By: Tod Higinbotham

As data centers evolve into modular, AI-optimized hubs that improve scalability and efficiency, innovations like prefabricated modular designs, AI-driven automation, and advanced cooling systems are redefining the landscape.

As artificial intelligence (AI) workloads rapidly proliferate across the globe – creating unprecedented demand for power, cooling, and scalability – data center operators are reimagining their approach to data center design.

No longer confined to the one-size-fits-all architectures of the past, today's data centers are evolving to become AI-optimized, modular, purpose-built ecosystems that prioritize performance, flexibility, scalability, and energy efficiency.

This shift is occurring in response to the unprecedented demands that AI workloads impose on power, cooling, and scalability. As organizations race to deploy high-performance compute environments optimized for training and inference workloads, they find that traditional data center configurations are ill-equipped.

The AI Load Challenge

The AI revolution introduced three distinct data center architectures. First, existing co-location facilities designed initially for general-purpose compute are now being retrofitted to accommodate GPU-intensive workloads, which require higher power densities and advanced cooling solutions. Second, new data centers are being designed from the ground up – some with more traditional compute workloads and many with AI-specific architecture in mind. The third is hyper-scale architectures.

CoreSite's Chicago data center (CH1) is a notable example of retrofitting. Originally designed as a telecommunications hub, CH1 has been upgraded to support modern high-density workloads. CoreSite incorporated redundant switchgear, spare generator positions, and enhanced cooling systems, enabling the facility to handle rack densities upwards of 40 kilowatts per cabinet with air cooling. 

Colovore, a high-density hosting specialist, is expanding its Santa Clara facility to create a 9-megawatt data center optimized for AI workloads. This new facility will support rack densities of 50kW using water-chilled rear-door cooling units and can scale up to 250kW per rack with direct liquid cooling.

And, of course, there are the hyperscalers. Fueled by AI demand, hyperscalers, including Meta, Google, and Amazon, are racing to build some of the world's most significant data center clusters, which carry price tags of tens of billions of dollars per cluster.

The Power Challenge

The differences between retrofitting and new builds are stark. In retrofitted environments, infrastructure must be adapted to meet higher power densities and address the dynamic nature of AI workloads. GPU clusters present a unique challenge by producing sharp, unpredictable power spikes. Power infrastructure and cooling systems originally optimized for flat CPU loads are now contending with a more volatile profile. In purpose-built facilities, designers can anticipate these challenges and engineer solutions that deliver optimized performance from the outset.

Modularity: The Foundation of Flexibility

The shift to modular data center design marks a key innovation in the continuing transformation of data centers. While "modular" is used in different contexts, within infrastructure, it refers to the practice of preassembling sub-components of a data center – such as power distribution systems, cooling loops, and battery enclosures – off-site in controlled environments. These modules are then shipped to the deployment site, where they are integrated with minimal on-site labor.

Modular design accelerates deployment, improves build quality through factory-based testing, and lowers costs by reducing construction complexity. In practical terms, modular configurations enable phased buildouts, allowing operators to scale