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/

https://ai-ran.org/

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