---
title: "Live VLM WebUI"
publisher: "nvidia"
type: "playbook"
updated: "2026-01-02T23:03:49.100Z"
description: "Real-time Vision Language Model interaction with webcam streaming"
canonical: "https://build.nvidia.com/spark/live-vlm-webui.md"
---

# Basic idea

Live VLM WebUI is a universal web interface for real-time Vision Language Model (VLM) interaction and benchmarking. It enables you to stream your webcam directly to any VLM backend (Ollama, vLLM, SGLang, or cloud APIs) and receive live AI-powered analysis. This tool is perfect for testing VLM models, benchmarking performance across different hardware configurations, and exploring vision AI capabilities.

The interface provides WebRTC-based video streaming, integrated GPU monitoring, customizable prompts, and support for multiple VLM backends. It works seamlessly with the powerful Blackwell GPU in your DGX Spark, enabling real-time vision inference at impressive speeds.

# What you'll accomplish

You'll set up a complete real-time vision AI testing environment on your DGX Spark that allows you to:

- Stream webcam video and get instant VLM analysis through a web browser
- Test and compare different vision language models (Gemma 3, Llama Vision, Qwen VL, etc.)
- Monitor GPU and system performance in real-time while models process video frames
- Customize prompts for various use cases (object detection, scene description, OCR, safety monitoring)
- Access the interface from any device on your network with a web browser

# What to know before starting

- Basic familiarity with Linux command line and terminal operations
- Basic knowledge of Python application installation with pipx
- Basic knowledge of REST APIs and how services communicate via HTTP
- Familiarity with web browsers and network access (IP addresses, ports)
- Optional: Knowledge of Vision Language Models and their capabilities (helpful but not required)

# Prerequisites

**Hardware Requirements:**
- Webcam (laptop built-in camera, USB camera, or remote browser with camera)
- At least 10GB available storage space for Python packages and model downloads

**Software Requirements:**
- DGX Spark with DGX OS installed
- Python 3.10 or later (verify with `python3 --version`)
- pipx (installed in Step 2)
- Network access to download Python packages from PyPI
- A VLM backend running locally (Ollama being easiest) or cloud API access
- Web browser access to `https://<SPARK_IP>:8090`

**VLM Backend Options:**
1. **Ollama** (recommended for beginners) - Easy to install and use
2. **vLLM** - Higher performance for production workloads
3. **SGLang** - Alternative high-performance backend
4. **NIM** - NVIDIA Inference Microservices for optimized performance
5. **Cloud APIs** - NVIDIA API Catalog, OpenAI, or other OpenAI-compatible APIs

# Ancillary files

All source code and documentation can be found at the [Live VLM WebUI GitHub repository](https://github.com/NVIDIA-AI-IOT/live-vlm-webui).

The package will be installed in an isolated environment via pipx, so no additional files are required for basic installation.

# Time & risk

* **Estimated time:** 20-30 minutes (including Ollama installation and model download)
* 5 minutes to install Live VLM WebUI via pipx
* 10-15 minutes to install Ollama and download a model (varies by model size)
* 5 minutes to configure and test
* **Risk level:** Low
* Python packages installed in a pipx-managed environment, isolated from the system Python environment
* No system-level changes required
* Port 8090 must be accessible for web interface functionality
* Self-signed SSL certificate requires browser security exception
* **Rollback:** Uninstall the Python application with `pipx uninstall live-vlm-webui`. Ollama can be uninstalled with standard package removal. No persistent changes to DGX Spark configuration.
* **Last Updated:** 07/01/2026
* Updated Live VLM WebUI installation to use pipx

## More

- [Instructions](/spark/live-vlm-webui/instructions.md)
- [Troubleshooting](/spark/live-vlm-webui/troubleshooting.md)