AI Infrastructure Challenges: Power and Cooling in High-Density Data Centers

As artificial intelligence (AI) continues to revolutionize industries, one of the biggest hurdles for AI-driven organizations is managing their computational infrastructure’s growing power and cooling demands. Whether enabling autonomous vehicles or powering predictive analytics, AI workloads require immense processing capabilities, and as performance increases, so do the energy consumption and heat generation.

A recent T5 Data Centers project with a leading quantitative trading firm demonstrated how the combination of GPUs (Graphics Processing Units)-intensive environments and AI workloads are pushing the limits of traditional data center cooling systems – and how this firm overcame these challenges.

The Growing Demand for Computational Power

AI systems, especially those in data-intensive fields like quantitative finance, rely on specialized hardware to perform complex calculations. GPUs are a primary tool for this, offering the parallel processing power required to run advanced AI algorithms. However, as the density of these GPUs increases, often reaching 700 watts per square foot, the power needed to operate them skyrockets. This high power density, which refers to the amount of power consumed per unit area, leads to challenges in cooling and efficiency.

Traditional air-cooling systems struggle to manage these massive power loads in data centers, especially when power densities exceed what standard infrastructure can handle. According to the Uptime Institute, many legacy data centers are engineered to support power densities of only 5 to 10 kW per rack, whereas AI environments can require 30 kW per rack or more. This mismatch can lead to higher energy bills and operational instability, with extreme weather events such as heatwaves or storms often exacerbating the problem. These challenges underscore the urgent need for more efficient, reliable cooling solutions.

The Liquid Cooling Solution

Liquid cooling, a cutting-edge solution, is rapidly emerging as the most efficient way to manage the thermal load created by high-performance computing (HPC). Unlike traditional air systems, liquid cooling uses a liquid coolant to remove heat from components directly. This results in more efficient thermal management, reduced energy consumption, and more predictable cooling performance — all crucial for AI-driven systems that require maximum uptime and minimal latency. 

According to Omdia, global adoption of direct-to-chip liquid cooling is expected to grow over 20% annually through 2027, driven largely by hyperscale and AI-focused deployments. Additionally, ASHRAE notes that liquid cooling can remove up to 70% more heat than standard air cooling, making it a top choice for organizations pushing the boundaries of compute performance. 

In the case study, T5 Data Centers provided a liquid-cooled data center solution for a leading trading firm whose operations rely on dense GPU clusters. By retrofitting an existing 40,000-square-foot facility with liquid cooling technology, T5 helped the firm manage power densities up to 700 watts per square foot — a significant leap from traditional air-cooling capabilities.

This reliable solution allowed the trading firm to scale its operations while maintaining performance stability. Moreover, the liquid cooling infrastructure mitigated the risk of costly disruptions caused by power spikes or overheating, ensuring a more predictable and sustainable cost structure for the firm.

The Importance of Scalable, Reliable Infrastructure

As AI continues to evolve, so too does the need for infrastructure that can scale with it. The quantitative trading firm needed a solution that could not only meet its immediate cooling and power demands but also support its long-term growth. T5 Data Centers demonstrated the ability to deliver this by designing a flexible, scalable facility that could accommodate future advancements in AI technology, providing a sense of security for the firm’s future needs.

This adaptability is essential in AI-driven industries where technology lifecycles are measured in months—not years. As Gartner highlighted in its 2024 data center trends report, “modular, scalable infrastructure and sustainable cooling are now table stakes for enterprises building next-gen AI platforms.”

By combining cutting-edge liquid cooling with a scalable infrastructure model, T5 ensured that the trading firm could meet both current and future needs. This foresight is critical in industries where the pace of technological change is rapid, and staying ahead of the curve is necessary for success.

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If you want to learn more about how liquid cooling transforms data center infrastructure, including details on the technology used, the power requirements, and the operational strategies behind the project, download the full case study.