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An AI data center is a facility that houses the specific IT infrastructure needed to train, deploy and deliver AI applications and services. It has advanced compute, network and storage architectures and energy and cooling capabilities to handle AI workloads.

While traditional data centers contain many of the same components as an AI data center, their computing power and other IT infrastructure capabilities vary greatly. Organizations that want to capitalize on the benefits of AI technology would benefit from access to the necessary AI infrastructure.

There are many routes to this access, and most businesses will not need to build their own AI data centers from the ground up—a monumental undertaking. Options such as hybrid cloud and colocation have lowered the barrier to entry so that organizations of all sizes can reap the value of AI.

AI data centers share many similarities with traditional data centers. They each contain hardware such as servers, storage systems and networking equipment. Operators of both need to consider things such as security, reliability, availability and energy efficiency.

The differences between these two kinds of data centers stem from the extraordinary demands of high-intensity AI workloads. In contrast to AI data centers, typical data centers contain infrastructure that would quickly be overwhelmed by AI workloads. AI-ready infrastructure is specially designed for the cloud, AI and machine learning tasks.

For example, conventional data centers are more likely to be designed for and contain central processing units (CPUs). Whereas AI-ready data centers require high-performance graphics processing units (GPUs) and their IT infrastructure considerations, such as advanced storage, networking, energy and cooling capabilities. Often, the sheer number of GPUs necessary for AI use cases also requires far more square footage.

Hyperscale data centers are huge, including at least 5,000 servers and occupying at least 10,000 square feet of physical space. They provide extreme scalability capabilities and are engineered for large-scale workloads (such as generative AI). They are in wide use globally by cloud providers such as Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP) for various purposes that include artificial intelligence, automation, data analytics, data storage, data processing and more. 

A colocation data center refers to a situation where one company owns a hyperscale data center and rents out its facilities, servers and bandwidth to other companies.

This setup allows businesses to enjoy the benefits of hyperscale, without the major investment. Some of the world's biggest users of colocation services are Amazon (AWS), Google and Microsoft. For example, these cloud service providers lease significant data center space from a data center operator called Equinix. Then, they make their newly acquired space available to customers, renting it out to other businesses.

High-performance computing

An AI-ready data center needs high-performance computing (HPC) capabilities such as those found within AI accelerators. AI accelerators are AI chips used to speed up ML and deep learning (DL) models, natural language processing and other artificial intelligence operations. They are widely considered to be the hardware making AI and its many applications possible

GPUs, for example, are a type of AI accelerator. Popularized by Nvidia, GPUs are electronic circuits that break complicated problems into smaller pieces that can be solved concurrently, a methodology known as parallel processing. HPC uses a type of parallel processing known as massively parallel processing, which employs tens of thousands to millions of processors or processor cores. This capability makes GPUs incredibly fast and efficient. AI models train and run on data center GPUs, powering many leading AI applications.

Increasingly, AI-ready data centers also include more specialized AI accelerators, such as a neural processing unit (NPU) and tensor processing Units (TPUs). NPUs mimic the neural pathways of the human brain for better processing of AI workloads in real time. TPUs are accelerators that have been custom built to speed tensor computations in AI workloads. Their high throughput and low latency make them ideal for many AI and deep learning application

Adequate power and cooling solutions

The high computational power, advanced networking and vast storage systems in AI data centers require massive amounts of electrical power and advanced cooling systems to avoid outages, downtime and overload. Goldman Sachs anticipates that AI will drive a 165% increase in data center electricity demand by 2030. And McKinsey's analysis suggests that the annual global demand for data center capacity might reach 171 to 219 gigawatts (GW). The current demand is 60 GW.

To meet these intense energy consumption and cooling requirements, some AI data centers employ a high-density setup. This strategy maximizes data center square footage with compact server configurations that perform better, are more energy efficient and contain advanced cooling systems.

For example, liquid cooling often uses water rather than air cooling to transfer and dissipate heat. It offers greater efficiency in handling high-density heat and improved power usage effectiveness (PuE)—a metric used to measure data center energy efficiency. Another cooling method, hot and/or cold aisle cooling containment, organizes server racks to optimize airflow and minimize the mixing of hot and cold air.

Given these significant power requirements, today's organizations often seek a balance between their AI ambitions and sustainability goals. One impressive example comes from Apple, one of the world's largest owners of hyperscale data centers. Since 2014, all of Apple's data centers have run completely on renewable energy through various combinations of biogas fuel cells, hydropower, solar power and wind power.

Others are looking toward extraterrestrial energy sources, hoping to take advantage of the high-intensity solar power in space to build new data centers. Breakthroughs in orbital data centers might lower energy costs considerably for training AI models, potentially cutting power expenses by as much as 95%. 

The $5.3 trillion AI Investment (debt)