Retrofitting Data Centers for AI: Strategies for Space, Cooling, and Electrical Demands

Data Center Retrofit Strategies for AI Workloads

The surge in AI adoption across industries has brought a new set of challenges for data center management. AI servers demand significantly more space, power, and cooling than traditional infrastructure. As a result, retrofitting existing data centers to meet these demands is becoming essential for businesses that want to stay competitive and efficient.

Here’s a clear breakdown of how to approach these upgrades with practical strategies that address the unique requirements of AI workloads.

Optimize Liquid Cooling

AI workloads push power density in data centers well beyond traditional levels—often exceeding 120-136 kW per rack. This increase generates a lot of heat, making air cooling alone insufficient. A hybrid cooling strategy is needed, typically combining about 22-25% air cooling with 75-78% liquid cooling.

Direct-to-chip liquid cooling is one of the most effective methods. It involves circulating treated water through coolant distribution units (CDUs) and durable overhead steel piping to manage heat safely and efficiently. Prioritizing chilled water distribution to AI servers while reserving air cooling for supporting equipment helps maintain system stability and longevity.

Upgrade the Electrical Distribution System

AI accelerators are sensitive to power fluctuations, and older UPS systems may not handle the rapid and large changes in power demand. Modern AI loads can cause swings of 3 MW or more in sub-cycle periods, which can lead to data corruption or system crashes if not managed properly.

Upgrading electrical infrastructure means replacing outdated UPS technology, redesigning power distribution for redundancy, and configuring power supplies to support high-density computing loads. For example, server chassis may need to operate with six out of eight power supplies active instead of the usual A/B power distribution. Backup generators and modern battery systems are crucial to ensure uptime and reliability.

Deploy an Energy and Power Management System

Minimizing downtime is critical when running AI workloads. Energy and power management systems (EPMS) offer high-resolution waveform monitoring to quickly identify and respond to power quality issues. Without such systems, power transients can cause cooling failures and strain hardware.

Integrating EPMS with building management solutions allows for real-time monitoring and fast corrective action, helping maintain energy efficiency and system stability. This proactive approach reduces risks and keeps AI servers operating smoothly.

One Size Doesn’t Fit All

Every data center has its own constraints and requirements. The right retrofit strategy depends on the specific space, power, and cooling characteristics of each facility and the nature of the AI workloads it supports.

Engage engineers experienced in various equipment configurations and plan for future scalability to reduce long-term costs and risks. Building strong vendor relationships also helps stay informed about the latest technology improvements and best practices.

For professionals interested in expanding their expertise in AI infrastructure and management, exploring courses and certifications can be a valuable step. Resources such as Complete AI Training’s latest AI courses provide practical knowledge to support these evolving demands.