Think of this as giving a city a "digital nervous system" powered by NVIDIA chips and AI software so it can see traffic, predict congestion, and coordinate signals, buses, and emergency vehicles more intelligently and automatically.
Manual, siloed management of traffic and urban mobility leads to congestion, delays, accidents, and inefficient use of roads and public transport. NVIDIA’s AI stack for smart cities aims to automate perception (cameras, sensors), prediction (traffic flows), and control (signals, routing) to improve mobility and safety while reducing operational costs.
Deep integration of NVIDIA GPUs and edge hardware, CUDA software ecosystem, and pre-built AI frameworks for computer vision and digital twins (e.g., Omniverse) that are hard for new entrants to replicate quickly.
Hybrid
Vector Search
High (Custom Models/Infra)
Inference latency and GPU/edge compute costs for real-time processing of many video streams across an entire city.
Early Majority
Leverages NVIDIA’s end-to-end stack (GPUs, edge devices, SDKs, and simulation/digital-twin tools) to deliver real-time, city-scale perception and control, rather than point solutions for a single intersection or corridor.