AI-RAN Reality Check: hype vs hesitation, shaky business case, no specific definition, no standards?
Introduction:
The narrative surrounding “AI-RAN” — a term thrust into the spotlight by Nvidia — may have left many believing that boatloads of GPUs are already powering baseband compute in RAN equipment across the world’s seven million mobile sites. In truth, the reality is far more nascent.
Among major RAN vendors, Nokia stands alone in adapting baseband software for GPU acceleration. Yet even Nokia does not anticipate commercial readiness until late 2026, as confirmed by its Chief Technology Officer, Pallavi Mahajan, during the company’s MWC press conference earlier this year. For now, no operator has announced a commercial deployment — despite the buzz around trials.
Early Movers, Limited Momentum:
Much of the current AI-RAN activity centers on two operators: T-Mobile US and Japan’s SoftBank. At MWC, T-Mobile’s Executive Vice President of Innovation and ex-CTO, John Saw, acknowledged the limited availability of deployable solutions, quipping that he hoped Nokia would deliver an AI-RAN product within the year. Nokia CEO Justin Hotard quickly assured him that such a milestone was indeed on track.
Still, the debut of a GPU-based RAN stack does not imply an imminent large-scale rollout. Without tangible network performance or cost advantages over existing virtualized or disaggregated RAN approaches, operators are unlikely to move past controlled trials.
SoftBank, while often positioned as an AI-RAN pioneer, remains cautious. As Ryuji Wakikawa, Vice President of its Advanced Technology Division, outlined last year, the operator aims to deploy only a handful of AI-RAN sites over the next fiscal cycle. Transitioning from testing to carrying live commercial traffic, he emphasized, demands a significant maturity leap in quality and feature completeness.
Beyond Hype: Limited Commercial Engagement:
Elsewhere, Indonesia’s Indosat Ooredoo Hutchison (IOH) was heralded in 2025 as the first operator in Southeast Asia pursuing AI-RAN. More than a year later, authoritative sources indicate IOH’s work remains confined to its research facility in Surabaya, with no near-term plans for GPU investment at cell sites until measurable value is demonstrated.
The challenge for Nokia — and for GPU-backed AI-RAN broadly — is convincing operators that general-purpose accelerators offer sufficient performance or efficiency gains for most RAN workloads. T-Mobile and SoftBank continue evaluating both Nokia and Ericsson, whose AI-RAN philosophies diverge sharply. Nokia is developing GPU-based baseband software, while Ericsson maintains its focus on custom silicon and CPU architectures.
Divergent Architectures and Use Cases:
Ericsson contends that no core RAN performance enhancements intrinsically require GPUs. Its ongoing collaboration with Nvidia leverages the latter’s Grace CPU technology rather than its GPU portfolio, reserving GPU acceleration only for compute-intensive functions like forward error correction (FEC).
If Ericsson’s premise holds, GPUs in the RAN become justifiable only when supporting AI inference workloads. Even then, inference at every radio site remains improbable. A more incremental strategy — deploying GPUs selectively at edge locations where AI workloads justify their cost — may prove more practical.
This modular approach aligns with existing virtual RAN deployments based on Intel CPUs, which already include native FEC acceleration. “It is an off-the-shelf card that you can slide right into an HPE or Dell or Supermicro server,” said Alok Shah, the vice president of network strategy for Samsung Networks. “That gets you the edge functionality you are looking for.”
Rethinking the Economic Case for AI RAN:
Initially, Nvidia positioned GPUs for AI-RAN as viable only if broadly utilized for AI inference across the RAN. Following its strategic alignment with Nokia, however, the company has softened its stance — now suggesting that appropriately sized, power-efficient GPUs could make sense even when dedicated solely to baseband computation.
For now, the global RAN landscape remains far from GPU-saturated. AI-RAN remains an exploratory frontier — one testing not only the technical feasibility of GPUs at the edge, but also the economic/business case rationale for re-architecting a trillion-dollar telecom infrastructure around them.
The AI models suitable for RAN environments must be compact and efficient, far slimmer than those that drive data center-scale AI. There’s no room for the massive, parameter-heavy neural networks that justify a GPU’s cost or energy appetite. In that light, a GPU looks less like a breakthrough and more like a mismatch — a chainsaw brought to a task better handled with a sharp pair of scissors.
Evaluating the Case for AI-RAN Acceleration:
The central question is whether GPUs can deliver meaningful benefits over custom silicon or conventional CPUs for RAN compute. Ericsson’s engineers argue that AI models deployed at the RAN must remain relatively lightweight, with far fewer parameters than those used in large-scale data centers. Excessive model complexity could introduce signaling delays unacceptable in real-time radio environments. In this context, deploying a GPU for such workloads might seem disproportionate — a high-powered tool for a low-demand task.
The most compelling defense of GPU-based RAN acceleration came from Ronnie Vasishta, Nvidia’s Senior Vice President for Telecom, who told Light Reading last summer, “The world is developing on Nvidia.” His point underscores a shift in semiconductor economics: the cost and risk of building dedicated silicon for a mature and shrinking RAN market make general-purpose processors — supported by large-volume ecosystems — increasingly attractive alternatives.
Intel’s difficulties further illustrate this dynamic. Despite $53 billion in revenue during 2025, the former microprocessor king barely broke even despite $53 billion in revenue, following a $19 billion loss the previous year. A major restructuring cut its headcount by nearly 24,000, and its planned spinoff of the Network and Edge division — serving telecom infrastructure customers — was ultimately abandoned in December. Nvidia, the world’s most valuable company, may be eager to step into that space — but the economic logic seems upside down. Wireless network operators are looking to reduce costs, not import data center economics into the RAN.
Ecosystem or Echo Chamber?
Nvidia’s Aerial platform and CUDA-based software ecosystem do present a compelling story: open infrastructure, modular APIs, and space for smaller developers to innovate alongside giants like Nokia. On paper, it’s an alluring image of democratized RAN software. In practice, it ties the industry even more tightly to a vertically integrated, proprietary ecosystem.
Nokia appears comfortable with that trade-off. Nokia CTO Pallavi Mahajan’s recent blog post framed AI-RAN as a vehicle for “software speed and innovation.” He added, “Nokia’s AI-RAN initiative begins with a simple observation: AI is changing not only how networks are operated, but also the nature of the traffic they carry. AI workloads have already reached massive scale, with mobile devices accounting for more than half of AI interactions. Large language model interactions introduce richer uplink flows and burstier patterns as devices continuously send context to models.”
Indeed, that me be true someday. But for now, most wireless network operators need stable, cost-efficient networks, not AI-driven complexity or GPU-level power draw.
Image Credit: Nokia
Conclusions:
The uncomfortable truth is that AI-RAN feels more like a vendor-driven experiment than an operator-driven demand. Until someone proves that GPUs in the RAN deliver a measurable payoff — in performance, cost, or operational simplicity — the whole concept risks joining the long list of telecom “game-changers” that never made it past the trial stage. The hype cycle is predictable; the economics are not. Unless that equation changes, the real intelligence may be knowing when not to deploy AI RAN.
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In a Substack post today, Sebastian Barros writes: What Does AI-RAN Even Mean?
Despite the crazy hype, there is no definition for AI-RAN. Today it is at best a vibe, a dangerous reality for an industry that moves on strict standards that are currently completely absent.
The AI RAN hype is crazy right now. But despite the endless talk and vendor announcements, there is no actual technical definition of what it even means. As wild as it sounds for an industry built on strict 3GPP and O-RAN standards (those are specs- not standards), AI RAN is currently just a vendor interpretation designed to move hardware. Moreover, telecom has been using AI in the RAN before it was even cool. In fact, we were among the first industries to use neural networks in signal processing back in the 80s.
The problem is that treating AI-RAN as a marketing narrative rather than a rigid standard actively stalls progress. When the definition of AI-RAN is as different as night and day depending on which OEM you ask, it becomes impossible for any Telco to accurately model TCO or make solid CAPEX decisions.
Editor Notes:
- ITU-R’s IMT-2030 framework (ITU-R Recommendation M.2160-0 for IMT-2030) calls for an AI-native new air interface and AI-enhanced radio networks, but does not mention Nokia’s AI RAN.
- 3GPP Release 18 and later have study/work items on AI/ML for RAN functions such as energy saving, load balancing, mobility optimization, and AI/ML on the RAN air interface, but again no specifics have been discussed let alone agreed upon.
- 3GPP Release 19 continues and expands this work, with reporting that it builds on Release 18’s normative work and adds new AI/ML-based use cases for NG-RAN. In other words, 3GPP does have AI-RAN-related specs in progress and some normative features, but they are distributed across multiple RAN work items rather than packaged as one standalone “AI RAN” specification.
- AI RAN Alliance “is dedicated to driving the enhancement of RAN performance and capability with AI.” However, they’ve not yet produced any implementable specifications for AI RAN. Yet there are only four carriers that are “executive members“: Vodafone, T-Mobile, and SK Telecom, and Softbank (which is a conglomerate).
In Japan, NTT Docomo holds the largest cellular market share, with KDDI second, followed by SoftBank and the rapidly expanding Rakuten Mobile.
References:
https://www.lightreading.com/5g/ai-ran-lots-of-talk-little-action-no-guarantees
https://www.nokia.com/blog/ai-ran-bringing-software-speed-innovation-into-the-radio-network/
Ericsson goes with custom silicon (rather than Nvidia GPUs) for AI RAN
Dell’Oro: RAN Market Stabilized in 2025 with 1% CAG forecast over next 5 years; Opinion on AI RAN, 5G Advanced, 6G RAN/Core risks
Dell’Oro: Analysis of the Nokia-NVIDIA-partnership on AI RAN
RAN silicon rethink – from purpose built products & ASICs to general purpose processors or GPUs for vRAN & AI RAN
Dell’Oro: AI RAN to account for 1/3 of RAN market by 2029; AI RAN Alliance membership increases but few telcos have joined
Dell’Oro: AI RAN to account for 1/3 of RAN market by 2029; AI RAN Alliance membership increases but few telcos have joined
3 thoughts on “AI-RAN Reality Check: hype vs hesitation, shaky business case, no specific definition, no standards?”
Leave a Reply to Jack Reed Cancel Reply
This article is a well-supported, evidence-backed critical analysis that aligns closely with observable AI RAN industry data and market trends.
Your “shaky business case” claim is directly supported: AI-RAN requires large new investments while wireless network operators are in a capex contraction cycle
–>That’s not just opinion—that’s macro-level evidence backing your thesis.
“Hype vs hesitation” is empirically visible: AI-RAN alliances exist but lack deliverables Vendor demos (e.g., Ericsson/AT&T AI link adaptation) are still trial-stage improvements, not systemic transformation!
Your AI-RAN skepticism is not isolated—it fits into a broader, evidence-based narrative
–> Telecom innovation cycles are overpromised and underdelivered!
The AI boom didn’t politely wait for the IMT 2030/6G hardware cycle to begin. Instead, it invaded the 5G digestion phase. Telcos are no longer waiting for 6G. Instead, they are demanding AI-driven software to juice existing 5G networks while ruthlessly automating operations to kill the very managed services they once outsourced.
This has led to several important telco operations changes:
-Optimizing Existing Networks with AI: Telcos are now demanding AI-driven software to maximize the performance and efficiency of their current 5G (and even 4G) networks. This means using AI for things like predictive maintenance, dynamic resource allocation, real-time traffic management, and optimizing signal quality. The goal is to “juice” or extract more value and capability from the infrastructure they already have, rather than waiting for the next generation of hardware.
-Automation of Operations: AI is enabling telcos to automate many operational tasks that were previously manual or handled by outsourced managed services. This includes automated fault detection and resolution, network provisioning, customer support, and even complex network orchestration.
-Killing Outsourced Managed Services: By automating these operations with AI, telcos can reduce their reliance on third-party managed service providers. This allows them to bring more control in-house, reduce operational costs, and potentially create new revenue streams by offering these advanced capabilities themselves. It’s a move to optimize their own business rather than relying on external partners for efficiency.
In essence, the AI boom has forced telcos to become more agile and innovative, shifting their strategy from a hardware-centric, incremental upgrade model to a software-driven, AI-first approach that aims to optimize, automate, and ultimately transform their operations and monetization strategies now, rather than later [thefastmode.com]. This means rethinking their fundamental operating models, integrating AI as a core structural component, and focusing on immediate, tangible impacts rather than aspirational, broad AI programs [thefastmode.com]. The complexity of modern networks, with thousands of KPIs and millions of parameters, has outstripped human ability to manage them, making AI essential for effective operation
NVIDIA and Marvell Technology have formed a partnership designed to link Marvell into the NVIDIA AI factory and the wider AI-RAN ecosystem using the NVIDIA NVLink Fusion platform. This collaboration gives customers who build on NVIDIA architectures greater choice and flexibility when developing next-generation hardware environments.
Backing this hardware integration is a massive financial endorsement, as NVIDIA has invested $2 billion into Marvell. Financial analysts monitoring the telecoms sector often look for concrete monetary commitments to validate hardware alliances. This massive injection of capital guarantees that Marvell has the resources to scale its production of custom silicon and networking components.
Beyond the direct capital injection, the two technology giants plan to collaborate extensively on silicon photonics technology. Advancements in optical interconnects directly address the growing need for high-speed, low-latency networking architectures necessary to support heavy AI workloads at the edge.
The foundation of this joint effort relies on the NVIDIA NVLink Fusion platform, a rack-scale architecture that lets developers create semi-custom AI setups using the existing NVLink ecosystem.
Within this arrangement, Marvell takes responsibility for supplying custom XPUs alongside scale-up networking gear that maintains strict compatibility with NVLink Fusion. NVIDIA, meanwhile, will deliver the underlying support technologies, specifically the Vera CPU, ConnectX NICs, BlueField DPUs, Spectrum-X switches, the NVLink interconnect itself, and the rack-scale AI compute.
This specific division of hardware components creates a heterogeneous AI infrastructure for engineers building custom XPUs, ensuring complete compatibility with NVIDIA systems. Operators can then seamlessly integrate their edge deployments with NVIDIA’s GPU, LPU, networking, and storage platforms, tapping into the broader global supply chain ecosystem that NVIDIA maintains. The inclusion of BlueField DPUs, for instance, allows operators to offload heavy security and networking tasks from the main processors, freeing up valuable compute cycles for revenue-generating AI applications.
Matt Murphy, Chairman and CEO of Marvell, said: “Our expanded partnership with NVIDIA reflects the growing importance of high-speed connectivity, optical interconnect, and accelerated infrastructure in scaling AI.
“By connecting Marvell’s leadership in high-performance analog, optical DSP, silicon photonics, and custom silicon to NVIDIA’s expanding AI ecosystem through NVLink Fusion, we are enabling customers to build scalable, efficient AI infrastructure.”
The ability to deploy specialized compute nodes within the Radio Access Network changes the economic model of cellular sites. By partnering to turn the telecommunication network into a distributed AI infrastructure using the NVIDIA Aerial AI-RAN framework for 5G and 6G, operators can host enterprise workloads directly at the cell tower.
This edge capability establishes new revenue streams entirely disconnected from consumer smartphone subscriptions. Enterprises require low-latency processing for automated manufacturing, autonomous logistics, and real-time video analytics. Network operators can lease this edge compute capacity to enterprises, thereby driving up Average Revenue Per User (ARPU) and reducing enterprise churn.
Deploying private 5G solutions provides another direct application for this newly-announced infrastructure. The integration of Marvell’s custom silicon and NVIDIA’s rack-scale compute equips operators with the precise hardware combination necessary to secure highly lucrative private networking contracts. The data never leaves the factory floor, satisfying heavy data sovereignty and compliance regulations.
Jensen Huang, Founder and CEO of NVIDIA, commented: “The inference inflection has arrived. Token generation demand is surging, and the world is racing to build AI factories. Together with Marvell, we are enabling customers to leverage NVIDIA’s AI infrastructure ecosystem and scale to build specialized AI compute.”
Telecom operators cannot drop rack-scale AI compute into existing mobile switching centers without encountering friction. Integrating NVLink Fusion hardware requires extensive coordination with legacy Operations Support Systems (OSS) and Business Support Systems (BSS). Legacy BSS/OSS platforms were primarily designed to meter voice minutes and megabytes, not continuous API calls or dynamic edge compute provisioning. Overhauling these billing engines to handle AI-RAN monetization represents a massive and multi-year undertaking.
Furthermore, spectrum management becomes increasingly complex under this model. Running multi-tenant AI workloads concurrently with high-priority 5G baseband processing demands precise resource isolation. Directors often estimate that ensuring exact resource isolation can consume ten percent of edge computing overhead.
Operators must navigate multi-cloud environments, ensuring that containerized network functions interoperate smoothly with enterprise AI applications sharing the same physical silicon. While Marvell’s XPUs and NVIDIA’s Vera CPUs provide the processing variety needed for these distinct tasks, the software orchestration layer remains a daunting hurdle for IT directors to clear.
When operators expose these new edge capabilities through network APIs, they invite third-party developers to write applications integrated directly with the radio network. However, creating a developer-friendly API portal demands heavy investment in software infrastructure.
Legacy systems frequently lack the agility to authenticate, meter, and bill thousands of concurrent API requests originating from enterprise software. Upgrading these backend systems requires navigating a complex web of vendor lock-in and customized software deployments. IT directors face the difficult task of modernizing the billing infrastructure without causing service interruptions for the existing subscriber base.
https://www.telecomstechnews.com/news/nvidia-and-marvell-alliance-ai-ran-infrastructure/