AI Data Centers: A Popular Term That’s Hard to Define

AI Data Centers: A Popular Term That’s Hard to Define

Article originally written by Christopher Tozzi and featured in DataCenter Knowledge, August 2025

Modern AI workloads are reshaping data center requirements, but what truly differentiates an ‘AI data center’ from traditional facilities remains surprisingly ambiguous.

The term ‘AI data center’ is appearing with increasing frequency in conversations about modern data centers. This is unsurprising, given that modern AI technology has placed novel demands on data centers and the infrastructure inside them.

Yet, it can also feel like a hollow term because it remains unclear how, exactly, an AI data center is different from other data centers. Before casually using this terminology – or assuming that AI workloads require fundamentally different data centers than those already in existence – it's important to clarify what the term actually means.

What Is an AI Data Center?

The short definition of ‘AI data center’ is a facility that has been specifically designed for hosting AI workloads.

This isn’t an especially satisfying definition, however, because the needs of AI workloads can vary widely. Thus, there are no distinctive data center features that separate AI-oriented facilities from “regular” data centers.

Plus, few people were talking about AI data centers until generative AI went mainstream with the launch of ChatGPT in 2022. That’s the point when the media started discussing AI data centers as a unique type of data center. Before then, other types of AI workloads – like predictive and descriptive analytics – were running perfectly well in traditional data centers, and no one was suggesting that AI required a fundamentally new type of data center.

Key Features of AI Data Centers

That said, it is reasonable to conclude that modern AI workloads do benefit from data centers designed specifically to meet their unique needs. Exactly how a data center does this can vary, but common features of AI-oriented data centers may include:

• Larger overall capacity. Arguably, this is the most important defining characteristic of an AI data center: It’s simply bigger – in the sense that it can provide more total power input and accommodate a larger number of servers – than most traditional data centers.
• The availability of servers equipped with GPUs or other hardware devices that can accelerate some AI tasks, like AI model training and inference.
• Optimized networking equipment, like DPUs, that can help to process traffic very rapidly – another important requirement for many AI workloads, especially those that need to process large quantities of data in real time.
• High-efficiency data center cooling systems, like immersion cooling, which are important in some cases for dissipating the large quantities of heat produced by AI workloads.
• Potentially, specialized power systems, like behind-the-meter energy solutions, which can help meet the high energy requirements of AI workloads.

But here’s the catch: these aren’t exclusive to AI. As the article notes, “all of these features exist in many traditional data centers, too.” Hyperscale providers have been deploying them for years, even before generative AI entered the spotlight. That makes the distinction less about a brand-new category and more about scale, optimization, and workload mix.

Key takeaway: The most useful lens isn’t a rigid definition but adaptability. As Data Center Knowledge concludes, “it’s more fruitful to ask how adaptable a data center is to AI workloads” rather than chase the “AI data center” label. At Galtra, we agree. Our focus is helping organizations design and adapt facilities with the power, cooling, and flexibility to meet today’s demands — and tomorrow’s.