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High-performance data centers: The engine of scalable AI   

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AI’s transformative power doesn’t just stem from advanced algorithms—it’s rooted in the infrastructure that supports them. In the era of data gravity, the sheer volume and proximity of data shape the flow of innovation. Large datasets attract applications, services and other data in the digital world. In the context of AI, this means that where your data lives dictates where your advanced AI models must be trained and deployed. The increasing scale and complexity of data demand infrastructure that can seamlessly process, store and move it to where insights are needed most. 

Getting from AI strategy to results requires a substrate built for the job. Read this white paper to discover how high-performance data centers (HPDCs) provide the specialized, localized compute supported by advanced cooling systems to efficiently process heavy data loads while ensuring privacy, control and sovereignty. Dive into the details of their critical role as engines of secure, scalable and sustainability-focused AI.

What you'll find inside:

  • The high-performance data center difference: Get a clear breakdown of what sets a high-performance data center apart from a traditional one.
  • A blueprint for action: Discover how to center your AI strategy on infrastructure that ensures security, sovereignty and scalability.
  • AI factories and connected AI: Understand the infrastructure requirements of each of these two key elements of effective AI implementations.
  • Scaling with a commitment to sustainability: See how high-performance data centers rely on efficient design, clean energy and advanced cooling to keep AI's growing energy footprint under control.

Frequently Asked Questions

 

High-performance data centers (HPDCs) are purpose-built facilities designed to handle massive compute workloads for AI. They feature advanced processors, high-density cooling and low-latency connectivity, enabling faster model training, real-time inference and global scalability.

HPDCs combine high rack densities with advanced liquid cooling and renewable energy sourcing to deliver efficient performance. This reduces energy consumption while supporting large-scale AI workloads, ensuring growth without compromising environmental goals.

Data gravity refers to the tendency of large datasets to attract applications and services. For AI, this means placing compute resources close to data sources to minimize latency, improve security and comply with sovereignty regulations—something HPDCs are designed to achieve.

HPDCs provide fortified environments with redundant power, layered access controls and encryption protocols. They also enable geographic control, helping enterprises meet data sovereignty and regulatory requirements without sacrificing performance.

AI factories are centralized hubs for training and refining models, while connected AI refers to distributed, interconnected networks that deliver real-time insights at the edge. HPDCs bridge these approaches, enabling both high-volume training and low-latency deployment.

Hybrid Multicloud Networking Edge Computing Sustainability Colocation Interconnection Digital Transformation