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Nokia’s AI-RAN approach couples near-term spectral-efficiency gains with a longer-term architectural shift toward shared accelerated compute
As telecoms operators look to leverage the AI inflection point as a way to rethink operations and grow revenues, Nokia sees the significance of AI-RAN as more than a way to make the radio access network perform better. The larger proposition is that AI changes what the RAN itself needs to become.
That distinction puts Nokia toward the more architectural end of the emerging AI-RAN spectrum. The immediate business case remains grounded in familiar operator priorities of spectral efficiency, capacity, power consumption and cost per bit. But Nokia is using those near-term benefits to support a broader transition from hardware-defined RAN infrastructure toward a programmable platform where radio and AI workloads increasingly share compute resources.
The company describes its AI-RAN platform as available for 5G and 5G-Advanced, software-upgradable toward 6G and fully Open RAN compliant. It supports three deployment paths: adding AI-accelerated capacity to existing AirScale deployments, introducing dedicated accelerated AI-RAN nodes, or deploying cloud-native AI-RAN on COTS infrastructure. The common denominator is Nokia’s anyRAN software stack and a shared software roadmap.
The range of options matters because Nokia is simultaneously advocating an architectural shift and acknowledging brownfield reality. Operators cannot forklift networks every time the compute model changes. Aji Ed, vice president and head of AI-RAN and Cloud RAN at Nokia, framed that pragmatism around placement of compute rather than attachment to a particular processor architecture. “It’s about bringing the right compute at the right place, and with the right configuration,” Ed said.
That creates an evolutionary path. Existing AirScale deployments can gain AI capability through a plug-in card; operators requiring greater AI headroom can use an accelerated AI-RAN node; and workloads demanding maximum flexibility can move onto cloud-native infrastructure. Nokia says all three approaches lead toward the same 6G destination.
AI-RAN starts with the economics of spectrum
The near-term rationale is much less abstract: operators have spent billions acquiring spectrum, making any improvement in bits per second per hertz economically meaningful.
“We bring twice the network capacity, twice the spectral efficiency compared to what we have today,” Ed said. Nokia has reported more than 20% efficiency gains from existing AI-driven radio innovations and sees advanced AI models operating at radio timescales pushing spectral efficiency toward more than 2x conventional architectures.
Those gains will not arrive uniformly. Ed emphasized that different algorithms apply to different radio configurations, traffic patterns and network conditions. AI-based multi-user MIMO pairing, channel estimation and link adaptation, for instance, can produce different results depending on whether a site uses TDD massive MIMO, FDD or other configurations. The highest returns may also come at congested sites where additional capacity has the greatest economic value.
But the more important architectural argument is what happens after the first efficiency gains are realized. Traditional RAN innovation remains tied partly to silicon refresh cycles. Nokia’s AI-RAN proposition moves more of that innovation into software, allowing new algorithms and models to change network performance without corresponding hardware replacements.
Ed described the objective as a “fully software defined architecture, which runs at the software speed, not just dependent on the silicon speed.” That is the bridge from AI for RAN toward the broader AI and RAN vision in which connectivity and AI workloads use shared infrastructure.
From spectral efficiency to the AI economy
This is where Nokia’s AI-RAN strategy becomes more ambitious. The RAN is among the most geographically distributed compute footprints in existence. If operators can combine radio processing with AI inference on common accelerated infrastructure, that footprint potentially becomes an edge AI platform rather than infrastructure dedicated solely to connectivity.
Nokia describes that end state as a programmable, AI-native RAN in which RAN functions and AI workloads operate on a shared computing foundation. Generative, agentic and physical AI could also change traffic itself — increasing uplink requirements, demanding lower latency and creating more need for distributed processing.
That direction aligns with a broader shift in telecom AI. The central challenge is no longer demonstrating that AI can perform useful tasks; it is introducing probabilistic systems into networks engineered around deterministic behavior, reliability and accountability. The practical industry response is to phase AI into bounded domains with appropriate guardrails rather than wait for some future moment when autonomy becomes risk-free.
Ed makes a similar distinction between demonstration and deployment. “Having a demonstratable capability is one thing versus deploying this capability in a commercial grade scale,” he said, noting that models may require continuous fine-tuning and even adaptation to site-specific conditions.
And accelerated computing has to satisfy basic operator economics. Ed reduced the test to “the power, the price, and the performance”: power and price need to remain comparable while performance improves.
The next problem is orchestration. Shared AI-RAN infrastructure adds another resource-management layer: operators must coordinate onboarding, lifecycle management and allocation of both AI and RAN workloads across heterogeneous distributed infrastructure. Ed acknowledged that this creates additional complexity even as Nokia works with partners on AI-RAN orchestration.
That tension captures Nokia’s larger AI-RAN bet. Spectral efficiency provides the business case to begin. Software-defined infrastructure creates a mechanism for continuous improvement. Accelerated computing expands what can run in the RAN. And orchestration ultimately determines whether operators can turn those capabilities into a manageable platform.
As the broader AI-RAN market develops, Nokia is effectively arguing that the prize is bigger than a smarter scheduler. The end state is a RAN that continuously improves through software and increasingly participates in the AI economy — an architectural transition that begins in 5G, accelerates through 5G-Advanced and becomes foundational to AI-native 6G.
