Finnish telecom equipment maker Nokia is preparing to introduce artificial intelligence-powered radio access network technology in India as it looks to capture opportunities created by rising 5G data consumption, expanding AI workloads and growing demand for data-centre connectivity. The company is working with Nvidia to develop AI-RAN solutions that combine Nokia’s telecom expertise with Nvidia’s accelerated computing technology.
Nokia India country business leader Samar Mittal told Business Standard that commercial deployments of AI-RAN in India could begin by 2028, alongside other global markets. The company is also discussing potential trials with Indian telecom operators. The strategy represents a shift towards software-defined, AI-enabled networks that could improve spectrum efficiency, automate operations and support new services at the network edge.
Nokia Targets India With Nvidia-Backed AI-RAN
AI-RAN stands for artificial intelligence radio access network. It combines AI capabilities and accelerated computing with the radio infrastructure that connects mobile devices to telecom networks.
Traditional radio access networks rely on specialised hardware and software to transmit data between smartphones, wireless devices and the wider network. AI-RAN aims to make these networks more intelligent by using AI-driven optimisation and computing resources to improve performance and potentially support additional AI services.
Nokia plans to integrate Nvidia GPUs into its baseband units, which handle important radio-processing functions. The partnership is also designed to use commercially available hardware, giving telecom operators more flexibility in how they deploy and upgrade their networks.
The companies are positioning the technology as a way for operators to increase network capacity without relying entirely on traditional hardware replacement cycles.
Key Details of Nokia’s AI-RAN Strategy
| Indicator | Details |
|---|---|
| Technology | AI-powered radio access networks |
| Technology partner | Nvidia |
| Nvidia investment in Nokia | $1 billion announced in October 2025 |
| Potential Indian commercial deployment | By 2028 |
| Current India status | Discussions around possible trials |
| Reported current spectral-efficiency gain | Around 20% |
| Target efficiency improvement | 50% by 2027; double current efficiency by 2028 |
Sources: Business Standard and Nvidia’s partnership announcement.
These figures describe Nokia’s stated targets and plans, not guaranteed outcomes for every Indian telecom operator. Deployment timelines will depend on testing, operator investment decisions and technical requirements.
Why India Is Important for Nokia
India has become an important market for telecom equipment suppliers because of its large mobile subscriber base and rapid adoption of 5G services.
According to Mittal, India’s 5G data consumption has increased by 70%, while average consumption per user has reached 31 GB per month. Nokia sees these trends as an indication that operators will need more network capacity and improved efficiency as data-intensive applications expand.
AI applications could increase demand further. Services involving video generation, real-time assistants, connected devices and enterprise AI can place additional demands on mobile networks, particularly in densely populated areas.
For Nokia, India offers an opportunity to work with telecom operators on these challenges while using the market as a testing ground for technologies that could also be deployed internationally.
How Nvidia’s $1 Billion Investment Supports the Partnership
Nvidia announced a $1 billion investment in Nokia in October 2025 as part of a strategic partnership focused on AI-native telecommunications infrastructure.
The collaboration aims to bring Nvidia’s accelerated computing platform into Nokia’s radio access network portfolio. The companies are working on technology intended to support existing 5G networks and the eventual transition towards 6G.
GPUs are commonly associated with AI model training and inference, but the partnership seeks to expand their role into telecom network infrastructure.
By integrating GPU computing into radio networks, Nokia and Nvidia aim to support AI-based radio optimisation and additional computing workloads at the network edge.
The partnership also creates an opportunity to use telecom infrastructure for services beyond connectivity. In suitable deployments, edge GPUs could function as localised computing resources for enterprises and other customers that need low-latency processing close to where data is generated.
AI-RAN Could Improve Spectrum Efficiency
Spectrum is one of the most important resources in mobile telecommunications. Operators use licensed radio frequencies to provide wireless services, and the amount of available spectrum is limited.
Improving spectral efficiency allows a network to transmit more data using the same amount of spectrum. This can be particularly valuable in crowded areas where many users are accessing mobile services simultaneously.
Nokia says its software-defined radio solutions are already delivering approximately 20% gains in spectral efficiency, with a target of 50% by 2027 and twice the current efficiency by 2028. These are company-reported results and targets that will need to be assessed across different network conditions.
If such improvements can be achieved at scale, telecom operators could potentially increase capacity, improve service quality and reduce the cost of carrying each unit of data.
However, the practical benefits will depend on the network configuration, spectrum bands, traffic patterns, hardware and software implementation.
What AI-RAN Means for Indian Telecom Operators
Indian telecom operators have invested heavily in deploying 5G infrastructure. As these networks mature, the focus is increasingly shifting from initial coverage expansion towards improving utilisation, managing capacity and generating additional revenue.
AI-RAN could support this transition in several ways:
- Better network utilisation: AI-driven systems could help operators allocate resources according to changing traffic demand.
- Lower operating costs: Automation could reduce manual intervention in routine network-management tasks.
- Capacity improvements: Higher spectral efficiency could allow operators to carry more traffic using existing spectrum.
- New enterprise services: Edge computing could support applications that require low latency or local data processing.
- A path towards 6G: Software-defined architectures could make it easier to introduce future network capabilities.
These benefits remain dependent on implementation and commercial economics. Operators will need to assess whether the additional computing equipment, software and integration costs deliver sufficient returns.
Nokia Looks Beyond Traditional Telecom Equipment
AI-RAN is part of Nokia’s broader effort to expand its business beyond conventional mobile network equipment.
Mittal identified network automation, data-centre connectivity, hyperscaler partnerships and private wireless networks as additional areas of focus. Nokia is also developing optical-network technologies aimed at reducing latency and supporting high-speed connectivity.
Hyperscalers—the companies operating large cloud and computing platforms—need high-capacity connections between data centres and networks. As AI workloads expand, this demand could create opportunities for telecom equipment providers in optical networking and related infrastructure.
Private networks represent another opportunity. These are dedicated wireless networks built for specific organisations or locations, such as airports, logistics facilities and industrial sites.
Nokia sees private networks as an additional business opportunity alongside its relationships with telecom operators and large cloud providers.
India’s Manufacturing and R&D Role
Nokia also maintains a substantial manufacturing and research presence in India.
Mittal said the company’s Chennai manufacturing facility exports around 40% of its total output globally, including radio and backhaul equipment. India also hosts software research and development facilities, global support centres, network operations centres and equipment repair operations.
This footprint could help Nokia support local customer requirements while using engineering and operational capabilities developed in India for global markets.
The combination of local manufacturing, software development and telecom deployments could become more important as AI-RAN technology moves from trials towards commercial implementation.
Challenges That Could Affect AI-RAN Adoption
Despite its potential, AI-RAN faces several challenges.
First, telecom operators must evaluate the cost of GPU-powered infrastructure against the benefits of improved efficiency. Hardware, energy consumption, software licensing and integration costs could influence adoption.
Second, operators will need to ensure that AI workloads do not interfere with the reliability and performance required for essential mobile-network functions.
Third, AI-RAN will need to work with existing network equipment and different vendor environments. Nokia’s software-defined approach is intended to provide flexibility, but real-world deployments will still require testing and coordination.
Finally, the 2028 commercial timeline is a potential milestone rather than a confirmed nationwide rollout date. Trials, operator procurement decisions and regulatory considerations could influence the pace of adoption.
The Bigger Picture
Nokia’s India strategy reflects a broader transformation in telecommunications: networks are increasingly becoming computing platforms as well as connectivity systems. The integration of AI and GPUs into radio infrastructure could help operators manage rising data demand while creating opportunities for edge computing and enterprise services.
For India, the development could bring new opportunities in telecom engineering, software, data-centre connectivity and network automation. However, the scale of the commercial impact will depend on whether the promised efficiency gains translate into measurable cost savings and better network performance for operators.
Looking Ahead
Nokia plans to explore AI-RAN trials with Indian telecom operators, with commercial deployments potentially beginning by 2028. The next important milestones will include operator participation, trial results, infrastructure requirements and evidence that the technology can deliver its targeted improvements under real network conditions.
For Nokia and Nvidia, India could become an important market for demonstrating how accelerated computing can be integrated into 5G and future 6G infrastructure. If the technology proves commercially viable, it could change how operators expand capacity and deliver AI-enabled services. Until then, the partnership remains a significant technology initiative whose long-term success will depend on deployment economics and operator adoption.
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