wrist camera, and room camera recordings, is stored under `/home/$USER/.cache/huggingface/lerobot/<repo_id>`.
## Part 3: Real-World Data Collection
## Step 1. Set Up for Real-World Data Collection
Ensure the leader arm, follower arm, wrist camera, and room camera are connected to DGX Spark. On DGX Spark, open a new terminal, activate the `so_arm_starter` conda environment:
```shell
conda activate so_arm_starter
```
## Step 2. Collect Real-World Data Episodes
Run the following command to collect real-world data episodes as LeRobot dataset:
```shell
python -m lerobot.record \
--robot.type=so101_follower \
--robot.port=/dev/ttyACM1 \
--robot.cameras="{wrist: {type: opencv, index_or_path: 2, width: 640, height: 480, fps: 30}, room: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \
--robot.id=so101_follower \
--teleop.type=so101_leader \
--teleop.port=/dev/ttyACM0 \
--teleop.id=so101_leader \
--display_data=true \
--dataset.repo_id="spark/scrub-nurse-real" \
--dataset.num_episodes=20 \
--dataset.single_task="Grip the scissors and put them into the tray." \
--dataset.push_to_hub=false
```
Modify robot device IDs, names and camera indices to match yours. Ensure `--dataset.single_task` matches the task description for synthetic data collection. You can change `--dataset.repo_id` to alter the LeRobot dataset name. The dataset will be saved under `/home/$USER/.cache/huggingface/lerobot/<repo_id>`.
The command initiates the Rerun viewer and teleoperation for both arms. Follow these steps for pick-and-place demonstration recording:
* The recording starts immediately upon command execution for the current episode; be prepared or you'll need to re-record.
* Each episode's recording has three sequential states:
1. **Demonstration recording** (60s) — Record the task.
2. **Scene Reset** (60s) — Perform randomization, robot/object resets. Rerun displays signals, but no recording occurs.
3. **Data Saving** (approx. 5s) — Saves recording to a LeRobot dataset. Rerun temporarily freezes; no recording occurs.
* Right Arrow (→) — skips to the next state. Cannot skip State 3 (saving stage); pressing it then could corrupt the episode.
* Left Arrow (←) (during State 1) — cancels the current recording, giving 60 seconds to reset the scene before recording restarts. Use this if you mess up.
* **ESC** — stops recording and saves all currently recorded content. Use after a completed successful episode to avoid including unwanted "garbage" data.
* Collecting multiple small, separate LeRobot datasets might be easier, and they can be combined for GR00T training later.
## Step 3. Prepare Datasets for Training
After creating the datasets, copy the `modality.json` file generated during synthetic data creation (e.g., `/home/$USER/.cache/huggingface/lerobot/spark/scrub-nurse-sim/meta/modality.json`) to each dataset's `meta` folder. This file is essential for GR00T model training.
Collecting 20 real-world episodes should be sufficient for this tutorial.
## Part 4: GR00T N1.5 Fine-Tuning
## Step 1. Launch Docker Container
Run the following command on DGX Spark to start a docker container:
```shell
docker run -it --gpus all --privileged --rm \
--ipc=host \
--network=host \
--ulimit memlock=-1 \
--ulimit stack=67108864 \
--entrypoint=bash \
-e "NVIDIA_VISIBLE_DEVICES=all" \
-e "PYTHONPATH=<path-to-i4h-workflows>/workflows/so_arm_starter/scripts"\
-v /dev:/dev \
-v /home/"$USER"/.cache/huggingface/lerobot:/root/.cache/huggingface/lerobot \
-v $(pwd):/workspace \
-w /workspace \
soarm-dgx
```
We mount `/home/"$USER"/.cache/huggingface/lerobot` to the container so previous calibration files and datasets are accessible.
## Step 2. Download Pretrained Model
Download our pretrained GR00T N1.5 model [here](https://github.com/isaac-for-healthcare/i4h-workflows/blob/main/workflows/so_arm_starter/README.md#-running-workflows). The model was trained on 70 simulated and 5 real episodes. This model will likely require fine-tuning due to variations in your robot hardware, calibration, and task setup.
## Step 3. Run GR00T N1.5 Fine-Tuning
Run the following command to run GR00T N1.5 fine-tuning:
```shell
PYTHONWARNINGS="ignore::UserWarning" python -m training.gr00t_n1_5.train \
--dataset_path <dataset-1> <dataset-2> ... \
--output_dir /workspace/training-output/ \
--data_config so100_dualcam \
--base-model-path <pretrained-gr00t-model> \
--max-steps 30000 \
--save-steps 2000
```
Change `--base-model-path` to the pretrained model path. Experiment with `--max-steps` and `--save-steps`; we found 30,000 steps typically sufficient for convergence. On DGX Spark, 30,000 steps should take around 24 hours.
You can use Tensorboard to monitor the training progress.
## Part 5: Deploying Trained Robotic Policy
## Step 1. Convert Model to TensorRT Format
To get the optimal inference performance, let's convert the fine-tuned GR00T N1.5 model to [TensorRT](https://developer.nvidia.com/tensorrt) format.
Open a terminal window and create the same docker container as in Part 4. Then, run the following commands:
```shell
python -m policy_runner.gr00tn1_5.trt.export_onnx --ckpt_path <fine-tuned-gr00t-model-path>
bash <path-to-i4h-workflows>/workflows/so_arm_starter/scripts/policy_runner/gr00tn1_5/trt/build_engine.sh
```
This generates a `gr00t_engine` folder that contains the converted TensorRT model. Avoid running heavy compute or graphics tasks on DGX Spark during conversion.
## Step 2. Deploy in Isaac Sim
To deploy the trained policy model in Isaac Sim, an [RTI DDS](https://www.rti.com/products/dds-standard) license file is required for communication of different modules. Get a professional or evaluation license from [here](https://www.rti.com/get-connext).
Open a new terminal window and create the same docker container as in Part 4. First, set the `RTI_LICENSE_FILE` environment variable:
```shell
export RTI_LICENSE_FILE=<path-to-rti-license-file>
```
Then, run the following command:
```shell
python -m policy_runner.run_policy \
--ckpt_path=<fine-tuned-gr00t-model-path> \
--task_description="Grip the scissors and put them into the tray." \
--trt \
--trt_engine_path=<fine-tuned-gr00t-tensorrt-model>
```
This loads the GR00T model for inference in the background.
Open another terminal window. Activate the `so_arm_starter` conda environment and set `PYTHONPATH` and `RTI_LICENSE_FILE`:
```shell
conda activate so_arm_starter
export PYTHONPATH=<path-to-i4h-workflows>/workflows/so_arm_starter/scripts
export RTI_LICENSE_FILE=<path-to-rti-license-file>
```
Then, run the following command in the terminal:
```shell
python -m simulation.environments.sim_with_dds --enable_cameras
```
Isaac Sim will open up and load the pick-and-place scene, then the simulated robot will execute the task autonomously, driven by the GR00T N1.5 policy model.
## Step 3. Deploy in Real World
Ensure the follower arm, wrist camera, and room camera are connected to DGX Spark.
Launch the same docker container as in Part 4. Find and modify the configuration file under `<path-to-i4h-workflows>/workflows/so_arm_starter/scripts/holoscan_apps/soarm_robot_config.yaml` to update the follower arm's device ID, name, camera indices, and the fine-tuned GR00T model path. Then, run the following command:
```shell
python -m holoscan_apps.gr00t_inference_app \
--config <path-to-i4h-workflows>/workflows/so_arm_starter/scripts/holoscan_apps/soarm_robot_config.yaml
```
This command launches an efficient GR00T N1.5 inference application using [NVIDIA Holoscan SDK](https://github.com/nvidia-holoscan/holoscan-sdk). The follower arm will execute the task autonomously shortly after.
## Conclusion
This tutorial demonstrated the end-to-end workflow of developing and deploying an autonomous healthcare robot on a single **NVIDIA DGX Spark**. Leveraging **NVIDIA Isaac For Healthcare**, we consolidated the 3-computers workflow of synthetic data generation, GR00T N1.5 training, and robotic policy deployment onto one powerful hardware platform. This workflow highlights the efficiency of the DGX Spark for accelerating the physical AI development pipeline, making the creation and deployment of intelligent healthcare robots more streamlined and accessible.

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@ -1,6 +1,6 @@
# Run models with llama.cpp on DGX Spark
> Build llama.cpp with CUDA and serve models via an OpenAI-compatible API (Nemotron 3 Nano Omni as example)
> Build llama.cpp with CUDA and serve models via an OpenAI-compatible API (Gemma 4 31B IT as example)
## Table of Contents
@ -17,15 +17,15 @@
[llama.cpp](https://github.com/ggml-org/llama.cpp) is a lightweight C/C++ inference stack for large language models. You build it with CUDA so tensor work runs on the DGX Spark GB10 GPU, then load GGUF weights and expose chat through `llama-server`’s OpenAI-compatible HTTP API.
This playbook walks through that stack end to end using **Nemotron 3 Nano Omni** as the hands-on example: an NVIDIA MoE family that runs well from quantized GGUF on Spark. Checkpoint choices and paths for all supported models are summarized in the matrix below; commands are in the instructions.
This playbook walks through that stack end to end. As the model example, it uses **Gemma 4 31B IT** - a frontier reasoning model built by Google DeepMind that llama.cpp supports, with strengths in coding, agentic workflows, and fine-tuning. The instructions download its **F16** GGUF from Hugging Face. The same build and server steps apply to other GGUFs (including other sizes in the support matrix below).
## What you'll accomplish
You will build llama.cpp with CUDA for GB10, download a **Nemotron 3 Nano Omni** example checkpoint, and run **`llama-server`** with GPU offload. You get:
You will build llama.cpp with CUDA for GB10, download a Gemma 4 31B IT model checkpoint, and run **`llama-server`** with GPU offload. You get:
- Local inference through llama.cpp (no separate Python inference framework required)
- An OpenAI-compatible `/v1/chat/completions` endpoint for tools and apps
- A concrete validation that the **Nemotron 3 Nano Omni** example runs on this stack on DGX Spark
- A concrete validation that **Gemma 4 31B IT** runs on this stack on DGX Spark
## What to know before starting
@ -39,8 +39,8 @@ You will build llama.cpp with CUDA for GB10, download a **Nemotron 3 Nano Omni**
**Hardware requirements**
- NVIDIA DGX Spark with GB10 GPU
- Sufficient unified memory for the example **Q8_0** checkpoint (weights on the order of **~35GB**, plus KV cache and runtime overhead—scale up if you pick a larger quant or longer context)
- At least **~40GB** free disk for the example download plus build artifacts (more if you keep multiple GGUFs)
- Sufficient unified memory for the F16 checkpoint (on the order of **~62GB** for weights alone; more when KV cache and runtime overhead are included)
- At least **~70GB** free disk for the F16 download plus build artifacts (use a smaller quant from the same repo if you need less disk and VRAM)
**Software requirements**
@ -50,15 +50,12 @@ You will build llama.cpp with CUDA for GB10, download a **Nemotron 3 Nano Omni**
- CUDA Toolkit: `nvcc --version`
- Network access to GitHub and Hugging Face
## Model support matrix
## Model Support Matrix
The following models are supported with llama.cpp on Spark. The instructions use the **Nemotron 3 Nano Omni** example row by default.
The following models are supported with llama.cpp on Spark. All listed models are available and ready to use:
| Model | Support Status | HF Handle |
|-------|----------------|-----------|
| **Nemotron 3 Nano Omni** (example walkthrough) | ✅ | `ggml-org/NVIDIA-Nemotron-3-Nano-Omni` |
| **Qwen3.6-35B-A3B** | ✅ | `unsloth/Qwen3.6-35B-A3B-GGUF` |
| **Qwen3.6-27B** | ✅ | `unsloth/Qwen3.6-27B-GGUF` |
| **Gemma 4 31B IT** | ✅ | `ggml-org/gemma-4-31B-it-GGUF` |
| **Gemma 4 26B A4B IT** | ✅ | `ggml-org/gemma-4-26B-A4B-it-GGUF` |
| **Gemma 4 E4B IT** | ✅ | `ggml-org/gemma-4-E4B-it-GGUF` |
@ -67,17 +64,17 @@ The following models are supported with llama.cpp on Spark. The instructions use
## Time & risk
* **Estimated time:** About 30 minutes, plus downloading the example GGUF (~35GB order of magnitude for the default quant)
* **Estimated time:** About 30 minutes, plus downloading the ~62GB example
* **Risk level:** Low — build is local to your clone; no system-wide installs required for the steps below
* **Rollback:** Remove the `llama.cpp` clone and the model directory under `~/models/` to reclaim disk space
* **Last updated:** 04/28/2026
* Walkthrough now uses Nemotron Omni; other model rows stay available
* **Last updated:** 04/02/2026
* First Publication
## Instructions
## Step 1. Verify prerequisites
The **example** checkpoint is **`nemotron-3-nano-omni-ga_v1.0-Q8_0.gguf`** from Hugging Face repo **`ggml-org/NVIDIA-Nemotron-3-Nano-Omni`** (full handle: `ggml-org/NVIDIA-Nemotron-3-Nano-Omni/nemotron-3-nano-omni-ga_v1.0-Q8_0.gguf`). Other supported GGUFs—including Qwen3.6, Gemma, and alternate Nemotron Omni builds—use the same build and server steps; change `hf download` and `--model` paths (see the [overview model matrix](overview.md)).
This walkthrough uses **Gemma 4 31B IT** (`gemma-4-31B-it-f16.gguf`) as the example checkpoint. You can substitute another GGUF from [`ggml-org/gemma-4-31B-it-GGUF`](https://huggingface.co/ggml-org/gemma-4-31B-it-GGUF) (for example `Q4_K_M` or `Q8_0`) by changing the `hf download` filename and `--model` path in later steps.
Ensure the required tools are installed:
@ -124,25 +121,25 @@ make -j8
The build usually takes on the order of 5–10 minutes. When it finishes, binaries such as `llama-server` appear under `build/bin/`.
## Step 4. Download example Nemotron 3 Nano Omni GGUF
## Step 4. Download Gemma 4 31B IT GGUF (supported model example)
llama.cpp loads models in **GGUF** format. This playbook uses the **Q8_0** checkpoint from `ggml-org/NVIDIA-Nemotron-3-Nano-Omni`, which balances quality and memory on DGX Spark GB10 unified memory.
llama.cpp loads models in **GGUF** format. **gemma-4-31B-it** is available in GGUF from Hugging Face; this playbook uses a F16 variant that balances quality and memory on GB10-class hardware.
```bash
hf download ggml-org/NVIDIA-Nemotron-3-Nano-Omni \
nemotron-3-nano-omni-ga_v1.0-Q8_0.gguf \
--local-dir ~/models/NVIDIA-Nemotron-3-Nano-Omni
hf download ggml-org/gemma-4-31B-it-GGUF \
gemma-4-31B-it-f16.gguf \
--local-dir ~/models/gemma-4-31B-it-GGUF
```
The file is on the order of **~35GB** (exact size may vary). The download can be resumed if interrupted.
The F16 file is large (**~62GB**). The download can be resumed if interrupted.
## Step 5. Start llama-server with Nemotron 3 Nano Omni
## Step 5. Start llama-server with Gemma 4 31B IT
From your `llama.cpp/build` directory, launch the OpenAI-compatible server with GPU offload:
```bash
./bin/llama-server \
--model ~/models/NVIDIA-Nemotron-3-Nano-Omni/nemotron-3-nano-omni-ga_v1.0-Q8_0.gguf \
--model ~/models/gemma-4-31B-it-GGUF/gemma-4-31B-it-f16.gguf \
--host 0.0.0.0 \
--port 30000 \
--n-gpu-layers 99 \
@ -165,7 +162,7 @@ llama_new_context_with_model: n_ctx = 8192
main: server is listening on 0.0.0.0:30000
```
**Keep this terminal open** while testing. Large GGUFs can take a minute or more to load; until you see `server is listening`, nothing accepts connections on port 30000 (see Troubleshooting if `curl` reports connection refused).
**Keep this terminal open** while testing. Large GGUFs can take several minutes to load; until you see `server is listening`, nothing accepts connections on port 30000 (see Troubleshooting if `curl` reports connection refused).
## Step 6. Test the API
@ -175,7 +172,7 @@ Use a **second terminal on the same machine** that runs `llama-server` (for exam
curl -X POST http://127.0.0.1:30000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "nemotron",
"model": "gemma4",
"messages": [{"role": "user", "content": "New York is a great city because..."}],
"max_tokens": 100
}'
@ -198,7 +195,7 @@ Example shape of the response (fields vary by llama.cpp version; `message` may i
}
],
"created": 1765916539,
"model": "nemotron-3-nano-omni-ga_v1.0-Q8_0.gguf",
"model": "gemma-4-31B-it-f16.gguf",
"object": "chat.completion",
"usage": {
"completion_tokens": 100,
@ -212,15 +209,15 @@ Example shape of the response (fields vary by llama.cpp version; `message` may i
}
```
## Step 7. Longer completion (with Nemotron 3 Nano Omni)
## Step 7. Longer completion (with example model)
Try a slightly longer prompt to confirm stable generation with **Nemotron 3 Nano Omni**:
Try a slightly longer prompt to confirm stable generation with **Gemma 4 31B IT**:
```bash
curl -X POST http://127.0.0.1:30000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "nemotron",
"model": "gemma4",
"messages": [{"role": "user", "content": "Solve this step by step: If a train travels 120 miles in 2 hours, what is its average speed?"}],
"max_tokens": 500
}'
@ -234,7 +231,7 @@ To remove this tutorial’s artifacts:
```bash
rm -rf ~/llama.cpp
rm -rf ~/models/NVIDIA-Nemotron-3-Nano-Omni
rm -rf ~/models/gemma-4-31B-it-GGUF
```
Deactivate the Python venv if you no longer need `hf`:

37
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View File

@ -27,7 +27,7 @@ This playbook shows you how to deploy LM Studio on an NVIDIA DGX Spark device to
## What you'll accomplish
You'll deploy LM Studio on an NVIDIA DGX Spark device to run **Nemotron 3 Nano Omni** (`nvidia/nemotron-3-nano-omni`), and use the model from your laptop. More specifically, you will:
You'll deploy LM Studio on an NVIDIA DGX Spark device to run gpt-oss 120B, and use the model from your laptop. More specifically, you will:
- Install **llmster**, a totally headless, terminal native LM Studio on the Spark
- Run LLM inference locally on DGX Spark via API
@ -54,15 +54,6 @@ You'll deploy LM Studio on an NVIDIA DGX Spark device to run **Nemotron 3 Nano O
- Laptop and DGX Spark must be on the same local network
- Network access to download packages and models
## Model support matrix
To explore all supported models in LM Studio, check out [LM Studio model catalog](https://lmstudio.ai/models) page.
| Model | Support Status | Model Path |
|-------|----------------|-----------|
| **Nemotron 3 Nano Omni** | ✅ | `nvidia/nemotron-3-nano-omni` |
| **Qwen3.6-35B-A3B** | ✅ | `qwen/qwen3.6-35b-a3b` |
| **GPT-OSS-120B** | ✅ | `openai/gpt-oss-120b` |
## LM Link (optional)
[LM Link](https://lmstudio.ai/link) lets you **use your local models remotely**. You link machines (e.g. your DGX Spark and your laptop), then load models on the Spark and use them from the laptop as if they were local.
@ -75,7 +66,7 @@ If you use LM Link, you can skip binding the server to `0.0.0.0` and using the S
## Ancillary files
All required assets can be found below. These sample scripts can be used in Step 7 of Instructions.
All required assets can be found below. These sample scripts can be used in Step 6 of Instructions.
- [run.js](https://github.com/lmstudio-ai/docs/blob/main/_assets/nvidia-spark-playbook/js/run.js) - JavaScript script for sending a test prompt to Spark
- [run.py](https://github.com/lmstudio-ai/docs/blob/main/_assets/nvidia-spark-playbook/py/run.py) - Python script for sending a test prompt to Spark
@ -89,8 +80,8 @@ All required assets can be found below. These sample scripts can be used in Step
* **Rollback:**
* Downloaded models can be removed manually from the models directory.
* Uninstall LM Studio or llmster
* **Last Updated:** 04/28/2026
* Introduce Nemotron Omni as example
* **Last Updated:** 03/12/2026
* Add instructions for LM Link features
## Instructions
@ -147,22 +138,22 @@ where `<SPARK_IP>` is your device's IP address. You can find your Spark’s IP a
hostname -I
```
## Step 4. (Optional) Connect with LM Link
## Step 3b. (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.
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 6 — 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
## Step 4. 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.
As an example, let's download and run gpt-oss 120B, one of the best open source models from OpenAI. This model is too large for many laptops due to memory limitations, which makes this a fantastic use case for the Spark.
```bash
lms get nvidia/nemotron-3-nano-omni
lms get openai/gpt-oss-120b
```
This download will take a while due to its large size. Verify that the model has been successfully downloaded by listing your models:
@ -171,15 +162,15 @@ This download will take a while due to its large size. Verify that the model has
lms ls
```
## Step 6. Load the model
## Step 5. 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
lms load openai/gpt-oss-120b
```
## Step 7. Set up a simple program that uses LM Studio SDK on the laptop
## Step 6. 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.
@ -211,12 +202,12 @@ Pre-reqs: User has installed `jq` and `curl`
bash run.sh
```
## Step 8. Next Steps
## Step 7. 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
## Step 8. 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.

116
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View File

@ -26,7 +26,7 @@
- [Step 7. Interactive TUI](#step-7-interactive-tui)
- [Step 8. Exit the sandbox and access the Web UI](#step-8-exit-the-sandbox-and-access-the-web-ui)
- [Step 9. Create a Telegram bot](#step-9-create-a-telegram-bot)
- [Step 10. Install cloudflared and start the Telegram bridge](#step-10-install-cloudflared-and-start-the-telegram-bridge)
- [Step 10. Configure and start the Telegram bridge](#step-10-configure-and-start-the-telegram-bridge)
- [Step 11. Stop services](#step-11-stop-services)
- [Step 12. Uninstall NemoClaw](#step-12-uninstall-nemoclaw)
- [Troubleshooting](#troubleshooting)
@ -97,7 +97,8 @@ By participating in this demo, you acknowledge that you are solely responsible f
**Hardware and access:**
- A DGX Spark (GB10) with keyboard and monitor, or SSH access
- A **Telegram bot token** from [@BotFather](https://t.me/BotFather) (create one with `/newbot`) -- only needed if you want the Telegram bot. Have it ready *before* running the installer; the onboard wizard prompts for it.
- An **NVIDIA API key** from [build.nvidia.com](https://build.nvidia.com/settings/api-keys) (needed for the Telegram bridge)
- A **Telegram bot token** from [@BotFather](https://t.me/BotFather) (create one with `/newbot`)
**Software:**
@ -117,7 +118,8 @@ Expected: Ubuntu 24.04, NVIDIA GB10 GPU, Docker 28.x+.
| Item | Where to get it |
|------|----------------|
| Telegram bot token (optional) | [@BotFather](https://t.me/BotFather) on Telegram -- create with `/newbot`. Required only for the Telegram bot; have it ready before running the installer. |
| NVIDIA API key | [build.nvidia.com/settings/api-keys](https://build.nvidia.com/settings/api-keys) |
| Telegram bot token | [@BotFather](https://t.me/BotFather) on Telegram -- create with `/newbot` |
### Ancillary files
@ -127,8 +129,8 @@ All required assets are handled by the NemoClaw installer. No manual cloning is
- **Estimated time:** 20--30 minutes (with Ollama and model already downloaded). First-time model download adds ~15--30 minutes depending on network speed.
- **Risk level:** Medium -- you are running an AI agent in a sandbox; risks are reduced by isolation but not eliminated. Use a clean environment and do not connect sensitive data or production accounts.
- **Last Updated:** 04/28/2026
* Updated for NemoClaw v0.0.22+: revised Telegram setup, renamed tunnel commands, refreshed uninstall instructions.
- **Last Updated:** 03/31/2026
* First Publication
## Instructions
@ -247,13 +249,9 @@ curl -fsSL https://www.nvidia.com/nemoclaw.sh | bash
The onboard wizard walks you through setup:
1. **Sandbox name** -- Pick a name (e.g. `my-assistant`). Names must be lowercase alphanumeric with hyphens only.
2. **Inference provider** -- Select **Local Ollama**.
3. **Model** -- Select **nemotron-3-super:120b**.
4. **Messaging channels** -- If you want a Telegram bot, select `telegram` here and paste your bot token when prompted. Create the bot first via [@BotFather](https://t.me/BotFather) in Telegram (see Step 9). If you skip this, you can re-run the installer later to recreate the sandbox with Telegram enabled.
5. **Policy presets** -- Accept the suggested presets when prompted (hit **Y**).
> [!IMPORTANT]
> Telegram must be configured at this step. The channel plugin and bot token are wired into the sandbox container during onboarding — they cannot be added to an existing sandbox by exporting environment variables on the host.
2. **Inference provider** -- Select **Local Ollama** (option 7).
3. **Model** -- Select **nemotron-3-super:120b** (option 1).
4. **Policy presets** -- Accept the suggested presets when prompted (hit **Y**).
When complete you will see output like:
@ -299,7 +297,7 @@ Expected: JSON listing `nemotron-3-super:120b`.
Still inside the sandbox, send a test message:
```bash
openclaw agent --agent main -m "hello" --session-id test
openclaw agent --agent main --local -m "hello" --session-id test
```
The agent will respond using Nemotron 3 Super. First responses may take 30--90 seconds for a 120B parameter model running locally.
@ -328,7 +326,7 @@ exit
http://127.0.0.1:18789/#token=<long-token-here>
```
**If accessing the Web UI from a remote machine**, you need to set up an SSH tunnel. The NemoClaw onboard wizard already created the port 18789 forward on the Spark, so you only need to tunnel from your remote machine.
**If accessing the Web UI from a remote machine**, you need to set up port forwarding.
First, find your Spark's IP address. On the Spark, run:
@ -338,7 +336,13 @@ hostname -I | awk '{print $1}'
This prints the primary IP address (e.g. `192.168.1.42`). You can also find it in **Settings > Wi-Fi** or **Settings > Network** on the Spark's desktop, or check your router's connected-devices list.
From your remote machine, create an SSH tunnel to the Spark (replace `<your-spark-ip>` with the IP address from above):
Start the port forward on the Spark host:
```bash
openshell forward start 18789 my-assistant --background
```
Then from your remote machine, create an SSH tunnel to the Spark (replace `<your-spark-ip>` with the IP address from above):
```bash
ssh -L 18789:127.0.0.1:18789 <your-user>@<your-spark-ip>
@ -353,67 +357,62 @@ http://127.0.0.1:18789/#token=<long-token-here>
> [!IMPORTANT]
> Use `127.0.0.1`, not `localhost` -- the gateway origin check requires an exact match.
> [!NOTE]
> If the Web UI fails to load and the port forward may be stale, reset it on the Spark host:
> ```bash
> openshell forward stop 18789 my-assistant || true
> openshell forward start 18789 my-assistant --background
> ```
---
## Phase 3: Telegram Bot
> [!IMPORTANT]
> Telegram must be enabled in the **NemoClaw onboard wizard** (Step 4 → Messaging channels). The channel plugin and bot token are wired into the sandbox container at sandbox creation time — `policy-add` only opens network egress and is not enough on its own. If you skipped Telegram during onboard, re-run the installer to recreate the sandbox with Telegram enabled.
> [!NOTE]
> If you already configured Telegram during the NemoClaw onboarding wizard (step 5/8), you can skip this phase. These steps cover adding Telegram after the initial setup.
### Step 9. Create a Telegram bot
Do this **before** running the NemoClaw installer in Step 4 so you have your bot token ready when the wizard prompts for it.
Open Telegram, find [@BotFather](https://t.me/BotFather), send `/newbot`, and follow the prompts. Copy the bot token it gives you.
Open Telegram, find [@BotFather](https://t.me/BotFather), send `/newbot`, and follow the prompts. Copy the bot token it gives you and paste it into the wizard when you reach the **Messaging channels** step.
### Step 10. Install cloudflared and start the Telegram bridge
The Telegram bridge needs a public webhook URL so Telegram can deliver messages to your bot. NemoClaw uses [cloudflared](https://developers.cloudflare.com/cloudflare-one/connections/connect-networks/) to create a free `trycloudflare.com` tunnel.
### Step 10. Configure and start the Telegram bridge
Make sure you are on the **host** (not inside the sandbox). If you are inside the sandbox, run `exit` first.
Install cloudflared (DGX Spark is arm64):
Set the required environment variables. Replace the placeholders with your actual values. `SANDBOX_NAME` must match the sandbox name you chose during the onboard wizard:
```bash
curl -L --output cloudflared.deb \
https://github.com/cloudflare/cloudflared/releases/latest/download/cloudflared-linux-arm64.deb
sudo dpkg -i cloudflared.deb
export TELEGRAM_BOT_TOKEN=<your-bot-token>
export SANDBOX_NAME=my-assistant
export NVIDIA_API_KEY=<your-nvidia-api-key>
```
Start the tunnel:
Add the Telegram network policy to the sandbox:
```bash
nemoclaw tunnel start
nemoclaw my-assistant policy-add
```
Verify the public URL is live:
When prompted, select `telegram` and hit **Y** to confirm.
Start the Telegram bridge.
```bash
export TELEGRAM_BOT_TOKEN=<your-bot-token>
nemoclaw start
```
The Telegram bridge starts only when the `TELEGRAM_BOT_TOKEN` environment variable is set. Verify the services are running:
```bash
nemoclaw status
```
You should see `● cloudflared` with a `trycloudflare.com` public URL (e.g. `https://assembled-peer-persian-kitty.trycloudflare.com`).
Open Telegram, find your bot, and send it a message. The bot forwards it to the agent and replies.
> [!NOTE]
> If `nemoclaw tunnel start` prints `cloudflared not found — no public URL`, the cloudflared install above did not complete successfully. Re-run the install, then restart the tunnel:
> ```bash
> nemoclaw tunnel stop && nemoclaw tunnel start
> ```
> [!NOTE]
> The first response may take 30--90 seconds for a 120B parameter model running locally.
> [!NOTE]
> If sending a message returns `Error: Channel is unavailable: telegram`, the channel was not enabled during onboard. Re-run the installer to recreate the sandbox with Telegram selected at the **Messaging channels** step.
> If the bridge does not appear in `nemoclaw status`, make sure `TELEGRAM_BOT_TOKEN` is exported in the same shell session where you run `nemoclaw start`. You can also try stopping and restarting:
> ```bash
> nemoclaw stop
> export TELEGRAM_BOT_TOKEN=<your-bot-token>
> nemoclaw start
> ```
> [!NOTE]
> For details on restricting which Telegram chats can interact with the agent, see the [NemoClaw Telegram bridge documentation](https://docs.nvidia.com/nemoclaw/latest/deployment/set-up-telegram-bridge.html).
@ -424,10 +423,10 @@ Open Telegram, find your bot, and send it a message. The bot forwards it to the
### Step 11. Stop services
Stop the cloudflared tunnel:
Stop any running auxiliary services (Telegram bridge, cloudflared tunnel):
```bash
nemoclaw tunnel stop
nemoclaw stop
```
Stop the port forward:
@ -439,13 +438,14 @@ openshell forward stop 18789 # stop the dashboard forward
### Step 12. Uninstall NemoClaw
Run the uninstaller via curl (matches the [NemoClaw README](https://github.com/NVIDIA/NemoClaw)). It removes all sandboxes, the OpenShell gateway, Docker containers/images/volumes, the CLI, and all state files. Docker, Node.js, npm, and Ollama are preserved.
Run the uninstaller from the cloned source directory. It removes all sandboxes, the OpenShell gateway, Docker containers/images/volumes, the CLI, and all state files. Docker, Node.js, npm, and Ollama are preserved.
```bash
curl -fsSL https://raw.githubusercontent.com/NVIDIA/NemoClaw/refs/heads/main/uninstall.sh | bash
cd ~/.nemoclaw/source
./uninstall.sh
```
**Uninstaller flags** (pass via `bash -s -- <flags>`):
**Uninstaller flags:**
| Flag | Effect |
|------|--------|
@ -453,10 +453,10 @@ curl -fsSL https://raw.githubusercontent.com/NVIDIA/NemoClaw/refs/heads/main/uni
| `--keep-openshell` | Leave the `openshell` binary in place |
| `--delete-models` | Also remove the Ollama models pulled by NemoClaw |
To remove everything including the Ollama model, non-interactively:
To remove everything including the Ollama model:
```bash
curl -fsSL https://raw.githubusercontent.com/NVIDIA/NemoClaw/refs/heads/main/uninstall.sh | bash -s -- --yes --delete-models
./uninstall.sh --yes --delete-models
```
The uninstaller runs 6 steps:
@ -468,7 +468,7 @@ The uninstaller runs 6 steps:
6. Remove state directories (`~/.nemoclaw`, `~/.config/openshell`, `~/.config/nemoclaw`) and the OpenShell binary
> [!NOTE]
> If you have a local clone at `~/.nemoclaw/source` you want to keep, move or back it up before running the uninstaller — it is removed as part of state cleanup in step 6.
> The source clone at `~/.nemoclaw/source` is removed as part of state cleanup in step 6. If you want to keep a local copy, move or back it up before running the uninstaller.
## Useful commands
@ -478,13 +478,13 @@ The uninstaller runs 6 steps:
| `nemoclaw my-assistant status` | Show sandbox status and inference config |
| `nemoclaw my-assistant logs --follow` | Stream sandbox logs in real time |
| `nemoclaw list` | List all registered sandboxes |
| `nemoclaw tunnel start` | Start cloudflared tunnel (public URL for Telegram webhooks) |
| `nemoclaw tunnel stop` | Stop the cloudflared tunnel |
| `nemoclaw start` | Start auxiliary services (Telegram bridge, cloudflared) |
| `nemoclaw stop` | Stop auxiliary services |
| `openshell term` | Open the monitoring TUI on the host |
| `openshell forward list` | List active port forwards |
| `openshell forward start 18789 my-assistant --background` | Restart port forwarding for Web UI |
| `curl -fsSL https://raw.githubusercontent.com/NVIDIA/NemoClaw/refs/heads/main/uninstall.sh \| bash` | Remove NemoClaw (preserves Docker, Node.js, Ollama) |
| `curl -fsSL https://raw.githubusercontent.com/NVIDIA/NemoClaw/refs/heads/main/uninstall.sh \| bash -s -- --delete-models` | Remove NemoClaw and Ollama models |
| `cd ~/.nemoclaw/source && ./uninstall.sh` | Remove NemoClaw (preserves Docker, Node.js, Ollama) |
| `cd ~/.nemoclaw/source && ./uninstall.sh --delete-models` | Remove NemoClaw and Ollama models |
## Troubleshooting

30
nvidia/sglang/README.md
View File

@ -53,7 +53,6 @@ The following models are supported with SGLang on Spark. All listed models are a
| Model | Quantization | Support Status | HF Handle |
|-------|-------------|----------------|-----------|
| **Nemotron-3-Nano-Omni-30B-A3B-Reasoning** | BF16 | ✅ | [`nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16`](https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16) |
| **GPT-OSS-20B** | MXFP4 | ✅ | `openai/gpt-oss-20b` |
| **GPT-OSS-120B** | MXFP4 | ✅ | `openai/gpt-oss-120b` |
| **Llama-3.1-8B-Instruct** | FP8 | ✅ | `nvidia/Llama-3.1-8B-Instruct-FP8` |
@ -76,19 +75,12 @@ Note: for NVFP4 models, add the `--quantization modelopt_fp4` flag.
* **Estimated time:** 30 minutes for initial setup and validation
* **Risk level:** Low - Uses pre-built, validated SGLang container with minimal configuration
* **Rollback:** Stop and remove containers with `docker stop` and `docker rm` commands
* **Last Updated:** 04/28/2026
* Introduce Nemotron-3-Nano-Omni reasoning FP8 support
* **Last Updated:** 03/15/2026
* Use latest NGC SGLang container: nvcr.io/nvidia/sglang:26.02-py3
## Instructions
## Step 1. Use model specific deployment guide
Certain models require special deployment configurations. Please refer to their respective model cards to run on DGX Spark:
| Model | Quantization | HF Model Card Link |
|-------|-------------|----------------|
| **Nemotron-3-Nano-Omni-30B-A3B-Reasoning** | BF16 | https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16 |
## Step 2. Verify system prerequisites
## Step 1. Verify system prerequisites
Check that your NVIDIA Spark device meets all requirements before proceeding. This step runs on
your host system and ensures Docker, GPU drivers, and container toolkit are properly configured.
@ -116,7 +108,7 @@ sudo usermod -aG docker $USER
newgrp docker
```
## Step 3. Pull the SGLang Container
## Step 2. Pull the SGLang Container
Download the latest SGLang container. This step runs on the host and may take
several minutes depending on your network connection.
@ -130,7 +122,7 @@ docker pull nvcr.io/nvidia/sglang:26.02-py3
docker images | grep sglang
```
## Step 4. Launch SGLang container for server mode
## Step 3. Launch SGLang container for server mode
Start the SGLang container in server mode to enable HTTP API access. This runs the inference
server inside the container, exposing it on port 30000 for client connections.
@ -144,7 +136,7 @@ docker run --gpus all -it --rm \
bash
```
## Step 5. Start the SGLang inference server
## Step 4. Start the SGLang inference server
Inside the container, launch the HTTP inference server with a supported model. This step runs
inside the Docker container and starts the SGLang server daemon.
@ -167,7 +159,7 @@ sleep 30
curl http://localhost:30000/health
```
## Step 6. Test client-server inference
## Step 5. Test client-server inference
From a new terminal on your host system, test the SGLang server API to ensure it's working
correctly. This validates that the server is accepting requests and generating responses.
@ -185,7 +177,7 @@ curl -X POST http://localhost:30000/generate \
}'
```
## Step 7. Test Python client API
## Step 6. Test Python client API
Create a simple Python script to test programmatic access to the SGLang server. This runs on
the host system and demonstrates how to integrate SGLang into applications.
@ -205,7 +197,7 @@ response = requests.post('http://localhost:30000/generate', json={
print(f"Response: {response.json()['text']}")
```
## Step 8. Validate installation
## Step 7. Validate installation
Confirm that both server and offline modes are working correctly. This step verifies the
complete SGLang setup and ensures reliable operation.
@ -221,7 +213,7 @@ docker ps
docker logs <CONTAINER_ID>
```
## Step 9. Cleanup and rollback
## Step 8. Cleanup and rollback
Stop and remove containers to clean up resources. This step returns your system to its
original state.
@ -240,7 +232,7 @@ docker container prune -f
docker rmi nvcr.io/nvidia/sglang:26.02-py3
```
## Step 10. Next steps
## Step 9. Next steps
With SGLang successfully deployed, you can now:

14
nvidia/speculative-decoding/README.md
View File

@ -57,7 +57,7 @@ In short: two Sparks let you run models that are too large for one, while specul
- Docker with GPU support enabled
```bash
docker run --gpus all nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc12 nvidia-smi
docker run --gpus all nvcr.io/nvidia/tensorrt-llm/release:1.2.0rc6 nvidia-smi
```
- Active HuggingFace Token for model access
- Network connectivity for model downloads
@ -68,9 +68,9 @@ In short: two Sparks let you run models that are too large for one, while specul
* **Duration:** 10-20 minutes for setup, additional time for model downloads (varies by network speed)
* **Risks:** GPU memory exhaustion with large models, container registry access issues, network timeouts during downloads
* **Rollback:** Stop Docker containers and optionally clean up downloaded model cache.
* **Last Updated:** 04/20/2026
* Upgrade to latest container 1.3.0rc12
* Add Speculative Decoding example with Qwen3-235B-A22B on Two Sparks
* **Last Updated:** 01/02/2026
* Upgrade to latest container v1.2.0rc6
* Add EAGLE-3 Speculative Decoding example with GPT-OSS-120B
## Instructions
@ -111,7 +111,7 @@ docker run \
-v $HOME/.cache/huggingface/:/root/.cache/huggingface/ \
--rm -it --ulimit memlock=-1 --ulimit stack=67108864 \
--gpus=all --ipc=host --network host \
nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc12 \
nvcr.io/nvidia/tensorrt-llm/release:1.2.0rc6 \
bash -c '
hf download openai/gpt-oss-120b && \
hf download nvidia/gpt-oss-120b-Eagle3-long-context \
@ -172,7 +172,7 @@ docker run \
-e HF_TOKEN=$HF_TOKEN \
-v $HOME/.cache/huggingface/:/root/.cache/huggingface/ \
--rm -it --ulimit memlock=-1 --ulimit stack=67108864 \
--gpus=all --ipc=host --network host nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc12 \
--gpus=all --ipc=host --network host nvcr.io/nvidia/tensorrt-llm/release:1.2.0rc6 \
bash -c "
# # Download models
hf download nvidia/Llama-3.3-70B-Instruct-FP4 && \
@ -309,7 +309,7 @@ docker run -d --rm \
-e TRITON_PTXAS_PATH="/usr/local/cuda/bin/ptxas" \
-v ~/.cache/huggingface/:/root/.cache/huggingface/ \
-v ~/.ssh:/tmp/.ssh:ro \
nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc12 \
nvcr.io/nvidia/tensorrt-llm/release:1.2.0rc6 \
bash -c "curl https://raw.githubusercontent.com/NVIDIA/dgx-spark-playbooks/refs/heads/main/nvidia/trt-llm/assets/trtllm-mn-entrypoint.sh | bash"
```

10
nvidia/trt-llm/README.md
View File

@ -57,7 +57,7 @@ inference through kernel-level optimizations, efficient memory layouts, and adva
- DGX Spark device
- NVIDIA drivers compatible with CUDA 12.x: `nvidia-smi`
- Docker installed and GPU support configured: `docker run --rm --gpus all nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc5 nvidia-smi`
- Docker installed and GPU support configured: `docker run --rm --gpus all nvcr.io/nvidia/tensorrt-llm/release:1.2.0rc6 nvidia-smi`
- Hugging Face account with token for model access: `echo $HF_TOKEN`
- Sufficient GPU VRAM (40GB+ recommended for 70B models)
- Internet connectivity for downloading models and container images
@ -136,7 +136,7 @@ models and containers.
nvidia-smi
## Verify Docker GPU support
docker run --rm --gpus all nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc5 nvidia-smi
docker run --rm --gpus all nvcr.io/nvidia/tensorrt-llm/release:1.2.0rc6 nvidia-smi
```
@ -146,7 +146,7 @@ docker run --rm --gpus all nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc5 nvidia-s
## Set `HF_TOKEN` for model access.
export HF_TOKEN=<your-huggingface-token>
export DOCKER_IMAGE="nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc5"
export DOCKER_IMAGE="nvcr.io/nvidia/tensorrt-llm/release:1.2.0rc6"
```
## Step 4. Validate TensorRT-LLM installation
@ -161,8 +161,8 @@ docker run --rm -it --gpus all \
Expected output:
```
[TensorRT-LLM] TensorRT-LLM version: 1.3.0rc5
TensorRT-LLM version: 1.3.0rc5
[TensorRT-LLM] TensorRT-LLM version: 1.2.0rc6
TensorRT-LLM version: 1.2.0rc6
```
## Step 5. Create cache directory

27
nvidia/vllm/README.md
View File

@ -54,9 +54,6 @@ The following models are supported with vLLM on Spark. All listed models are ava
| Model | Quantization | Support Status | HF Handle |
|-------|-------------|----------------|-----------|
| **Nemotron-3-Nano-Omni-30B-A3B-Reasoning** | BF16 | ✅ | [`nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16`](https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16) |
| **Nemotron-3-Nano-Omni-30B-A3B-Reasoning** | FP8 | ✅ | [`nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8`](https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8) |
| **Nemotron-3-Nano-Omni-30B-A3B-Reasoning** | NVFP4 | ✅ | [`nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-NVFP4`](https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-NVFP4) |
| **Gemma 4 31B IT** | Base | ✅ | [`google/gemma-4-31B-it`](https://huggingface.co/google/gemma-4-31B-it) |
| **Gemma 4 31B IT** | NVFP4 | ✅ | [`nvidia/Gemma-4-31B-IT-NVFP4`](https://huggingface.co/nvidia/Gemma-4-31B-IT-NVFP4) |
| **Gemma 4 26B A4B IT** | Base | ✅ | [`google/gemma-4-26B-A4B-it`](https://huggingface.co/google/gemma-4-26B-A4B-it) |
@ -97,22 +94,12 @@ Reminder: not all model architectures are supported for NVFP4 quantization.
* **Duration:** 30 minutes for Docker approach
* **Risks:** Container registry access requires internal credentials
* **Rollback:** Container approach is non-destructive.
* **Last Updated:** 04/28/2026
* Add support for Nemotron-3-Nano-Omni reasoning BF16, FP8, NVFP4
* **Last Updated:** 04/02/2026
* Add support for Gemma 4 model family
## Instructions
## Step 1. Use model specific deployment guide
Certain models require special deployment configurations. Please refer to their respective model cards to run on DGX Spark:
| Model | Quantization | HF Model Card Link |
|-------|-------------|----------------|
| **Nemotron-3-Nano-Omni-30B-A3B-Reasoning** | BF16 | https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16 |
| **Nemotron-3-Nano-Omni-30B-A3B-Reasoning** | FP8 | https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8 |
| **Nemotron-3-Nano-Omni-30B-A3B-Reasoning** | NVFP4 | https://huggingface.co/nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-NVFP4 |
## Step 2. Configure Docker permissions
## Step 1. Configure Docker permissions
To easily manage containers without sudo, you must be in the `docker` group. If you choose to skip this step, you will need to run Docker commands with sudo.
@ -128,7 +115,7 @@ sudo usermod -aG docker $USER
newgrp docker
```
## Step 3. Pull vLLM container image
## Step 2. Pull vLLM container image
Find the latest container build from https://catalog.ngc.nvidia.com/orgs/nvidia/containers/vllm
@ -149,7 +136,7 @@ For Gemma 4 model family, use vLLM custom containers:
docker pull vllm/vllm-openai:gemma4-cu130
```
## Step 4. Test vLLM in container
## Step 3. Test vLLM in container
Launch the container and start vLLM server with a test model to verify basic functionality.
@ -184,7 +171,7 @@ curl http://localhost:8000/v1/chat/completions \
Expected response should contain `"content": "204"` or similar mathematical calculation.
## Step 5. Cleanup and rollback
## Step 4. Cleanup and rollback
For container approach (non-destructive):
@ -193,7 +180,7 @@ docker rm $(docker ps -aq --filter ancestor=nvcr.io/nvidia/vllm:${LATEST_VLLM_VE
docker rmi nvcr.io/nvidia/vllm
```
## Step 6. Next steps
## Step 5. Next steps
- **Production deployment:** Configure vLLM with your specific model requirements
- **Performance tuning:** Adjust batch sizes and memory settings for your workload