AI-RAN Market

Overview

The global AI-RAN Market was valued at USD 3.1 billion in 2025 and is projected to reach USD 33.4 billion by 2034, growing at a CAGR of 30% during the forecast period (2026–2034). The market is driven by operator demand for higher spectral and energy efficiency on costly spectrum, rising uplink traffic from AI-enabled devices, and the opportunity to host inference workloads on idle base station capacity. Mobile operators, enterprises, and neutral host providers are the main buyers of AI-RAN hardware, software, and services. The market is shifting from conventional, purpose-built basebands running fixed, rule-based algorithms toward software-defined platforms where trained models adjust scheduling, beamforming, and power settings in real time. Vendors are moving from selling hardware boxes toward selling software features, and operators are testing whether GPUs, CPUs, or custom silicon offer the best return per site.  Government initiatives such as the US National Telecommunications and Information Administration's Public Wireless Supply Chain Innovation Fund, which finances Open RAN software and test projects, and South Korea's Hyper-AI Network programme, a KRW 17.2 billion government-backed pilot of AI-RAN at industrial sites, are accelerating field validation and domestic ecosystem development. By region, Asia-Pacific held the largest share of the market in 2025, supported by early operator adoption in Japan and South Korea and large-scale 5G-Advanced networks in China. North America is expected to be the fastest-growing region during the forecast period, driven by operator field trials of GPU-based platforms, deep supply of accelerated computing, and rising investment in 6G readiness across the United States.

Market Size & Share

Market Dynamics

KEY MARKET TREND

Shift from Rule-Based Optimization to AI-Native Radio Intelligence Emerging as a Transformational Trend

• Radio vendors are replacing static, rule-based algorithms with trained models for link adaptation, scheduling, and beamforming. These models learn from live network conditions and adjust settings within milliseconds, helping operators raise capacity from spectrum they already hold. The approach is moving from laboratory trials to software releases that operators can activate across installed sites.
• Neural receivers, digital twins, and reinforcement learning agents are being tested in the physical layer and in the near real-time controller. Interfaces defined by the O-RAN Alliance let third-party applications run these models next to vendor software. This keeps AI innovation open to developers and reduces the risk of lock-in to a single supplier.
• Competition is forming around three compute choices, namely GPUs, general-purpose CPUs, and custom chips. Each camp argues for a different cost and power profile, and operators are running comparative field tests before committing at scale. Suppliers that can run the same AI software across several processor types hold an advantage in multi-vendor networks.
• Samsung Electronics and KT Corporation announced that they had validated AI-based RAN optimization technology on KT's commercial network, with stable, uninterrupted service across real-world environments. It was the first demonstration of the technology on a live commercial network, following simulation-based verification completed in June. The companies plan to extend validation to more commercial networks.

KEY MARKET DRIVER

Operator Pressure to Raise Network Efficiency and Monetize Edge Compute Is the Key Driver

• Mobile data traffic keeps rising while operator revenue grows slowly, so operators need more capacity from the spectrum and sites they already own. AI models that improve scheduling and beamforming can lift throughput without new radio hardware, which lowers the cost per delivered bit. This efficiency case is the main reason operators are funding AI-RAN pilots.
• Uplink demand from AI assistants, video uploads, and connected devices is growing faster than traditional planning assumed. Networks built for downlink-heavy streaming struggle with the latency and uplink capacity these applications need. AI-RAN platforms respond by predicting channel conditions and reserving resources before congestion appears. This matters most in dense urban cells where congestion affects many users.
• Base stations carry compute capacity that sits idle during off-peak hours. Operators see an opening to host edge inference for enterprise customers on the same accelerated servers that run radio functions, creating a second revenue line from existing sites. This business case gives finance teams a clearer reason to approve accelerated compute at cell sites.
• NVIDIA announced a USD 1 billion investment in Nokia to add NVIDIA-powered, commercial-grade AI-RAN products to Nokia's RAN portfolio. T-Mobile US agreed to collaborate with both companies on testing, with field evaluations beginning in 2026. Nokia plans to embed NVIDIA ARC-Pro in its new AI-RAN solution, with Dell Technologies supplying the servers.

KEY MARKET OPPORTUNITY

Software Subscription Models and Private Network Adoption Create Significant Market Opportunity

• Mid-sized and regional operators are an untapped segment, because many lack in-house AI teams and prefer validated, ready-to-deploy packages. Suppliers that offer reference designs, managed services, and remote optimization can win this demand. Success here depends on clear performance guarantees and simple pricing that fits smaller capital budgets. Early reference deployments help build trust in this group.
• Enterprises building private 5G networks in factories, ports, and warehouses need low-latency links and on-site inference for robots and vision systems. AI-RAN allows one edge platform to serve both connectivity and AI processing, reducing equipment at each site. This opens a new customer group for integrators, managed service firms, and infrastructure suppliers.
• Every AI model placed in a live radio network needs testing and validation first, which creates demand for digital twins, scenario generators, and automated test workflows. Test vendors, system integrators, and cloud platform providers can earn recurring revenue from this verification work. Investor interest is rising as the work scales with each new model release.
• SoftBank Corp. stated that it will implement its AITRAS converged AI-RAN solution across its own commercial network and offer it to telecom operators globally from 2026 and beyond. In March 2025, SoftBank and Fujitsu completed the implementation of Central Unit functions on the NVIDIA Grace CPU Superchip platform as a step toward commercialization.

AI-RAN Market Size, 2025-2034 (USD Billion)

Segmentation Analysis

Analysis by Architecture

AI-for-RAN held the largest market share in 2025 because operators can adopt it as a software upgrade on the radio and baseband equipment already installed in their networks. The category uses trained models to improve scheduling, link adaptation, beamforming, and energy saving, which gives measurable gains in capacity and power use without a new site visit. Alignment with 3GPP work on AI for the radio network and the O-RAN Alliance's intelligent controller framework gives buyers confidence in interoperability. Procurement teams also find the business case easy to document, since results can be compared with current performance on the same cells and traffic profile.

Converged AI-RAN, which the AI-RAN Alliance describes as AI-and-RAN, is projected to grow at the fastest CAGR during the forecast period as operators look for ways to run radio and AI workloads on the same accelerated infrastructure. This design raises the use of compute at each site and opens a path to sell edge inference to enterprises, which improves the return on capital spent on accelerators. Rising uplink demand from AI-enabled devices adds weight to the case, and software-defined designs allow the same platform to move from 5G-Advanced to early 6G. Vendors are expanding reference designs and orchestration tools that balance the two workloads in real time.

Architecture categories include

• AI-for-RAN (Dominating Segment)
• Converged AI-RAN (Highest CAGR Segment)
• AI-on-RAN

Analysis by Component

Hardware held the largest market share in 2025 because every AI-RAN deployment needs accelerated compute before any software gain can be realized. Spending is concentrated on baseband processors, inline accelerators, GPU-equipped edge servers, and the ruggedized, power-efficient systems required at cell sites and aggregation points. Telecom-grade requirements such as NEBS compliance and strict temperature and power limits raise the cost and engineering effort per unit compared with enterprise servers. Early deployments are also being built ahead of traffic, since operators prefer to install capable platforms once and upgrade functions later through software.

Software is projected to grow at the fastest CAGR during the forecast period as vendors shift from selling boxes to selling AI models, orchestration layers, and feature subscriptions. Once accelerated hardware is in place, each new model for scheduling, positioning, energy saving, or automation can be licensed and updated without new equipment, which improves margins and gives operators a steady upgrade path. Open interfaces allow third-party applications to run on the controller, widening the supplier base. Continuous model retraining on live network data also creates recurring revenue and encourages longer contracts between operators and vendors.

Component categories include

• Hardware (Dominating Segment)
• Software (Highest CAGR Segment)
• Services

Analysis by Compute Platform

Custom Silicon Platforms held the largest market share in 2025 because most live radio networks still run on purpose-built baseband chips that vendors have tuned over several product generations. These chips deliver predictable latency and low power per cell, which matters for operators managing thousands of sites. Vendors are adding neural network accelerators and AI inference blocks to existing silicon, allowing AI features to be switched on through software. Operators with large installed bases favour this route because it protects earlier investments, reduces integration risk, and keeps performance consistent with the rest of the network.

GPU-Accelerated Platforms are projected to grow at the fastest CAGR during the forecast period as operators test a single, programmable compute layer for radio processing and AI inference. GPUs offer large parallel processing capacity, a mature developer ecosystem, and a software upgrade path from 5G-Advanced toward 6G without replacing hardware. Server makers are releasing telecom-grade systems built around these accelerators, which lowers entry barriers for operators and integrators. Falling cost per unit of compute and growing enterprise demand for edge inference improve the economics, even though power use per site remains a key point operators are examining.

Compute Platform categories include

• GPU-Accelerated Platforms (Highest CAGR Segment)
• CPU-Based Platforms
• Custom Silicon Platforms (Dominating Segment)

Analysis by End User

Mobile Network Operators held the largest market share in 2025 because they own the radio networks where AI-RAN is deployed and carry the cost of spectrum, sites, and energy. Pressure to improve network efficiency and customer experience while keeping capital spending in check makes AI-driven optimization attractive. Operators run structured trials with several suppliers, which feeds early purchase orders for software features and accelerated compute. Participation in bodies such as the AI-RAN Alliance and the O-RAN Alliance lets operators shape requirements, test results, and procurement criteria, and this keeps them at the centre of demand.

Enterprises are projected to grow at the fastest CAGR during the forecast period as factories, ports, logistics hubs, and campuses adopt private 5G networks that need low latency and on-site AI processing. AI-RAN lets one edge platform serve connectivity and inference for robots, vision systems, and autonomous vehicles, which reduces equipment at each site. Growing interest in industrial automation and physical AI encourages companies to buy managed solutions from integrators and vendors rather than build networks themselves. Spectrum availability for private use in many countries lowers the barrier to entry, and managed service contracts give enterprises predictable costs, which further supports this demand.

End User categories include

• Mobile Network Operators (Dominating Segment)
• Enterprises (Highest CAGR Segment)
• Neutral Host Providers

By Region

AI-RAN Market Share 2025, (CAGR)

North America

30%

South America

xx%

Europe

xx%

Middle East Africa

xx%

Asia Pacific

42%

Asia-Pacific held the largest market share in 2025, supported by early commercial work in Japan and South Korea and the scale of 5G-Advanced networks in China. China leads the region as large operators and domestic vendors add AI computing boards to existing base stations, while Japan and South Korea focus on GPU-based and CPU-based virtualized platforms that run AI and radio workloads together. India is preparing for wider adoption as operators densify 5G sites and look for ways to lift capacity without large capital outlays. National 6G research programmes across the region treat AI-native radio as a core design goal, and a strong electronics supply base shortens the path from trial to deployment.

North America is projected to grow at the fastest CAGR during the forecast period, driven by operator field trials of GPU-based AI-RAN, deep supply of accelerated computing, and rising investment in 6G readiness across the United States. Large operators are testing whether shared infrastructure can carry both radio and inference workloads, and the results guide purchase decisions across the region. An active Open RAN software ecosystem and strong corporate funding encourage local suppliers to build AI-ready products. Canada is seeing operator interest in software-based AI features that run on existing radios, while Mexico is expected to follow as 5G coverage widens.

Countries and Regions Covered

Asia-Pacific (Dominating Region)

• China (Largest Country Market)
• India
• Japan
• South Korea (Fastest-Growing Country Market)
• Rest of Asia-Pacific

North America (Fastest Growing Region)

• United States (Largest Country Market)
• Canada
• Mexico

Europe

• Germany (Largest Country Market)
• France
• United Kingdom
• Italy
• Rest of Europe

Latin America

• Brazil (Largest Country Market)
• Chile (Fastest-Growing Country Market)
• Rest of Latin America

Middle East & Africa

• Saudi Arabia (Largest Country Market)
• United Arab Emirates (Fastest-Growing Country Market)
• Rest of Middle East & Africa

Market Share

The AI-RAN Market is consolidated, with a small group of system vendors, including Nokia, Ericsson, Samsung Electronics, Huawei, and ZTE, controlling radio software and baseband relationships with operators. A second layer of silicon and server suppliers, led by NVIDIA, Intel, Marvell, and Dell Technologies, shapes the compute choices behind those systems. Success depends on proven performance gains in live networks, support for open interfaces, power efficiency, and the ability to run the same AI software across different processors. Leading companies are prioritizing subscription software, partnerships with chipmakers and server makers, and joint field trials with operators. Test and validation specialists are gaining relevance as each AI model needs verification before deployment. Acquisitions remain limited, while minority investments and co-development agreements are the main route to secure technology access.

Key Players

• NVIDIA Corporation (US)
• Nokia Corporation (Finland)
• Ericsson (Sweden)
• Samsung Electronics Co., Ltd. (South Korea)
• Huawei Technologies Co., Ltd. (China)
• ZTE Corporation (China)
• SoftBank Corp. (Japan)
• Intel Corporation (US)
• Marvell Technology, Inc. (US)
• Dell Technologies Inc. (US)
• Super Micro Computer, Inc. (US)
• 1Finity (Japan)
• Quanta Cloud Technology (Taiwan)
• VIAVI Solutions Inc. (US)
• Keysight Technologies, Inc. (US)
• Mavenir (US)

Recent Market Developments

• In March 2026, NVIDIA announced a strategic partnership with Marvell Technology, together with a USD 2 billion investment, to connect Marvell to the NVIDIA AI factory and AI-RAN ecosystem and to work on telecom infrastructure using NVIDIA Aerial AI-RAN for 5G and 6G. The deal links a major RAN silicon supplier to the GPU ecosystem and widens the choice of compute components for radio vendors.
• In June 2026, Ericsson introduced AI in RAN, a software subscription that brings telco-grade AI models into basebands and radios on Ericsson 5G Advanced, across purpose-built and Cloud RAN platforms. The first features, including an AI-native scheduler for link adaptation, became available in the second quarter, and Ericsson reports more than 15 deployments and trials. The offer shows that AI-RAN revenue can come from software on installed hardware.
• In June 2026, Amdocs, 1Finity, and Supermicro completed a live, field-validated AI-RAN blueprint that runs 1Finity Open vRAN software on Supermicro Arm-based servers with NVIDIA GH200 Grace Hopper Superchips on Red Hat OpenShift. The blueprint shows a multivendor route to AI-assisted optimization and anomaly detection on a unified platform that can later host edge AI services.
• In July 2026, Nokia launched its commercial AI-RAN platform, built on its anyRAN software and NVIDIA's Aerial AI-RAN platform and available for 5G and 5G-Advanced with a software upgrade path to 6G and O-RAN compliance. Nokia has set a target of more than 100% spectral efficiency gains by 2028, with pilots at the end of 2026 and commercial availability in 2027. The launch gives operators a GPU-based roadmap from an established radio vendor.

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