> This page is for version Dev.
> For other versions, use one of these documentation indexes:
> - 3.3.0 (default): https://docs.nvidia.com/vss/3.3.0/llms.txt
> - Dev: https://docs.nvidia.com/vss/dev/llms.txt

> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/vss/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/vss/_mcp/server.

# Smart City Blueprint

> **Note**
>
> This documentation describes the Smart City Blueprint as released with **VSS 3.3.0**.
> Deploy it from the [repository reference selected for this documentation build](https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization/tree/develop).
> Other tags and branches ship different microservice
> versions and are not covered by these pages. See [Quickstart Guide - Docker Compose](/vision-agent/agent-workflows/industry-specific-examples/smart-city-blueprint/quickstart-guide-docker-compose)
> to deploy it.

The NVIDIA Smart City AI Blueprint provides a set of reference workflows tailored for key verticals. These workflows help developers quickly build and deploy real-world applications by extending the core Video Search and Summarization (VSS) capabilities with a **three-computer solution** architecture. The end-to-end approach covers simulation, model training, and deployment for smart-city use cases.

The blueprint consists of three high-level stages:

* **Simulate** - create synthetic data using open-source simulators and upscale the data through NVIDIA pipelines.
* **Train** - follow recipes and instructions for training real-time computer-vision models and Cosmos3 Nano Reasoner.
* **Deploy** - configure VSS, take advantage of unique smart-city features and use reference examples to build your own solution.

![Illustration of the three-computer smart-city workflow: simulate, train, and deploy](/vss/_fern-img/b9bd5b13022850e82110b6e8ad45dad2d0c8dcf11d85339c949f90d4be5eadc4.webp)