The Telecom AI Renaissance: How NVIDIA Is Transforming Telecommunications Networks
From Connectivity Infrastructure to AI Power Grid
Telecommunications networks have served as the connective tissue of the global economy for decades—reliable, ubiquitous, and largely invisible. What's changing now is the fundamental nature of what these networks do. Carriers are evolving from providers of passive connectivity into operators of intelligent, self-adapting systems capable of solving problems autonomously. NVIDIA sits at the center of this transformation.
This article covers three dimensions of NVIDIA's telecom strategy: operational AI (efficiency and customer experience), AI Factories (new revenue streams), and AI RAN (next-generation network architecture).
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Operational AI: Efficiency and Customer Experience
Agentic AI in Telecom Operations
The most immediate application is operational: using AI agents to handle tasks that previously required human teams working for hours or days.
What agentic AI means in this context: autonomous AI systems that execute tasks, coordinate with other agents, and solve complex problems without requiring human intervention at each step.
Softbank example: Working with NVIDIA inference microservices, Softbank deployed a Large Telco Model that reduced the planning time for large-event traffic management from several days to minutes. When a baseball game is scheduled, the AI agent forecasts expected traffic demand, simulates solutions (moving user traffic to adjacent cells, adjusting capacity allocations), and schedules the configuration changes automatically. The reported result: 30% improvement in data throughput per user.
Amdocs example: NVIDIA's framework enabled Amdocs to build agents for network fault response. When a problem is detected, the agent uses a network digital twin to simulate multiple remediation options—offloading traffic to backup links, limiting video bandwidth, temporarily increasing primary link capacity—evaluates each option's impact, and presents the best choice for operator confirmation. Decision time drops from days to minutes.
The Data Foundation
For these agents to work, they need training data that's specific to telecom networks: configuration files, log files, RF (radio frequency) data, acoustic sensor data. These aren't the text, image, and video data that general AI models train on. They represent "the language of the network," and models trained on them can understand network behavior in ways general-purpose models cannot.
NVIDIA's Nemo framework supports the development of these specialized agents. Self-organizing AI networks—where agents make autonomous decisions within the network—are already appearing in practice, not just in research.
AI Factories: New Revenue Streams for Carriers
The Core Business Problem
Telecom revenue growth has stagnated at 1–2% annually in mature markets for years. The business model—selling connectivity—doesn't naturally scale. AI Factories offer a different revenue model.
An AI Factory is AI infrastructure that generates revenue directly. Unlike traditional cloud infrastructure (which carriers have often built but found difficult to monetize), AI Factories use carriers' existing assets—physical facilities, power supply capacity, customer relationships, network connectivity—to provide AI compute as a service.
NVIDIA's Cloud Partner Program (NCP)
NVIDIA supports carrier AI Factory development through its NCP program. Currently, 15 carriers worldwide are NCP participants with announced AI Factory builds.
A key driver is sovereign AI—the principle that data should remain within the country or region where it was generated, under that jurisdiction's laws. Healthcare, education, and research all handle sensitive data with this requirement. Carriers, with their nationwide infrastructure and existing customer relationships, are naturally positioned to serve sovereign AI demand.
Specific examples:
- Indosat Ooredoo Hutchison (Indonesia): Built an AI Factory and hosts "Sahabat AI," a voice assistant developed by GoTo (Indonesia's major tech company) using a large language model trained on Indonesian language data
- Telenor (Norway): Provides GPU-as-a-Service to Norwegian startups
- Iliad (France): Startup partnership program using AI Factory infrastructure
- Telus (Canada): Newly joined NCP as North America's first service provider AI Factory; aligned with Canada's national AI priority designation
The Infrastructure Demand Curve
NVIDIA CEO Jensen Huang describes three scaling laws for AI:
- Training and building large language models
- Fine-tuning and inference for specific applications
- Inference demand at deployment scale (the usage wave)
Each requires different infrastructure with different performance characteristics. As all three phases scale simultaneously, compute demand increases continuously. Carriers can play roles at every layer—from centralized AI development hubs to distributed inference points to the devices in users' hands.
AI RAN: The Next-Generation Network
Why Networks Need to Change
AI data traffic is fundamentally different from traditional voice, video, and web traffic. Real-time video search, simultaneous AI processing across millions of smartphones, autonomous vehicle situational awareness—these require enormous compute capacity with specific latency requirements:
- Physical AI: under 1 second
- Conversational AI: under 2 seconds (natural dialogue)
- Video search: under 6 seconds
Traditional cloud-centric architectures can't reliably meet these requirements. Edge computing—placing compute resources close to where data is generated—is necessary. Additionally, data gravity (data tending to remain near its source), cost of cloud transmission, and data sovereignty requirements all push toward distributed compute at the network edge.
The AI RAN Architecture
Traditional radio access networks are built from specialized hardware sized to peak demand, resulting in average utilization of about 30%. NVIDIA's AI RAN proposal is a convergence of AI workloads and RAN functions on shared general-purpose infrastructure.
AI RAN comprises three components:
- AI for RAN: Using AI to improve RAN spectral efficiency, power efficiency, and operational efficiency
- AI running together with RAN: RAN functions and AI applications co-hosted on the same general-purpose hardware
- AI delivered on RAN: Using the RAN as a delivery medium for AI services to users and devices
Softbank validation: In a proof-of-concept near Tokyo, Softbank demonstrated improved ROI, increased utilization, and reduced energy consumption from AI RAN deployment.
NVIDIA provides the Aerial RAN Platform (based on the Grace Hopper superchip) as the reference implementation—running GPU-accelerated RAN stack functions alongside 5G core functions and AI workloads simultaneously.
The AI RAN Alliance
Formed about a year ago, the AI RAN Alliance has grown to 75+ member organizations. At a recent Mobile World Congress, 10 demos were presented by alliance members; 9 used NVIDIA's AI RAN reference architecture.
Current participants and projects:
- T-Mobile with Ericsson and Nokia: establishing AI RAN Innovation Centers
- Indosat Ooredoo Hutchison: announced distributed AI RAN deployment
- Deep Sig: achieved 70% throughput improvement using neural network-based Layer 1 processing (pilot-free implementation)
- Fujitsu and Softbank: joint effort achieving 50% uplink performance improvement
The 6G Development Path
NVIDIA is incorporating AI from the ground up in 6G design—an "AI-first" approach rather than adding AI to an existing architecture.
Partners: T-Mobile, MITRE, Cisco, O-RAN development companies, Booz Allen Hamilton.
NVIDIA's 6G developer program has 2,000+ members. Key tools:
- Sionna: Differentiable Layer 1 simulator for 6G research
- Sionna Research Kit: Hardware research kit for Sionna
- Aerial Omniverse Digital Twin: Physically accurate real-time RF propagation simulation
The Digital Twin capability is particularly notable: it builds detailed 3D city models (including building materials, vegetation) to simulate base station placement, AI-based channel estimation (40% improvement over traditional methods), and optimize configurations before physical deployment.
Summary
Telecom's AI transformation is operating on three simultaneous fronts:
- Agentic AI: Automating network operations and customer service—tasks that took days now take minutes
- AI Factories: Converting existing infrastructure assets into AI compute services, creating new revenue streams aligned with sovereign AI demand
- AI RAN: Merging AI and radio access networks on shared infrastructure, enabling the distributed compute architecture that AI data traffic requires
Each reinforces the others. Carriers that move early across all three dimensions will be positioned not just as connectivity providers but as AI infrastructure for the economies they serve.
Reference: https://www.youtube.com/watch?v=Jp8DfvedGws
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