What is an AI data center?

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” and “colocation” describe two types of data centers commonly used by organizations for AI.

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.

In an early 2025 blog post, Microsoft named AI the “electricity of our age.” Whether that declaration is hyperbole or spot-on remains to be seen. However, the adoption of AI tools such as OpenAI’s ChatGPT by millions of nonexpert users has moved at an electrifying pace. This clear productivity and monetization potential of AI capabilities has led to an intense flow of new AI productivity tools, agents and content generators.

Open source models and the continued democratization of AI means it’s not just major players making waves in the AI ecosystem. Almost any entity can be a tech company, if they can identify an AI use case and adopt the IT infrastructure to get it done. According to a 2024 report by the IBM Institute for Business Value (IBM IBV), 43% of C-level technology executives say their concerns about their technology infrastructure have increased over the past six months because of generative AI, and they now are focused on optimizing their infrastructure for scaling it.

Meanwhile, the data center industry has grown to fit demand. Data center infrastructure around the globe is increasingly AI-ready, capable of processing high volumes of complicated computations and requests. Currently, the Asian Pacific and North American regions have the highest proliferation of data centers, particularly in areas such as Beijing, Shanghai, northern Virginia and the San Francisco Bay area.¹

Substantial investments from big tech have also signaled growth for the AI data center sector. In 2025, Microsoft plans to invest approximately USD 80 billion on data center construction and Meta is investing USD 10 billion in a new, four million-square-foot hyperscale data center development in the US state of Louisiana.

There are several of the unique features and functions key to AI-ready data centers:

• High-performance computing
• Advanced storage architecture
• Resilient and secure networking
• Adequate power and cooling solutions

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 applications.

The velocity and high computational needs of AI workloads require vast data storage with high-speed memory. Solid-state drives (SSDs)—semiconductor-based storage devices, which typically use NAND flash memory—are considered critical storage devices for AI data centers. Specifically, NVMe SSDs, which have the speed, programmability and capacity to handle parallel processing.

Data center GPUs, accelerators and some SSDs also use high-bandwidth memory (HBM). This type of memory architecture enables high-performance data transfer with lower power consumption than dynamic random-access memory (DRAM), a more traditional memory architecture.

Another typical facet of AI data center design is data storage architecture that can accommodate fluctuations in data demands, such as unexpected surges. Instead of running workloads on dedicated hardware, many data centers (both AI and conventional) use a cloud architecture where physical storage is virtualized.

Virtualization is the division of a single computer's hardware components (such as memory and storage) into multiple virtual machines. It enables better resource usage and flexibility by allowing users to run multiple applications and operating systems on the same physical hardware.

Virtualization is also the technology that drives hybrid cloud capabilities. Hybrid cloud gives organizations increased agility and flexibility to connect cloud and on-premises environments, which is critical for adopting data-intensive generative AI.

AI must be fast. Users expect instant responses from online AI applications and autonomous vehicles need to make split-second decisions on the road. Therefore, AI data center networking must be able to support the high-bandwidth requirements of AI workloads with low latency. For hyperscale data centers, bandwidth requirements can range from several gigabits per second (Gbps) to terabits per second (Tbps).

Traditional data centers use fiber optics for their external communications networks, but the racks in data centers still predominantly run communications on copper-based electrical wires. Copackaged optics, a new process from IBM Research, promises to improve energy efficiency and boost bandwidth by bringing optical link connections inside devices and within the walls of data centers used to train and deploy large language models (LLMs). This innovation might significantly increase the bandwidth of data center communications, accelerating AI processing.

Almost all modern data centers use virtualized network services. This capability enables the creation of software-defined overlay networks, built on top of the network's physical infrastructure. It allows for the optimization of compute, storage and networking for each application and workload without having to make physical changes to the infrastructure.

AI data centers require cutting-edge network virtualization technology with better interconnection, scalability and performance. It must also be able to address data privacy and security concerns related to the large volume of data used to train generative AI models.  In an IBM IBV survey, 57% of CEOs say concerns about data security will be a barrier to adopting generative AI.

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%.