How Nokia's Networking Lab Supports Cloud and AI Scale

AI workloads are placing growing pressure on data centre infrastructure as hyperscalers and cloud providers scale services that depend on high throughput and continuous availability.

Behind enterprise AI tools and chatbot platforms, data centre networks carry large volumes of traffic that require stable and predictable performance.

Nokia is narrowing its focus onto this area, with the launch of an AI Networking Innovation Lab in Sunnyvale, California. The facility is designed to help cloud companies and data centre operators test AI infrastructure under operational conditions before deployment into production environments.

The lab is built to address one of the core challenges facing modern data centres: how networking infrastructure handles AI training and inference workloads across distributed systems without bottlenecks or outages.

As AI adoption increases across cloud platforms and enterprise environments, operators are under pressure to ensure data centre fabrics can support demanding workloads at scale.

Rudy Hoebeke, Vice President of Software Product Management at Nokia, says: "The launch of Nokia’s AI Networking Innovation Lab marks a major milestone in our commitment to drive the next era of AI-native connectivity.

"As the industry continues to evolve with solutions like scale-across and AI-Grid, this lab is poised to accelerate AI networking technology that will not only support but optimise these emerging industry offerings.

"This centre gives our customers and partners early access to new technologies, deeper collaboration with the world’s leading AI ecosystem players and the confidence that their networks are validated under more realistic AI conditions.

"By accelerating innovation and reducing deployment risks, we’re enabling the industry to deliver faster, more reliable, and more sustainable AI experiences to people and businesses everywhere."

AI workloads reshape data centre networking

The launch reflects how AI infrastructure depends on networking performance inside the data centre as much as compute power itself.

Large-scale AI systems generate heavy east-west traffic, with data constantly moving between servers and storage systems within the same data centre environment.

These workloads require low-latency connections and high-capacity switching to support training models and real-time inference applications.

Nokia says traditional networking approaches no longer meet the requirements created by AI environments operating across cloud infrastructure and enterprise deployments.

The company is using the lab to test switching silicon, automation tools and networking protocols together under pressure conditions designed to replicate operational environments.

The facility supports Nokia Validated Designs, which are reference architectures tested across multi-vendor infrastructure.

These designs are intended to help operators reduce integration complexity when building AI-ready data centres.

Arno van Huyssteen, Vice President of Global Telecommunications for Nscale, says: "Nokia is a strategic networking partner for Nscale as we build towards AI Grid, and the engineering rigour behind their Validated Designs reflects the kind of innovation needed to enable next-generation AI infrastructure.

"The depth of hardware, software and failure testing behind those blueprints is what will give operators the confidence to deploy complex AI environments faster, with fewer integration risks and less operational disruption.

"We're excited to collaborate in the AI Networking Innovation Lab to help push the boundaries of AI-native networking and validate the next generation of solutions before they reach production."

Ecosystem partnerships support interoperability

Nokia is building the lab around partnerships with GPU providers, silicon vendors, cloud platforms and testing specialists.

The company aims to improve interoperability between infrastructure layers and reduce fragmentation across AI deployments.

AMD is among the companies working with Nokia through the initiative. The chipmaker says open ecosystems are important for operators seeking flexibility across AI infrastructure stacks.

Travis Karr, Corporate Vice President, HPC and Sovereign AI at AMD, says: "AMD believes customer collaboration and an open ecosystem are fundamental to accelerating AI innovation.

"By co-developing solutions with partners, such as Nokia in their AI networking innovation lab, we ensure our AMD enterprise AI solutions are tested with Nokia data centre switches on real-world workloads and network demands.

"An open, standards-driven approach empowers customers to integrate seamlessly across heterogeneous environments, avoiding lock-in and fostering industry-wide advancement in AI."

The lab also supports testing across multiple AI transport technologies including Ultra Ethernet Consortium protocols and RoCEv2, or Remote Direct Memory Access over Converged Ethernet version two.

RoCEv2 allows servers to exchange data directly across high-performance Ethernet networks with reduced latency and lower CPU usage, making it widely used in AI data centre environments.

As AI infrastructure scales, support for common networking standards is especially important for operators building interconnected cloud and enterprise platforms.

Real-world testing becomes key to deployment

A core part of Nokia’s approach is testing infrastructure against realistic operational conditions rather than relying on theoretical performance measurements.

The company says the facility recreates AI workloads to test congestion management, automation systems and failure scenarios across data centre environments.

Keysight is using the lab to emulate AI training workloads at scale and assess network performance under stress conditions.

Ram Periakaruppan, Vice President and General Manager, Network Applications and Security business at Keysight, says: "Partnering with Nokia in the AI Networking Innovation Lab has enabled us to benchmark and optimise AI networks under real-world conditions.

"Keysight emulated AI training workloads at scale across a range of AI transports, from UEC and RoCEv2 to emerging lossless fabric architectures.

"Together, we are helping accelerate AI network adoption by giving operators and hyperscalers the validated insights needed for confident, large-scale deployment."

Testing environments like Nokia’s lab provide AI hyperscalers a way to assess resilience and operational readiness before infrastructure reaches production workloads.

The launch also reflects broader competition among infrastructure vendors around validation and ecosystem support as AI demand increases across cloud and enterprise markets.