The AI-ready data center represents a major transformative force that is shaping what innovation looks like in the industry.
As high-density computing infrastructure to accommodate AI processes are increasingly demanded, power and efficiency requirements are being pushed to new limits. This is particularly true for the thermal and energy infrastructure involved in data center operation, including data center cooling.
A major movement that is, at the same time, accompanied by a growing scrutiny around data centers’ environmental footprint. In this context, a new era is emerging where solutions that level up data center efficiency and sustainability are key to future competitiveness.
As demand for AI-ready data centers soars, what does AI data center architecture entail exactly, and how are advanced operators levelling up to meet demand? Let's take a look.
Why AI is redefining data center infrastructure: the impact of AI workloads
As early as 2030, 70% of data center demand could be tied to AI-ready data centers, according to figures by McKinsey. While the consulting company observes calculations around future data center demand are complex, they also foresee “global demand for data center capacity could rise at an annual rate of between 19 and 22 percent from 2023 to 2030.”
This data tells the story of the rise of generative AI and AI workloads. But it also shows that the development of AI is directly linked with a new generation of data centers, capable of hosting GPU-intensive workloads.
The impact of AI in data center infrastructure is mainly due to these systems' outstanding growth of power densities. With the rise of AI models, average power density values are expected to increase from 17 kW in 2024 up to 30 kW by 2027, as published by McKinsey in the article cited above.
The requirements of high-density computing infrastructure translates into a several key requirements for AI-ready data centers:
- Increased power capacities and the need for advanced electrical systems that guarantee AI-ready data centers are both technically and economically feasible.
- Stronger resiliency, so that systems are able to continue running reliably under stress. In AI data center architecture, resiliency is built along multiple axes: from a focus on reliable backup systems to advanced electrical infrastructure.
- Expanded cooling capacities, with thermal management in AI data centers becoming a key factor in their development, as explored in more detail later in this article.
Regarding the impact of AI workloads, it should be noted that there are nuances between the requirements imposed by AI training compared with inference processes.
For instance, the McKinsey article mentions models like ChatGPT consuming “more than 80 kW per rack” and Nvidia’s advanced chips possibly requiring rack densities of up to 120 kW. Meanwhile, AI inference processes are typically less power-dense.
This distinction can have an impact on a number of particularities, from distinct engineering and electrical requirements to the data center’s location or resiliency requirements. For instance, while inference deployments can be developed in data centers with lower cooling capacities, AI training will require more advanced cooling, such as that provided by liquid cooling for AI servers.
What makes a data center AI-ready? Key characteristics of AI data center infrastructure
Scalability
Trends like edge AI and hyperscale data centers for AI are driving the need for scalable data centers. As more and more companies move towards hosting and running large AI models locally or near the source of data (the “edge” of the network), data centers need to be able to grow accordingly and offer state-of-the-art resources on demand.
Scalable design is thus a crucial part of AI-ready data centers, as able to level up as models grow or new projects start.
Energy efficiency
The capacity to ensure top energy efficiencies by AI-ready data centers is rapidly becoming a key “make-or-break” factor.
This is driven by two key realities: from an economic standpoint, operators must move to reduce operational costs at every opportunity, in order to counterbalance the power hunger of AI computing infrastructure.
On the other hand, energy efficiency is also becoming an imperative as both citizen movements and authorities are placing a heightened scrutiny on data centers’ environmental footprint.
In this context, issues related to energy efficiency, such as PUE optimization, have become a central issue, with a particular focus on data center cooling solutions.
Advanced cooling solutions
With more demanding workloads comes more heat in computing resources. For this reason, advanced thermal management in AI data centers lies at the heart of these infrastructures’ capacities to accommodate more demanding workloads.
Additionally, opting for the right cooling strategy is key to ensuring sustainability and economic viability. The reason for that is that cooling systems typically involve a large share of total data center energy consumption, varying “from about 7% for efficient hyperscale data centres to over 30% for less-efficient [ones]” according to IEA figures.
While different approaches are being experimented with as alternatives to conventional air-based cooling, liquid cooling in data centers is rapidly positioning itself as one of the most promising.
The role of liquid cooling in AI-ready data centers
Why liquid cooling is essential for AI servers
1. Decreased energy consumption
In brief, liquid cooling for AI servers is based on using specifically designed coolant liquids to remove heat from computing equipment.
An approach with a lower energy usage when compared to air-based solutions, thanks to liquids’ greater capacities for heat transfer. A greater capacity that, in turn, ensures faster heat removal while using fewer resources.
In practical terms, these energy savings directly translate into lower operating expenses.
Operators adopting direct-to-chip configurations report measurable decreases in cooling OPEX and improved return on investment compared with conventional air-cooled setups.
2. Higher thermal density and reliability
Put simply, AI workloads involve more heat generation coming from IT equipment. This is where liquid cooling’s higher thermal densities come in to offer more effective cooling.
As a rule, air cooling is regarded as an optimal option for rack densities up to 20 kilowatts (kW) per rack. From there, liquid cooling enables greater performance by offering greater thermal density, dissipating heat more effectively.
Additionally, liquid cooling also delivers more precise and consistent temperature control, with the added benefits this entails, including longer equipment lifespans.
At the same time, as environmental factors become increasingly strategic, liquid cooling solutions offer added benefits: on the one hand, the possibility to design waterless cooling solutions; on the other hand, the option to develop circular heat models by pairing data center cooling and district heating.
These are both critical movements at a time when improving ESG and environmental markers is becoming crucial for data centers’ compliance and overall success.
Types of liquid cooling systems
1. Direct-to-chip cooling
Direct-to-chip configurations work by delivering synthetic dielectric fluids directly to the server components that produce the most heat.
A renowned example of this approach is Google’s direct-to-chip solution deployed for AI training workloads, capable of dealing with the thermal loads from TPUs related to generative AI.
According to research by JLL, direct-to-chip cooling offers optimal results for rack densities between 100 kW and 175 kW. Meanwhile, a review by Dlzar Al Kez et al. (2025) cites direct-to-chip cooling as capable of achieving “a heat capture efficiency in the range of 70–75 %, allowing them to handle significantly higher thermal loads than conventional cooling systems”, as well as lowering “cooling-related energy consumption by up to 72.4 %, translating into significant energy savings and emissions reductions.”
2. Immersion cooling
This method involves submerging server components in specialized, non-conductive fluids that enable efficient heat dissipation.
Research by JLL mentioned above points towards immersion cooling for rack densities beyond 175 kW.
Meanwhile, ASHRAE describes the benefits of immersion cooling being “broad temperature support, high heat capture, high density, and flexible hardware and deployment options.” Additionally, the Dlzar Al Kez et al. review cited above notes that immersion cooling can offer “95 % energy savings and 90 % water savings”, while also achieving “PUE as low as 1.03”.
Global trends in AI-ready data centers
Hyperscale adoption
Hyperscalers are expected to continue leading in demand for AI-ready data centers. Following this, the McKinsey article cited above foresees around 60-65% of AI workloads in Europe and the US “will be hosted on CSP infrastructures and other hyperscaler infrastructures”. This is aligned with research by Precedence, which notes the hyperscale data center market size could reach USD 935.3 billion by 2032, growing at a CAGR of 27.9%.
With access to extensive resources and a leading position in the industry, hyperscaler growth positions these actors as key innovation drivers, including areas such as data center cooling and energy-efficient hardware.
Regulatory and sustainability frameworks
The AI-ready data center is also expected to be subject to further regulatory efforts. The following can be cited among the most impactful in the immediate future:
- ASHRAE TC 9.9 guidelines. A framework focusing on providing thermal guidelines and best practices for power equipment, including issues around temperature ratings or electrical power distribution.
- Uptime Institute Tiers. Provides the standards for classifying data center resilience and redundancy in 4 categories based on service availability times. As resilience becomes a key aspect in the AI-ready data center, certified Tiers III and IV will become increasingly important.
- EU Code of Conduct for Data Centres. A voluntary initiative that recognizes data centers’ best practices in terms of energy efficiency and sustainability.
These evolving standards are providing a roadmap for operators today, pointing towards the need to achieve not just higher densities, but also higher efficiencies, availability and environmental responsibility.
The future of AI-driven infrastructure: innovation and the need for strategic partnerships
As seen across the article, AI data center architecture is undergoing a series of profound transformations today and in the coming years.
Previously unseen innovation can be expected: from the expansion towards edge AI configurations, to the use of AI as part of smart data center management, including smart cooling and predictive maintenance in search for further efficiencies.
In this changing landscape, the need to move fast and embrace constant innovation is directly tied to operators’ capacities for building trusted, valuable collaborations. As such, finding the right strategic partners for data center design, development, and operation has never been more critical.
This is where ARANER comes in. As a global reference in thermal engineering and cooling infrastructure, we put our expertise to work in order to help operators build AI-ready data centers that are uniquely equipped for a competitive landscape, today and in the coming years.
ARANER’s long experience in delivering complex thermal engineering projects positions us as a key ally for building AI data center architecture.
From design to engineering development and implementation, we support operators in achieving AI-ready data centers where efficiency and sustainability are at the forefront. This includes our capacity to deliver cutting-edge liquid cooling for data centers, as well as the possibility to not only design and develop data centers, but also provide operational capabilities.
Additionally, thanks to our experience in successfully developing complex district cooling projects, we’re uniquely positioned to build circular models where waste heat from data centers feeds into sustainable district heating.
Ready to level up and transform into an AI-ready data center?
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