The rise of artificial intelligence (AI), particularly generative AI and machine learning, is reshaping how data centers must designed. As AI workloads demand more power, cooling, and networking, the hardware and hence the data center facilities must evolve to support these growing needs. Our AI-ready data centers are designed to accommodate the specific challenges that our clients’ AI infrastructure brings, while maintaining scalability and flexibility for the future. 

One of the core features of an AI-ready data center is its ability to handle IT Equipment (ITE) with high power densities. Unlike traditional IT workloads, AI clusters, particularly those utilizing GPUs and specialized AI accelerators, can demand 5-10 times more power (and even more) than typical data center hardware. Our facilities are equipped to deliver higher power per rack, ensuring AI hardware receives the energy needed at each individual rack without compromising the resiliency. 

To manage this increased power load, cooling becomes equally critical. Traditional air-cooling methods often fall short when faced with the heat generated by dense AI hardware. Our AI-ready data centers can accommodate advanced cooling technologies, including liquid cooling systems such as direct-to-chip cooling and immersion cooling. These systems efficiently dissipate heat from high-performance hardware, maintaining the necessary thermal conditions for continued uptime and operational efficiency. Intense workloads will generate more heat that could be turned into an asset instead of a liability. This is why, our data centers are prepared to deliver the excess heat for heat reuse applications, helping to decarbonize the heating industry and contribute to the well-being of the local communities. 

Additionally, the physical infrastructure must support heavier racks as AI hardware tends to be sometimes larger but especially more weight intensive. Floor loading capabilities have been designed to bear the added weight of these denser setups. 

We are also conscious that an AI cluster needs to accommodate very different kinds of hardware. This happens for AI training but especially for AI inference applications. Cloud hardware in an AI inference data center provides flexibility, scalability, and handles non-AI tasks like data pre-processing and application management, allowing dynamic resource allocation and integration with broader enterprise systems, ensuring smooth, efficient AI operations. This potentially required variety of hardware types has been considered in the design for the power and cooling distribution, understanding that different racks with different needs will have to share the same IT space. 

Our data centers employ a modular design to ensure flexibility in supporting diverse workloads, including AI infrastructure. This modularity allows us to easily scale power, cooling, and networking systems as client requirements evolve. Clients deploying AI clusters today can start small and scale up, adding compute, storage, and network resources as needed, without requiring a full redesign of the data center space. 

This design approach not only accommodates legacy hardware but also enables clients to incorporate future innovations. Our modular framework allows for quick adaptation and integration, making the transition seamless and future-proofing your AI infrastructure. 

AI workloads require significantly more network bandwidth than traditional applications. To support the massive amounts of data transferred between compute nodes, storage systems, and AI models, we have designed our AI-ready data centers to accommodate the extensive network cabling needed for these deployments. This increased network capacity ensures that clients can run even the most data-intensive AI applications without bottlenecks. 

In addition to raw bandwidth, our networking infrastructure allows intensive data connections to ensure low-latency interconnections within AI pods (which house compute, storage, and network resources, critical for large-scale machine learning models and real-time inference tasks. 

Given the power demands of AI clusters, power availability is a central consideration in where our data centers are located. We work closely with utility operators to ensure we have access to sufficient capacity, which can then be distributed to our clients. This collaboration allows clients to “land and expand” within our environments—starting with smaller AI deployments and scaling as their power and infrastructure needs grow. 

Choosing data center locations that have reliable access to abundant power is essential for AI operations, ensuring that clients’ future growth is not constrained by limited energy availability. 

AI training facilities do not need to be in metro areas, unlike AI inference clusters. This latter requires low latencies to deliver appropriate user experiences when using the AI applications, while the first one is focused on rapid and competitive model training. Most of our locations are mostly within metro areas in Europe with wonderful connectivity to the regional IXPs but staying far enough to have space and power for scaling up.  

At the core of our AI-ready data centers is the flexibility to meet both current and future AI infrastructure demands. By offering modular, scalable solutions with high power density, advanced cooling, and enhanced networking, we ensure that your AI deployments can grow without constraint. As AI continues to push the boundaries of what’s possible, our data centers are prepared to support your journey, offering the stability, power, and flexibility needed to drive your AI applications forward.