NVIDIA, T-Mobile, and partners integrate physical AI applications into AI-RAN-enabled infrastructure
News summary:
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T-Mobile will pilot NVIDIA RTX PRO 6000 Blackwell Server Edition AI infrastructure to demonstrate physical AI applications at the edge and complement the AI-RAN Innovation Center’s distributed network.
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Physical AI developers including Fogsphere, LinkerVision, Levatas, Vaidio, and Siemens Energy are using NVIDIA Metropolis Blueprints for Video Search and Summarization (VSS) to build inference and vision AI agents at the edge and integrate them into T-Mobile’s distributed edge network. The City of San Jose was one of the first to evaluate this technology.
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The new NVIDIA VSS Blueprint version 3 accelerates the development of inferential video analytics AI agents through a flexible modular architecture, advanced multimodal visual understanding, and integrated agent search capabilities.
GTC—NVIDIA and T-Mobile (NASDAQ: TMUS) today announced that they are collaborating with Nokia and a growing ecosystem of developers to enable physical AI applications on distributed edge AI networks. This collaboration demonstrates how next-generation AI-RAN infrastructure can transform wireless networks into a platform for distributed, high-performance edge AI computing and lays the foundation for developers to use the NVIDIA Metropolis platform to deploy vision AI agents that understand the physical world across cities, utilities, and industrial sites.
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NVIDIA and T-Mobile are working with Nokia and a growing ecosystem of developers to enable physical AI applications on distributed edge AI networks. This collaboration demonstrates how next-generation AI-RAN infrastructure can transform wireless networks into a platform for distributed, high-performance edge AI computing and lays the foundation for developers to use the NVIDIA Metropolis platform to deploy vision AI agents that understand the physical world across cities, utilities, and industrial sites.
NVIDIA’s AI-RAN portfolio includes NVIDIA ARC-Pro, built on the NVIDIA RTX PRO 4500 Blackwell Server Edition for power-constrained cell sites and the NVIDIA RTX PRO 6000 Blackwell Server Edition for high-capacity mobile switching offices (MSOs).
T-Mobile was the first company in the U.S. to pilot NVIDIA’s AI-RAN infrastructure using Nokia’s anyRAN software and is currently working with select NVIDIA physical AI partners to demonstrate how cell sites and mobile switching centers can support distributed edge AI workloads while continuing to deliver advanced 5G connectivity.
“Communications networks are evolving into AI infrastructure that enables billions of devices, from vision AI agents to robots and self-driving cars, to see, hear, and act in real time,” said Jensen Huang, founder and CEO of NVIDIA. “Together with T-Mobile and Nokia, we are creating a scalable blueprint for the world’s edge AI infrastructure by turning 5G networks into distributed AI computers.”
“Turning your network into a distributed AI computing platform and unlocking the full potential of physical AI requires ultra-low latency and spatio-temporal consistency at the network edge for billions of endpoints. This is what we’ve built at T-Mobile,” said Srini Gopalan, CEO of T-Mobile. “With the first nationwide 5G Standalone and 5G Advanced Networks, we are uniquely positioned to drive a future that relies on intelligent networks that enable intelligent systems to operate in real-time, rather than waiting in the cloud.”
Mobile networks as the nervous system for physical AI
The move to AI-RAN, built on NVIDIA accelerated computing, solves a critical bottleneck in scaling physical AI: the lack of low-latency, secure, and ubiquitous connectivity. While Wi-Fi is limited by range and security, T-Mobile’s 5G standalone network provides the wide-area coverage and guaranteed quality of service that complex AI agents need to operate in busy urban intersections, industrial facilities, and rural areas.
This architecture allows physical AI to offload heavy computations from the device to the closest edge location. By moving heavy processing to the network edge, developers can streamline the hardware requirements for individual cameras and robots and cost-effectively scale advanced AI models across billions of interconnected devices.
Leading developers bring inference and vision AI to the edge
A growing ecosystem of developers is working with NVIDIA and T-Mobile to integrate physical AI agents that drive real-time actions built on the NVIDIA Metropolis Blueprint for Video Search and Summarization (VSS) on T-Mobile’s distributed edge network. Examples of pilot usage include:
- Smart city management: LinkerVision, Inchor, and Voxelmaps are testing an integrated computer vision-based “city operations agent” and digital twin that can recognize, simulate, and optimize traffic light timing, with the goal of speeding up incident response times for the city of San Jose by five times.
- Automatic utility check: Levatas and Skydio use NVIDIA computing to automate inspections of hundreds of thousands of miles of power lines over 5G, detecting and resolving anomalies like tilted utility poles, corrosion, and thermal hot spots five times faster. They are currently evaluating AI-RAN infrastructure to further reduce costs, reduce storm recovery time, and accelerate the transition from reactive to predictive maintenance.
- Vision-based facility management: Developers like Vaidio are using VSS Blueprints to build facility management agents that go beyond simple sensors to perform threat detection and failure prediction, and trigger automated workflows to improve facility management.
- Real-time industrial safety: Fogsphere will provide SAIPEM with a safety AI agent to detect and respond to hazardous events in high-risk construction sites, such as workers under suspended loads and hydrocarbon spills, in real-time on land, offshore, and in drilling environments. Fogsphere is currently validating how AI-RAN infrastructure can enhance the functionality and performance of these agents (which are already Wi-Fi independent and run 24/7) on secure, distributed network computing.
These efforts reflect T-Mobile’s broader strategy to collaborate with NVIDIA, Nokia, and a diverse ecosystem of software providers, manufacturers, and enterprise innovators to test and enable edge AI capabilities.
Accelerate Vision AI agent development with Metropolis VSS 3 Blueprints
More than 1.5 billion cameras capture footage around the world, but humans review less than 1% of it. NVIDIA is introducing Metropolis VSS 3 blueprints that enable agents to make video decisions from the edge to the cloud.
Key features of the latest iteration of Blueprints include:
- Get agent information: AI agents can break down complex natural language queries and search video footage to find specific events in less than 5 seconds.
- Modular architecture: The flexible framework allows teams to adapt VSS 3 to a variety of environments, from retail stores to warehouses, without requiring a complete overhaul of the core infrastructure.
- 100x more efficient: VSS can summarize long-form video up to 100 times faster than manual review, significantly reducing repetitive tasks and review costs for global physical operations.
Partners using VSS Blueprints to optimize operations and enhance safety across the industry include Caterpillar, KION, Hitachi, HCLTech, Siemens Energy, Tulip, and Telit Cinterion.
For more information, NVIDIA VSS Blueprint in build.nvidia.com
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