Riding the AI Data Center Growth Curve

With the growing adoption of AI, the data center industry is entering a phase of rapid expansion. To accommodate these ever increasing demands, data center providers are searching for three main elements: land, power, and fiber-based connectivity to connect data centers. A recent forecast by Linesight predicts that data center energy capacity is on track to rise from 25 GW in 2024 to over 80 GW by 2030. At the same time, traditional service providers are expanding their fiber offerings to support data center interconnects with either lit wavelength services or dark fiber. In this eBook, we will examine how data center providers are leveraging new technologies that not only enable them to scale bandwidth but also increase energy efficiency.
 

Artificial intelligence (AI) is no longer an emerging technology — it is the engine powering transformation across nearly every industry. From generative models that enhance creativity and productivity to advanced analytics driving real-time decisions, AI is reshaping how we work, innovate, and compete. But behind this revolution lies a growing challenge: the infrastructure required to support AI at scale. Data centers, once built to handle predictable workloads, are now under immense pressure to meet the demands of AI’s scale, speed, and complexity. The growth of AI applications, especially large language models (LLMs) and other high-performance compute-intensive workloads, has exposed fundamental limitations in traditional architectures. Legacy tools, outdated testing approaches, and fragmented infrastructure can struggle to keep pace. In response, industry leaders are rethinking how AI data centers are designed, validated, and scaled.
 

1. UNDERSTAND AI’S INFRASTRUCTURE DEMANDS — AND VALIDATE ACCORDINGLY

The shift toward AI-centric computing changes everything, from the silicon layer to system- wide performance. Unlike conventional IT environments, AI workloads introduce massive parallelism, dynamic traffic patterns, and high- speed interconnect requirements. GPUs, NPUs, and AI accelerators must work in sync with dense networking layers, memory architectures, and storage pipelines. Validation must evolve accordingly. System- level validation is no longer optional — it’s mission-critical. Testing individual components in isolation is insufficient when a single bottleneck can ripple through the entire compute fabric. Modern data centers require real-world emulation of AI workloads, integrated benchmarking across layers, and performance testing that mirrors how AI systems behave at scale. This means embracing platforms and methodologies that can simulate AI traffic patterns, validate full-stack interactions, and catch performance issues early in the design process. Without this, even the most advanced hardware stack can underperform or fail under pressure.
 

2. SOLVE THE SCALING PUZZLE: FROM GPU CLUSTERS TO FULL-STACK PERFORMANCE

As organizations race to train increasingly large AI models, compute clusters have scaled to tens of thousands of interconnected GPUs. But simply adding more compute power doesn’t guarantee better performance. In fact, inefficiencies in scaling often lead to underutilized resources, network congestion, and increased operational costs. One of the biggest challenges is maintaining high GPU utilization while managing traffic across the fabric. AI data centers must coordinate compute, memory, and I/O systems across hundreds, or even thousands, of nodes. Without optimized high-speed digital interfaces and interconnects, performance gains flatten or even degrade. Keysight advocates for proactive investment in high-speed validation and compliance tools. For instance, by employing high-performance oscilloscopes and electronic design automation (EDA) software, engineers can validate new designs comprehensively. These validation tools become critical in addressing signal integrity issues early and ensuring reliable data center performance.
 

3. DESIGN WITH TOMORROW’S STANDARDS IN MIND

AI is not only scaling in size but also in speed. Ethernet, PCIe, DDR, and Compute Express Link (CXL) standards are evolving rapidly to accommodate faster data movement and lower latency. While these advancements are essential for enabling high-performance AI systems, they also introduce new design complexities. Signal integrity issues, power constraints, and electromagnetic interference become more pronounced as data rates increase. At the same time, architectural shifts such as disaggregated RIDING THE AI DATA CENTER GROWTH CURVE 5 compute and memory pools require engineers to rethink how components interact within increasingly compact footprints. The eBook introduces the importance of rigorous standards compliance. Keysight emphasizes deploying next-generation digital communication analyzers, bit error ratio testers (BERTs), and other specialized testing solutions as a necessity, not a luxury, for data centers aiming to keep pace with exponential AI growth. These tools also play a critical role in supporting interoperability, as vendors across the ecosystem race to deliver compatible components at breakneck speed.