Critical connectivity for modern data centers
Scale AI on a foundation that’s built to perform
The AI supercycle is driving a fundamental shift in cloud and data center infrastructure, elevating the role of the network. The network is an essential foundation for connecting distributed data centers, ensuring data sovereignty and real-world AI performance.
To support the breadth of emerging use cases, AI requires a network that scales with the rapid expansion of data centers while delivering the mission-critical security, reliability and performance that modern applications demand.
The network infrastructure that AI needs
Cloud business by end of this decade
Data centers worldwide
DC capacity used for AI by 2030
Annual AI traffic growth
The cloud-network continuum
The cloud is facing pressures from AI that are forcing it to become more distributed, with a mix of central, edge and on-prem data centers.
- Emerging enterprise and consumer use cases with sensitive data and immediate response needs require inferencing closer to the end user
- On the training side, data sovereignty drives repatriation, while space and power limits require spreading workloads across data centers.
The cloud continuum needs a flexible and scalable network to seamlessly shift workloads to where processing the data is most efficient and secure. This calls for a programmable network, ready to adapt and thrive in a rapidly changing environment, no matter what the future holds.
Why the network plays a critical role
The cloud transformation is powered by an end-to-end network that connects workloads inside data centers, bridges buildings and sites, routes across wide-area links, and reaches users and devices with speed and reliability.
Access and distribution
The network ensures AI inference workloads reach enterprises and users quickly and reliably, delivering the performance modern applications demand.
Inside data centers
The network enables high-speed, reliable, and lossless interaction between compute resources, maximizing efficiency and minimizing job completion time in the AI factory.
Between data centers
The network ties together multiple facilities into a cohesive AI infrastructure, enabling ultra-fast, reliable, and secure collaboration across sites.
The profound changes in network design
As the cloud evolves to support AI applications, network design has to respond. This is not an incremental upgrade but a profound, foundational shift that is as urgent and important as every other part of the AI value chain.
- Speed and scalability must rise to meet the extreme performance requirements of AI workloads and their need to move horizontally to where they are most efficient
- Reliability and security are essential as AI drives increasingly critical applications with impacts on safety, defense and essential services in everything from manufacturing to healthcare
- Responsiveness and adaptability are essential to support both the rapid iteration and learning of AI model training, and the low-latency, real-time inferencing required to deliver instant insights and decisions to users
Report
2026 Data Center Networking
Automation imperative
Network automation is not an option. It's the only way to master the increasing network complexity and respond faster to changing demands or unexpected events.
By marrying AI and automation, the network becomes predictive and autonomous, configuring, healing and optimizing itself.
Automate now to turn the network into a key competitive advantage and stay ahead of the game.
Security must go deep
With AI-driven threats on the rise, protecting data from malicious actors must be your top priority.
By building security in depth, the network can encrypt data, detect anomalies, mitigate threats and enforce policies in real-time.
Secure every node, every layer, to safeguard your business and protect what matters most.
Networks for AI, with AI
AI is revolutionizing the network, just as it is transforming data center and cloud architectures.
Harness its power to streamline operations, optimize performance, strengthen security, reduce energy consumption, and improve reliability across your network infrastructure.
Now is the time to amplify innovation and boost efficiency.
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Sovereign AI data center networks
Modernize your data centers with Nokia networking solutions
Our comprehensive portfolio of networking solutions provides the reliability, scalability, efficiency and security needed to seamlessly connect users and AI workloads in and across data centers, irrespective of where they reside.
Frequently asked questions
Data center networking refers to the infrastructure that interconnects all components within one or more data centers, enabling storage, compute and application servers to communicate with each other, with external networks and with users.
It includes intra-data center connections (within a single facility), inter-data center links (between sites), and connectivity to public and private clouds, the Internet and the wide area network (WAN). It involves the design, implementation, and management of those connections.
The network plays a critical role in enabling the data center to function efficiently and meet the demands of cloud and AI applications. It provides a fast, scalable, secure, and reliable platform for the transmission, storage, and processing of large volumes of data, ultimately supporting the delivery of digital services and applications to users.
A modern data center is a highly efficient, scalable, and agile facility that supports the growing demands of digital transformation and data sovereignty. It is characterized by advanced technologies such as cloud computing, AI and distributed infrastructure which enable real-time data processing, analytics, and decision-making. Modern data centers are designed to be flexible, secure, and sustainable, with a focus on minimizing environmental impact.
Modern data centers are crucial because they underpin the digital transformation of businesses and organizations. They enable the delivery of superior cloud and AI-based services with low latency, reliable performance and consistent service levels. They ultimately contribute to economic growth, competitiveness and social development while being designed to minimize their environmental footprint and operate with greater sustainability.
AI is transforming data centers in two significant ways.
On one hand, the growing demand for AI-driven applications and services is driving the need for data center modernization. This means designing and building infrastructure that can support the intense computational requirements of AI workloads. To meet these demands, data centers must adopt new architectures and technologies that can provide the necessary scalability, performance and flexibility.
On the other hand, AI is also being used to optimize and improve the operation of data centers themselves. By leveraging natural language processing, anomaly detection, predictive analytics and automation, data centers can be simpler to operate and become more efficient, reliable, responsive and sustainable.
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