AI-driven data centers’ cooling systems

The issue on how to enhance data centers’ cooling systems has been on the table for long now. Today, optimizing cooling has grown to represent a strategic area for data centers, considering its impact on protecting hardware, energy consumption and overall data center economic viability.

In this context, the rise of AI-driven smart data center solutions emerges as a key solution. In essence, what the AI data center does is allowing for thermal management strategies that adapt to real-time conditions. This translates into a series of benefits, including the opportunity for cutting down costs and data centers’ environmental impact while ensuring equipment is at desirable temperature ranges at all times.

This shift is particularly crucial at a time when increasing computational demands are expected in the coming years, which will require doubling down on efficiency in order to build resilience while remaining economically sound.

Data centers’ cooling systems represent a complex ecosystem whose energy consumption can vary from about 7% to 30%, according to figures by the IEA. By applying AI-based automations, leading data centers such as Google’s have achieved an outstanding 40% reduction in energy consumption. An achievement that hasn’t gone unnoticed, with the smart data center will likely soon become the norm: the Uptime Institute Global Data Center Survey 2024 cites how “nine out of 10 (91%) vendor respondents believe that it is likely that AI will be widely used in the data center in the next five years”.

But how exactly are AI-driven solutions applied to data centers’ cooling systems?

In this article, we analyze the potential for AI to adjust data center cooling to real-time conditions and the ripple beneficial effects that it entails.

The two key ways AI is transforming data centers’ cooling systems

Real-time monitoring for dynamic cooling and predictive analytics

Sensors, digital twins and machine learning models are the first step in the AI ecosystem when it comes to data centers’ cooling systems.

These three act in the following progression: firstly, sensors provide monitoring capabilities. In charge of collecting real-time environmental and equipment information, they may monitor temperature data but also airflow data and power load data.

Building from this, digital twins use this wide spectrum of data to simulate potential cooling strategies and predict their outcomes without interrupting core operations. Finally, machine learning models are capable of seeing ‘the bigger picture’, analyzing complex data patterns to infer thermal trends and system requirements, and adapting cooling strategies as a consequence.

By combining these three technologies, a highly-adaptive smart data center emerges, where the cooling system responds to real workload and environmental conditions.

In other words, AI is helping build extremely accurate and dynamic data centers’ cooling systems that match real-time conditions and demand, reducing energy consumption when possible while guaranteeing adequate temperature ranges.

At the same time, AI-based technologies are also instrumental in predicting potential undesirable scenarios, allowing operators to apply preventive maintenance to extend equipment’s life. As such, advanced sensors can detect damage to cooling equipment at an early stage, issuing a warning that activates maintenance for the affected component before the actual issue takes place. As such, AI represents an opportunity to enhance equipment longevity and resilience. 

Intelligent cooling algorithms

The implications of AI for the smart data center go beyond the real-time, dynamic scenarios we just described. Intelligent cooling algorithms are capable of creating a system that is self-sufficient and self-governed, reducing human errors while expanding the capacities of operators.

At the core of this capacity are tools such as Reinforcement Learning, where the cooling system learns to make decisions by interacting with data in an iterative manner and with the aim of maximizing a long-term reward. In the case of RL applied to data centers’ cooling systems, it can learn to maximize energy use as a reward while also maintaining the desired temperature levels. In order to do so, it interacts with data on workloads, environmental conditions and cooling system parameters. 

Additionally, control loops are introduced in AI-based systems to guarantee all variables remain at desired levels by applying continuous adjustments.

By combining RL and control loops, it’s possible to optimize fundamental aspects of cooling systems such as fan speeds, liquid flow and chiller activity, all with a high degree of accuracy.

For instance, in Luo et al. (2022), Reinforcement Learning is suggested as a method to improve chiller operation as part of Google’s AI data centers: even in complex scenarios where multiple chillers are involved, the paper shows Reinforcement Learning goes beyond conventional control systems by minimizing energy consumption based on dynamic data. In fact, the paper mentions how this approach resulted in energy savings of between 9 and 13%.

 Cooling technologies for AI data centers 

The rapid growth of AI and high-performance computing (HPC) is increasing rack power densities and changing the thermal requirements of data centers. As GPU-intensive workloads generate significantly more heat than traditional IT environments, cooling architectures must evolve to maintain efficiency, reliability and scalability.

There is no single cooling technology suitable for every AI data center. The optimal approach depends on factors such as rack density, IT infrastructure, facility scale and operational requirements. In many cases, different cooling technologies can also coexist within the same facility.

Air cooling

Traditional air cooling remains suitable for lower-density environments, using systems such as Computer Room Air Handlers (CRAHs) to remove heat from IT equipment. However, as rack densities increase, moving enough air to effectively dissipate heat becomes more challenging and can increase fan energy consumption.

Direct-to-chip liquid cooling

Direct-to-chip cooling delivers liquid directly to cold plates installed on heat-generating components such as CPUs and GPUs. By removing heat closer to its source, it can support higher rack densities than conventional air-based systems and is increasingly relevant for AI and HPC environments.

Immersion cooling

Immersion cooling places IT equipment in a dielectric fluid that absorbs heat directly from servers and components. This approach can handle very high heat densities while reducing reliance on traditional air cooling, although it requires specific IT equipment and infrastructure considerations.

Hybrid cooling

Not every component within a data center has the same cooling requirements. Hybrid architectures combine air and liquid cooling, allowing high-density AI equipment to use liquid cooling while other IT infrastructure continues to rely on conventional air-based systems.

The main approaches can be summarized as follows:

Integrating liquid cooling with centralized cooling infrastructure

These technologies should not necessarily be considered as competing alternatives. Direct-to-chip, immersion or hybrid cooling address heat removal close to the IT equipment, while centralized cooling infrastructure manages heat at facility level.

In large-scale AI data centers, both layers can therefore work together as part of the same thermal architecture. A central cooling plant can supply and manage the cooling capacity required by high-density environments while optimizing equipment operation according to changing thermal loads.

This facility-level approach also creates opportunities to integrate technologies such as free cooling, thermal energy storage (TES) and waste heat recovery, helping improve overall energy efficiency, resilience and scalability.

As AI workloads continue to increase data center power and cooling requirements, designing these different layers as an integrated system will be increasingly important. The objective is not only to remove more heat, but to do so efficiently and reliably while adapting the cooling infrastructure to changing IT loads.

Benefits of AI-driven cooling systems for smart data centers

Energy efficiency and cost reductions

Google’s claim of having reduced their data center energy consumption by 40% thanks to DeepMind ensured attention towards AI-driven optimizations in cooling systems.

As seen across the article, outstanding figures like these rely on AI’s capability to continuously analyze and leverage real-time data, ensuring only the required energy is used while guaranteeing equipment safety at all times.

Because only the necessary cooling is activated, this opens the door to important energy efficiency and cost savings. Additionally, options such as free cooling in data centers can be activated strategically when actual conditions allow it.

Improved reliability and hardware longevity

AI technologies ensure hardware operates at desired temperature ranges at all times, reducing thermal stress on components. This enables an extension in equipment’s operational lifespan, which then cascades into a series of benefits, including less downtime due to overheating.

By ensuring consistent thermal management, data centers extend their reliability. On top of this, cost reductions are also achieved by AI’s predictive capabilities, which prevent catastrophic equipment failure (and its ensuing financial losses) while also increasing hardware’s lifespan in the medium and short term.

Scalability for future AI workloads

Data centers’ capacities to accommodate AI workloads in the not-so-distant future have been an important subject of discussion in the industry in recent times.

Critical projections have guided such discussions, with the issue of data center energy efficiency being a key concern.

In 2024, the IEA described data centers as accounting for “about 1.5% of global electricity consumption”, a figure that “has grown at 12% per year over the last five years.” Along the same lines, another report by the IEA paints a scenario where “from 2024 to 2030, data centre electricity consumption grows by around 15% per year, more than four times faster than the growth of total electricity consumption from all other sectors” and reaching “just under 3% of total global electricity consumption in 2030.”

This growth, which is “mainly driven by AI adoption” across the globe, puts data centers at a crossroads: they must scale up to handle increased computational demands while also limiting their energy consumption and environmental impact.

The AI-based smart data center solutions emerge as a key solution in this scenario. As seen above in this article, cooling systems that incorporate AI technologies are projected to enable outstanding improvements in energy efficiencies, necessary to make the data centers of the future technically, environmentally and economically viable.

Through applying adaptive and dynamic liquid cooling solutions, data centers’ cooling systems can advance to incorporate the heat generated by high-performance AI hardware, and they can do so efficiently.

In fact, smart cooling systems have been reporting the capacity to “increase the nominal tonnage of a typical plant by as much as 20%”. An important step to boost data centers’ cooling capacities to meet future workloads while also ensuring energy efficiency.

At a time when data centers’ cooling systems take centerstage as a strategic choice for economic and environmental reasons, at ARANER we put our thermal engineering expertise to work to become key allies for data center operators.

Today and in the near future, achieving data center redundancy for evolving computing-intensive workloads will be equally as important as designing green data centers that ensure compliance and remain economically viable.

In this scenario, design for data center cooling systems must incorporate cutting-edge technologies while also adjusting to each project’s needs, limitations and opportunities. This is precisely where ARANER comes in.

From modular cooling plants to Thermal Energy Storage (TES) systems and waste heat recovery, we design state-of-the-art data centers’ cooling systems that blend technology with sophisticated thermal engineering. The result? Fully-integrated AI-based data centers’ cooling systems that put energy efficiency and redundancy at the center.

Want to learn more about how we can help you build the smart data center your project needs? Discover more about our data center cooling solutions and get in touch with us to speak to our team.

Frequently asked questions about AI data center cooling

What is AI data center cooling?

AI data center cooling refers to the technologies used to manage the high heat loads generated by AI and HPC infrastructure. It can combine air cooling, liquid cooling and centralized cooling systems to maintain efficient and reliable operation.

Why do AI data centers need advanced cooling systems?

AI workloads rely heavily on GPUs and high-performance computing equipment, which generate high heat densities. Advanced cooling systems help manage these increasing thermal loads while maintaining efficiency, reliability and scalability.

Why is liquid cooling important for AI data centers?

Liquid cooling removes heat closer to high-performance components such as GPUs and CPUs. Technologies such as direct-to-chip and immersion cooling can therefore support the higher rack densities associated with AI workloads.

How can AI improve data center cooling efficiency?

AI can analyze real-time thermal and operational data to adjust cooling according to actual demand. It can optimize temperatures, flow rates, pumps and chiller operation while also supporting predictive maintenance.

What cooling system is best for an AI data center?

There is no single solution for every AI data center. The optimal cooling architecture depends on rack density, IT equipment, facility size, climate, redundancy requirements and expected growth.

Can liquid cooling be integrated with a central cooling plant?

Yes. Liquid cooling removes heat close to IT equipment, while centralized cooling infrastructure manages heat at facility level. Combining both approaches can support high-density AI environments efficiently and reliably.

Can AI data center cooling integrate with thermal energy storage?

Yes. Thermal energy storage (TES) can store cooling capacity for later use, helping data centers manage peak thermal loads, increase operational flexibility and optimize central cooling plant operation.

Can waste heat from AI data centers be recovered?

Yes. Waste heat from AI data centers can potentially be recovered and reused. Heat pumps can increase its temperature when necessary, enabling applications such as district heating instead of rejecting that thermal energy to the environment.