A new dawn for tech infrastructure: Building AI-ready data centres

Author: Isha Jain

By Darren Watkins, Chief Revenue Officer at VIRTUS Data Centres

It’s no secret that the past year has seen an enormous surge in the influence of Artificial Intelligence (AI) and Machine Learning (ML). What was once considered a niche technology has now exploded into the mainstream, profoundly impacting every aspect of our lives – from how businesses function to enhancing personal productivity, and even influencing the way we interact with entertainment and navigate daily tasks.

This explosion is having a knock-on effect on the infrastructure which underpins and powers our modern lives. Data centres, traditionally the backbone of many technological advancements, are now faced with the imperative to do more than ever to support data storage management and retrieval, and cloud services in an always-on manner. The rapid growth of AI highlights the pressing need for data centres to be even more agile, innovative, and collaborative – driving this new era.

In meeting this demand, data centre operators are swiftly adapting their facilities to accommodate the unprecedented requirements of AI and ML workloads. This entails not only scaling up capacity but also implementing advanced technologies, such as liquid cooling systems and optimised power distribution architectures, to ensure optimal performance and energy efficiency.

It’s important to note that achieving ‘AI readiness’ goes beyond technological functionality – it hinges on the imperative of early engagement with those customers who need AI ready infrastructure. This strategic engagement not only ensures a symbiotic relationship, but also serves as the linchpin for developing a truly flexible and customised infrastructure that can seamlessly evolve with the fast-growing and ever-changing technological landscape.

So, how are data centre operators rising to the challenge of this new era of demands support AI and ML?

A new approach to location

In the past, network technology infrastructure was carefully planned to reduce latency and data processing speeds. However, with the rise of AI and ML workloads, this approach is changing. Unlike other types of data processing that require low latency, AI and ML tasks have different priorities. This shift in focus is leading to a reconsideration of what makes an ideal location for data centres. Now, there’s a growing preference for larger more efficient campuses that can generate between 200 and 500MW of power and have access to renewable energy sources.

This change in strategy represents a departure from the previous emphasis on reducing latency. Instead, it reflects a broader understanding of how AI and ML are integrated into technology systems. The move toward larger campuses isn’t just about accommodating less latency-sensitive tasks. It’s a deliberate decision that takes into account the costs and benefits of operating at a larger scale. By prioritising bigger campuses, data centre providers can often achieve greater efficiency, both in terms of cost and sustainability. This shift challenges the traditional idea that proximity to users is always the most important factor for data centres.

Instead, it suggests that focusing on size and efficiency can lead to better overall outcomes.

Beyond size, the role of edge computing remains important. A fully integrated AI solution requires connectivity to all aspects of a business’s systems, and whilst core language models and inference models may reside in mega-scale campuses, there is an ongoing need for edge solutions in metropolitan cities, ensuring full integration. Additionally, for some companies, edge data centre solutions are essential for cost-effectiveness. For example, content distribution networks delivered via local edge data centres facilitate seamless iOS upgrades for iPhones, negating the need for individual data centres in every country.

Defying labels: mega-scale and the edge

It is clear that AI and ML are changing data centre requirements and it’s often the case that bigger is better. But what will the new generation of data centres be called – hyperscale 2.0, megascale, gigascale, or something else?

Whatever the label ends up being, it’s important to remember that “hyperscale” isn’t merely about physical size; it’s now a reflection of the specific customer it refers to. The term, “mega-scale campuses to host hyperscale customers”, might define the ongoing industry transformations more accurately.

Regardless of the terminology, one common challenge is evident; meeting the significant capacity demands of these customers. The current limitations of European hyperscale facilities to address the growing AI market underscore this challenge, and mega-scale campuses may be the answer. VIRTUS’ 200MW campus in Wustermark, Berlin, (under construction) is a great example of large-scale, sustainable facilities being built that are AI ready, and prepared to meet these future cloud, hyperscale and customer demands.

The increasing importance of sustainability

Sustainability plays a critical role in shaping the future of data centres, especially in the context of the rapid integration of AI and ML technologies. As these advanced workloads continue to drive demand for computational power and data storage, data centre operators are increasingly realising the importance of reducing environmental impact. This means not only optimising energy efficiency but also embracing renewable energy sources like solar and wind power to meet the growing energy demands sustainably.

In this evolving landscape, the emphasis on sustainability isn’t just a buzzword; it’s a strategic imperative that aligns with the broader goals of AI and ML integration. By prioritising environmentally conscious practices, data centres can support the scalability and reliability required for AI and ML workloads while minimising their carbon footprint. This holistic approach ensures that as AI and ML reshape industries and drive innovation, they do so in a way that is both technologically advanced and environmentally responsible.

VIRTUS understands the dual responsibility of meeting the demands of AI and ML workloads while mitigating their environmental impact. That’s why we’re committed to sustainability, working tirelessly to innovate and reduce our carbon emissions. Our recent strides towards achieving net zero carbon emissions by 2030, as showcased in our sustainability report, underscore our dedication to building a greener future while powering the advancements of AI and ML technologies.

A road to innovation

Facing unprecedented technological advancement, data centres continue to be more than mere facilities; they remain the bedrock infrastructure upon which the digital future is built. And with AI and ML driving the next wave of innovation, the role of data centres is becoming even more vital.

Being dynamic, innovating providers are not only key to shaping a more intelligent and sustainable digital world, but provide the development and investment made in delivering new technologies for customers and consumers, whilst increasing productivity and remaining committed to increasingly greater environmentally sustainable facilities.