Executive Summary
- As artificial intelligence drives a new era of compute-intensive workloads, data centers are facing an unprecedented cooling challenge.
- With AI data centres continually evolving, the industry will increasingly recognise that cooling performance does not begin with chillers or pumps alone.
- And, here Tim Bruewer, Sr. Global Strategic Marketing Manager at Watlow provides the ‘simple’ answer.
Rack densities are rising rapidly, cooling systems are becoming more complex, and operators are under growing pressure to maintain uptime at scale. Yet amid all the discussion around liquid cooling, high-efficiency chillers and next-generation cooling architectures, one critical technology often goes overlooked: electric heaters and controls.
At first glance, the idea sounds contradictory. Why would heaters matter in facilities designed entirely around removing heat?
The answer is simple. Modern cooling systems cannot be trusted until they have been tested with real heat loads under real operating conditions.
In today’s AI-driven data center environment, heaters are no longer just industrial components. They are essential tools for validating, commissioning and optimising the cooling infrastructure that keeps hyperscale computing online.
For operators, this is fundamentally a question of resilience: ensuring that critical cooling infrastructure can withstand changing thermal demands without compromising the availability of the computing systems it supports.
AI workloads are reshaping cooling requirements
Traditional enterprise data centers were largely designed around air cooling. But AI clusters packed with high-performance GPUs are changing the equation dramatically. Individual racks can now exceed 50kW, with some approaching or surpassing 100kW in high-density racks. The resulting heat density is pushing conventional cooling systems beyond their practical limits.
As a result, the industry is rapidly moving towards liquid cooling technologies capable of removing heat far more efficiently than air alone. These include coolant distribution units (CDUs) and direct-to-chip cooling systems that circulate coolant directly across processors and accelerators. Liquid load banks are also used to test and validate the performance of these cooling systems.
This shift is fundamentally changing how data centres are designed, cooled and commissioned. It is also changing how cooling performance is validated.
Historically, many systems were assessed primarily through simulation and modelling. Engineers could estimate heat loads, calculate cooling capacity and predict system behavior using digital models. But AI infrastructure is introducing levels of complexity that are increasingly difficult to validate theoretically.
Real-world operating conditions create fluctuating loads, localised hotspots and transient heat spikes that models alone cannot fully replicate. Even small variations in temperature or flow can have serious consequences inside liquid-cooled environments.
That is why thermal load testing is becoming essential across the data centre ecosystem. Before a cooling system goes live, operators need to know with certainty that it can manage the heat generated by live AI workloads. This requires applying tightly controlled heat loads before commissioning to simulate actual operating conditions.
In other words, cooling systems must first be tested and validated before they can be trusted to cool.
Why thermal load testing is becoming mission critical
As liquid cooling adoption accelerates, three key areas are emerging where heating, sensing and control play a central role:
The first is liquid load bank testing.
These systems simulate the heat output of high-density compute infrastructure before servers are installed. Using high-watt-density heaters combined with coolant circulation, liquid load banks reproduce the heat rejection demands of live AI workloads.
By applying controlled heat loads, engineers can evaluate how chillers, pumps and cooling loops perform under real operating conditions. This allows operators to verify cooling capacity, test control systems and identify potential weaknesses before expensive compute equipment is deployed.
Cooling systems must also be tested across varying load profiles, requiring carefully controlled ramping and stepping of heat output to replicate changing compute demands. Without this validation, operators risk commissioning infrastructure that has only been theoretically proven.
The second critical area is CDU commissioning.
CDUs act as the interface between facility water systems and critical equipment, regulating coolant temperature, pressure and flow throughout the data center. Before they enter service, these systems must undergo rigorous commissioning.
A more advanced liquid load bank called a rack emulator can simulate rack-side heat loads, allowing operators to test thermal transfer performance, validate heat exchanger operation and confirm stable coolant circulation.
This is not simply about reaching a target temperature. Engineers must also balance flow rate, pressure differentials and delta-T performance across facility and rack loops.
Finally, direct-to-chip cooling introduces even tighter temperature control requirements.
Here, liquid coolant flows directly across cold plates mounted to CPUs and GPUs. AI accelerators generate intense and highly concentrated heat loads, requiring exceptionally precise cooling performance. Even small temperature variations can increase condensation risk and affect system stability.
To validate these systems, engineers use specialised heaters that reproduce the transient heat behavior of live processors. These heaters simulate real compute conditions, allowing engineers to evaluate cooling performance before production hardware is fully operational.
Cooling performance now depends on data and control
The evolution of data centre cooling is not only changing hardware requirements. It is also driving demand for more advanced sensing, controls and data acquisition capabilities.
Modern load testing depends on far more than simply generating heat. Operators now require detailed visibility into system performance at every stage of commissioning and operation.
That includes real-time temperature monitoring, delta-T measurement, pressure and flow sensing, leak detection, thermal mapping, transient profiling and power usage effectiveness tracking.
The result is a far more connected cooling environment where heating, sensing, controls and analytics work together to improve efficiency and operational confidence.
This is especially important in liquid-cooled environments, where precise temperature management directly affects operational stability. Preventing condensation, maintaining stable coolant temperatures and monitoring transient heat behavior are all essential to ensuring long-term system performance.
As AI infrastructure continues to evolve, cooling is becoming as much a data and control challenge as it is a mechanical one.
Reliability starts before the data centre goes live
The scale of modern AI infrastructure is creating new pressures across the entire data centre supply chain. Hyperscale facilities require enormous power capacity, complex mechanical systems and extensive supporting infrastructure. Labour shortages, long lead times and supply chain consolidation are already reshaping the market.
At the same time, expectations for uptime continue to rise. In this environment, reliability is no longer a secondary consideration. Operators cannot afford uncertainty when deploying cooling infrastructure designed to support high-value AI environments.
That is why commissioning and validation are becoming such critical stages in data center deployment. The ability to accurately simulate, control and measure heat loads now plays a central role in ensuring cooling systems can perform safely and reliably under real-world operating conditions.
As AI data centres continue evolving, the industry will increasingly recognise that cooling performance does not begin with chillers or pumps alone. It begins with the ability to accurately reproduce the heat those systems are designed to remove.
Because in modern data centres, the hidden role of heaters is not creating heat. It is proving that cooling systems can handle it.
