Live VLM WebUI

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:

Ollama (recommended for beginners) - Easy to install and use
vLLM - Higher performance for production workloads
SGLang - Alternative high-performance backend
NIM - NVIDIA Inference Microservices for optimized performance
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.

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

Time & risk