What Is AI-RAN?

AI-RAN (artificial intelligence—radio access network) is a technology that enables the full integration of AI into the radio access network to realize transformative gains in operational performance, deliver new AI-based services, and unlock monetization opportunities. It enhances connectivity across mobile networks by leveraging AI to improve spectral efficiency, dynamic traffic handling, and real-time responsiveness.

There are three specific areas of AI integration into the RAN, as outlined by the AI-RAN Alliance—a community of telecom companies and academia with the mission to drive innovation and adoption of AI-RAN.

• AI and RAN (also referred to as AI with RAN): Using a common shared infrastructure to run both AI and RAN workloads, with the goal of maximizing utilization, lowering total cost of ownership (TCO), and generating new AI-driven revenue opportunities. AI-RAN’s deterministic architecture supports real-time processing of both AI and RAN workloads, crucial for latency-sensitive applications and services.
• AI for RAN: Advancing RAN capabilities through embedding AI/ML models, algorithms, and neural networks into the radio signal processing layer to improve spectral efficiency, radio coverage, capacity, and performance. Machine learning models embedded into radio signal processing enhance AI-RAN’s ability to boost radio coverage, optimize latency, and improve signal quality.
• AI on RAN: Enabling AI services on RAN at the network edge to increase operational efficiency and offer new services to mobile users. This turns the RAN from a cost center to a revenue source. With edge AI capabilities, AI-RAN brings intelligence closer to mobile users—enabling localized inferencing, reduced backhaul traffic, and real-time services at the network edge.

AI-RAN lays the technology foundation for the telecommunications industry to integrate the rapid advancements in AI technologies into the cellular telecommunications roadmap. It positions the telecom industry to fully embrace AI integration as it evolves toward 6G.

The surge in AI and generative AI applications is creating increased demands on cellular networks, driving demand for AI inferencing at the edge, and necessitating new approaches to handle these workloads. By enabling intelligent traffic management and low-latency services at the network edge, AI-RAN contributes to more responsive and consistent customer experiences across mobile applications.

At the same time, advances in AI-based radio signal processing techniques are showing compelling results versus traditional techniques and promising transformative gains in radio efficiency and performance.

As the industry begins its 6G journey, AI-RAN built with general-purpose commercial-off-the-shelf (COTS) servers and software-defined acceleration provides enhanced capabilities to process increased AI and non-AI traffic efficiently, compared to traditional RAN systems that are based on purpose-built hardware, whether it be custom application-specific integrated circuits (ASICs) or system on chips (SoCs) with embedded accelerators.

AI-RAN creates new revenue opportunities from hosting AI workloads and enables AI to be integrated into the operations of the RAN to optimize network performance, automate management tasks, and enhance overall user experience. For network operators, AI-RAN enables better resource allocation and monetization opportunities through AI service hosting and improved infrastructure efficiency.

AI-RAN enables the deployment of 5G RAN and AI workloads on a shared, distributed, and accelerated cloud infrastructure, thereby addressing the two key challenges communication service providers (CoSPs) have had for a long time:

• Average infrastructure utilization is low, leading to lower return on investment (ROI).
• Monetization of RAN-only services has limited upside, as it is seen as a basic accessible service, yet the traffic is increasing, and acquiring new spectrum or cell sites to serve growing traffic is expensive.

AI-RAN’s core mission is maximizing the ROI for service providers by delivering the following key benefits to CoSPs: 

• Maximizing the utilization of their infrastructure, resulting in lower TCO.
• New monetization opportunities via hosted AI services, resulting in increased revenues.
• Improving spectral efficiency, energy efficiency, and performance using AI techniques embedded into radio signal processing.
• Future-proofing their infrastructure investments.

For CoSPs, AI-RAN is transformational because it:

• Delivers the highest cell density, throughput, and spectral efficiency for RAN, while ensuring carrier-grade and deterministic performance of RAN workloads.
• Enables CoSPs to dynamically assign unused RAN capacity for AI workloads, increasing the overall ROI through new monetization opportunities.
• Enhances the energy efficiency of a fully loaded system.
• Future-proofs the CoSP’s infrastructure investments with the ability to deploy ongoing improvements (RAN and AI) via new software releases, using a continuous integration/continuous delivery (CI/CD) approach on the shared accelerated hardware platform, including a future software upgrade to 6G.

The key building blocks for AI-RAN include the following:

• Multipurpose Cloud-Native Infrastructure: Supports any RAN, any cloud-native network function (CNF), any business support systems/operations support systems (BSS/OSS)-based internal AI workloads, or any external AI workloads.
• Software-Defined Architecture Using COTS Servers: No fixed function or purpose-built hardware.
• General-Purpose Acceleration: Can accelerate multiple workloads.
• Multi-Tenant- and Multi-Workload-Capable Design: Both AI and RAN as first-class citizens, each with deterministic performance as per requirements.
• Scalable and Fungible Infrastructure: Same servers can be used for any workload optimally with software reconfiguration and same homogenous infrastructure can be used for any deployment scenario, including Centralized RAN (C-RAN), Distributed RAN (D-RAN), and massive multiple-input multiple-output (mMIMO) variants, not requiring bespoke infrastructure for each use case.

NVIDIA provides an AI-RAN reference architecture, built with NVIDIA MGX™ GH200 or Grace Blackwell-based platforms and NVIDIA BF3, CX7/CX8 NICs, and Spectrum-X™ switch fabric, which is fully programmable with a software upgrade and can accommodate the evolving landscape of AI applications and the evolution to future 6G networks.

NVIDIA has worked with our partners to define, build, and validate NVIDIA Cloud Partners (NCP) Telco Reference Architecture (RA). The goal of this RA is to create a blueprint that can drive rapid deployment of AI-RAN for the CoSP customers. The key elements of this RA include: 

• Standard rack-mounted Telco servers
• NVIDIA MGX GH200-based original equipment manufacturer (OEM) server platforms
• Spectrum-X-compliant fronthaul aggregation switches and network interface controllers (NICs)—Spectrum switches and NVIDIA BlueField® 3 (BF3) data processing units (DPUs)—support timing requirements for RAN fronthaul and optimized AI Ethernet capabilities