As AI\u2011native applications scale to more users, agents and devices, the telecommunications network is becoming the next frontier for distributing AI.\u00a0<\/p>\n
At NVIDIA GTC 2026, leading operators in the U.S. and Asia showed that this shift is underway, announcing AI grids<\/a> \u2014 geographically distributed and interconnected AI infrastructure<\/a> \u2014 using their network footprint to power and monetize new AI services across the distributed edge.\u00a0\u00a0<\/p>\n Different operators are taking different paths. Many are starting by lighting up existing wired edge sites as AI grids they can monetize today. Others harness AI-RAN<\/a> \u2014 a technology that enables the full integration of AI into the radio access network \u2014 as a workload and edge inference platform on the same grid.\u00a0\u00a0<\/p>\n Telcos and distributed cloud providers run some of the most expansive infrastructure in the world: about 100,000 distributed network data centers worldwide, spanning regional hubs, mobile switching offices and central offices, with enough spare power to offer more than 100 gigawatts of new AI capacity over time. Global Operators Turn Distributed Networks Into AI Grids<\/b><\/p>\n Across six major operators, AI grids are moving from concept to reality.<\/p>\n AT&T, a leader in connected IoT with over 100 million connections across thousands of device types, is partnering with Cisco and NVIDIA to build an AI grid for IoT. By running AI on a dedicated IoT core and moving AI inference closer to where data is created, AT&T can support mission\u2011critical, real\u2011time applications like public\u2011safety use cases with Linker Vision, enabling faster detection, alerting and response while helping keep sensitive information under customer control at the network edge.<\/p>\n \u201cScaling AI services that are both highly secure and accessible for enterprises and developers is a core pillar of our IoT connectivity strategy,\u201d said Shawn Hakl, senior vice president of product at AT&T Business. \u201cBy combining AT&T\u2019s business\u2011grade connectivity, localized AI compute and zero\u2011trust security while working with members of the NVIDIA Inception program and harnessing Cisco\u2019s AI Grid with NVIDIA infrastructure and Cisco Mobility Services Platform, we\u2019re bringing real\u2011time AI inference closer to where data is generated \u2014 accelerating digital transformation and unlocking new business opportunities.\u201d<\/p>\n Comcast<\/a> is developing one of the nation\u2019s largest low\u2011latency broadband footprints into an AI grid for real\u2011time, hyper\u2011personalized experiences. Working with NVIDIA, Decart, Personal AI and HPE, Comcast has validated that its AI grid keeps conversational agents, interactive media and NVIDIA GeForce NOW cloud gaming responsive and economical even during demand spikes, with significantly higher throughput and lower cost per token.<\/p>\n Spectrum<\/a> has the network infrastructure to support an AI grid that spans more than 1,000 edge data centers and hundreds of megawatts of capacity less than 10 milliseconds away from 500 million devices. The initial deployment focuses on rendering high-resolution graphics for media production using remote GPUs embedded across Spectrum\u2019s fiber-powered, low-latency network.<\/p>\n Akamai<\/a> is building a globally distributed AI grid, expanding Akamai Inference Cloud <\/a>across more than 4,400 edge locations with thousands of NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs. Akamai\u2019s AI grid orchestration platform<\/a> matches each request to the right tier of compute, improving the token economics of inference while powering low-latency, real-time AI experiences for applications like gaming, media, financial services and retail.<\/p>\n Indosat Ooredoo Hutchison is connecting its sovereign AI factory with distributed edge and AI\u2011RAN sites across Indonesia to build an AI grid for local innovation. By running Sahabat-AI \u2014 a Bahasa Indonesia-based platform \u2014 on this grid within Indonesia\u2019s borders, Indosat can bring localized AI services closer to hundred millions of Indonesians across thousands of islands, giving local developers and startups a sovereign platform to build AI applications that are fast, culturally relevant and compliant by design.<\/p>\n T\u2011Mobile<\/a> \u00a0is working with NVIDIA to explore edge AI applications using NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs, demonstrating how distributed network locations could support emerging AI-RAN and edge inference use cases. Developers including LinkerVision, Levatas, Vaidio, Archetype AI and Serve Robotics are already piloting smart\u2011city, industrial and retail applications on the grid, connecting cameras, delivery robots and city\u2011scale agents to real-time intelligence on the network edge. This demonstrates how cell sites and mobile switching offices can support distributed edge AI workloads while continuing to deliver advanced 5G connectivity.<\/p>\n New AI\u2011Native Services Put Telecom AI Grids to Work<\/b><\/p>\n AI grids are becoming foundational<\/a> to a new class of AI\u2011native applications \u2014 real\u2011time, hyper\u2011personalized, concurrent and token-intensive.<\/p>\n Personal AI <\/a>is using NVIDIA Riva to power human\u2011grade conversational agents on the AI grid. By running small language models closer to users, it achieves sub-500 millisecond end-to-end latency and over 50% lower cost-per-token, enabling voice experiences that feel natural while remaining economically viable at scale.<\/p>\n Linker Vision<\/a> is transforming city operations by running real\u2011time vision AI on the AI grid. By processing thousands of camera feeds across distributed edge sites, it delivers predictable latency for live detection and instant alerting \u2014 enabling safer, smarter cities with up to 10x faster traffic accident detection, 15x faster disaster response and sub\u2011minute alerts for unsafe crowd behavior.\u00a0<\/p>\n Decart<\/a> is redefining hyper\u2011personalized distributed media by bringing real\u2011time video generation to AI grids. By running its Lucy models at the network edge, it achieves sub\u201112-millisecond network latency, enabling interactive video streams and overlays that adapt instantly to each viewer, delivering smooth, immersive live video experiences even when viewership peaks.<\/p>\n AI Grid Reference Design and Ecosystem<\/b><\/p>\n The NVIDIA AI Grid Reference Design<\/a> defines the building blocks \u2014 including NVIDIA accelerated computing, networking and software platforms \u2014 for deploying and orchestrating AI across distributed sites.<\/p>\n A growing ecosystem of full\u2011stack partners including Cisco<\/a> and infrastructure partners like HPE<\/a> are bringing AI grid solutions to market on systems built with the NVIDIA RTX PRO 6000 Blackwell Server Edition<\/a>. Armada<\/a>, Rafay<\/a> and Spectro Cloud <\/a>are among the partners building an AI grid control plane to seamlessly orchestrate workloads across distributed AI infrastructure.<\/p>\n \u201cPhysical AI is accelerating the shift from centralized intelligence to distributed decision making at the network edge,\u201d said Masum Mir, senior vice president and general manager provider mobility at Cisco. \u201cOur partnership with NVIDIA brings together the full stack \u2014 from NVIDIA GPUs to Cisco\u2019s networking and mobility capabilities \u2014 enabling operators to power mission-critical applications, deliver real-time inferencing and participate in the AI value chain.\u201d<\/p>\n Together, this ecosystem is helping telcos and distributed cloud providers redefine their role in the AI value chain \u2014 transforming the network edge into a unified intelligence layer that runs, scales and monetizes AI workloads.<\/p>\n
AI grids turn this existing real-estate, power and connectivity into a geographically distributed computing platform that runs AI inference closer to users, devices and data, where response and cost per token align best. This is more than an infrastructure upgrade \u2014 it\u2019s a structural change in how AI is delivered, putting telecom networks at the center of scaling AI rather than just carrying its traffic.\u00a0<\/p>\n