System Requirements#

This page summarizes the recommended environment for running Real-Time Scene Understanding.

Hardware Platforms used for validation#

  • This application is specifically targeting Intel® Core™ platforms. Intel® Core™ Ultra 2 and 3 with integrated GPU are currently supported.

  • Intel® Core™ Ultra platforms with integrated NPU can run compatible VLMs; model and resolution support vary by device.

  • While there is no hard restriction in using this application on Intel® Xeon® platforms with or without Intel® Arc™ GPUs, users are requested to raise a feature ticket in case of any requirement.

Operating Systems used for validation#

  • Ubuntu: Refer to the official documentation for details on the required kernel version. For the listed hardware platforms, the kernel requirement translates to Ubuntu 24.04 or Ubuntu 24.10 depending on the GPU used.

Minimum Requirements#

Component

Minimum

Recommended

Memory

16 GB

32 GB

Disk Space

64 GB SSD

128 GB SSD

Software Requirements#

  • Docker Engine and Docker Compose

  • Intel® Graphics compute runtime (if using Intel GPU for inference acceleration)

  • RTSP source reachable from the real-time-scene-understanding container (optional, can be added via UI)

Network / Ports#

Default host ports (configurable in .env):

  • PORT=9100 (Dashboard UI and REST API; Compose maps this host port to container port 9100)

  • METRICS_SERVICE_PORT=9090 (Live metrics SSE service)

  • WEBRTC_SIGNALING_PORT=8889 (MediaMTX WebRTC/WHEP signaling)

  • 8189/udp (MediaMTX WebRTC media)

  • 3478/udp (Coturn TURN service)

Compose publishes the dashboard host port through PORT (default 9100); add PORT to .env to change it. The container listens on port 9100. The DASHBOARD_PORT value in .env.example does not change the host port mapping.

Model Requirements#

The application uses two VLM models. The alert model performs frame-level alert gating, and the deep-analyzer model processes segmented clips. The deep-analyzer model must support video input. The configured models are Qwen3-VL-2B-Instruct for the alert pipeline and Qwen3-VL-8B-Instruct for deep analysis.

Configure model selection via .env file as below:

  • ALERT_VLM_MODEL=Qwen3-VL-2B-Instruct

  • ALERT_VLM_DEVICE=CPU

  • DEEP_ANALYZER_MODEL=Qwen3-VL-8B-Instruct

  • DEEP_ANALYZER_DEVICE=GPU

These are the values in .env.example and match the model-preparation commands. Download or prepare them for the configured device and make sure their directory names match the model settings under ov_models/<device-lowercase>/<model-name>/. The application can use pre-converted OpenVINO models. The model directory names are:

  • Qwen3-VL-2B-Instruct

  • Qwen3-VL-8B-Instruct

Model files can also be prepared using the provided scripts and model directories in this repository (for example, model_download_scripts/download_models.sh and ov_models/). Please refer to the Model Preparation Guide for the steps.

Note

Pre-converted OpenVINO IR VLMs can avoid local model conversion. When choosing one, verify compatibility with the OpenVINO GenAI runtime and target device used by this application. Browse models from the OpenVINO organization on Hugging Face and its VLM collection.

Validation#

Proceed to Get Started once Docker is installed and internet connectivity is available for model downloads.