An AI data center is a physical facility that houses the IT infrastructure required to handle the enormous workload generated when training, deploying, and delivering Artificial Intelligence services. The infrastructure required to cater to the needs of a large language model is different from that of other data centers.

In an AI data center, power consumption could reach up to 100 kW per rack, depending on the workload.

AI data centers use High-performance Computing (HPC), Graphic Processing Units (GPUs), Neural Processing Units (NPU), a powerful and secure networking system, NVMe SSDs (Non-volatile memory express solid state drives), Advanced cooling solutions, monitoring sensors (Ekko sensors for airflow, temperature, humidity), etc.

Let us now understand in detail about AI data centers.

Emergence of Artificial Intelligence Data Center

Solutions such as OpenAI's ChatGPT and Google Search Engine, which are widely used in everyday life for quick information access, as well as social media platforms like Snapchat, Instagram, Facebook, and others, depend heavily on data center capacity infrastructure.

High-performance computing (HPC) is required to handle such massive amounts of data. In addition, AI data centers use a significant amount of power, with racks handling up to 50kW, which traditional data center operators can't provide.

Artificial Intelligence’s power is much more than we can comprehend. It is so powerful that 65-70% of the total time spent on data processing and collection can be automated with an ease that's beyond what is expected of user experience. This is one of the reasons why companies are actively switching to AI to make their tasks easier, thereby creating a surge in the number of AI data centers.

Traditional Data Centers vs AI Data Centers

Traditional data centers and AI (Artificial Intelligence) data centers are similar and dissimilar in several ways. They are similar in hardware requirements, and other data center infrastructure including servers, storage systems, cabling, and networking systems, to enhance efficiency, reliability, and security among their users.

The differences between AI data centers and traditional data centers are discussed below.

How Much Power Does an AI Data Center Consume?

AI data center power consumption varies greatly depending on size and type. According to research, global power demand from data centers will increase 50% by 2027 and by as much as 165% by the end of the decade (compared with 2023).

AI data centers work differently, resulting in higher energy consumption. To maximize computing power within a limited rack space, systems are being deployed in high-density configurations, resulting in increased power per rack and a higher energy demand per square meter. In AI data centers, racks can even reach up to 100 kW.

The International Energy Agency estimates that global data center electricity consumption in 2022 was 240-340 TWh, or around 1-1.3% of global final electricity demand. 

IEA also projected that electricity demand from data centers, AI being the most significant driver of this increase, is set to more than double by 2030 to around 945 terawatt-hours (TWh), a bit more than the entire electricity consumption of Japan.

Key Features of an AI Data Center

An AI data center is far more advanced and is equipped with modern data center architecture and technology that enables operators to smoothly navigate through various servers and data loads with ease.

Some of the most prominent features of an AI data center are: 

High-Performance Computing (HPC)

High-performance computing (HPC) capabilities lie at the core of AI and ML, enabled by AI accelerators—specialized chips designed to efficiently speed up AI workloads.

What is a GPU? What is its role in the Computing Process?

One of the most popular AI accelerators is the Graphics Processing Unit (GPU), developed by Nvidia. GPUs offer parallel processing, breaking complex problems into smaller tasks that can be solved alongside other tasks.

Beyond GPUs, data centers are increasingly adopting specialized AI chips like Neural Processing Units (NPUs) and Tensor Processing Units (TPUs). 

What is NPU?

NPUs mimic human brain neural pathways, offering enhanced real-time AI processing, while TPUs are custom-built to accelerate tensor computations, providing high output and low latency. 

Selecting the right technologies is crucial for organizations to remain competitive in the rapidly evolving AI landscape.

Advanced, Resilient, and Secure Networking Infrastructure

AI data center networking infrastructure requires low-latency networking to meet the demands of AI applications that are ever-increasing in every millisecond.

Hyperscale data centers require massive bandwidth, often reaching terabits per second. While traditional data center networking cannot bear this load and catch-up to the time constraints.

AI data centers use different network technologies, including InfiniBand, RDMA over Converged Ethernet (RoCE), and Scheduled Ethernet, comparing their costs, performance, and operational complexities. Among this, Enhanced Ethernet (ROCE) offers cost-effectiveness and high-performance for all AI workloads.

Modern AI data centers also use network virtualization, enabling software-defined networks that optimize compute, storage, and networking resources without physical changes. For AI, advanced network virtualization ensures better scalability, performance, and security.

Advanced Storage Infrastructure

AI data centers demand readily scalable storage to meet the unprecedented, rapidly expanding computational workload of modern AI data centers. 

NVMe SSDs (Non-Volatile Memory Express solid-state drives), built on NAND (a type of non-volatile memory) flash memory, are critical for these environments, offering the speed, programmability, and capacity needed for parallel processing and real-time data access.

Role of High-bandwidth memory in Data Transfer (HBM)

High-bandwidth memory (HBM) is also increasingly used in GPUs, accelerators, and few SSDs, enabling rapid data transfer with lower power consumption compared to traditional infrastructure.

To handle these unprecedented data surges, AI data centers often rely on cloud-based storage architectures with virtualization. 

Adequate Power and Innovative Cooling Solutions

Since AI data centers work nonstop all day, they require more than just traditional cooling equipment. 

The air cooling does not perform efficiently for AI data centers, so alternative thermal management & energy management solutions can be used, including liquid cooling, immersion cooling, air cooling (for lower-density setups), high power density, and heat reuse (waste‑heat recovery for district heating).

Liquid cooling also improves the data center's Power Usage Effectiveness (PUE), which is an important data center energy efficiency metrics used to identify and optimize power usage of the data center.

Many of the data centers have hybrid cooling options to make their operations more sustainable and efficient.

In addition, adequate power generation and backups are required for powering the cooling systems throughout the year. Some of the data centres, like those operated by Apple, run on fully renewable energy sources. Even a few hours of downtime can cost millions to operators and decrease their authenticity among users. 

Power back-up solutions provided by various companies like IBM, Schneider Electric, Vertiv, ABB, and others can be efficient for AI data centers and also help in reducing downtime and unanticipated system failures.

5 Most Trending AI Data Center Infrastructure Providers

Hyperscale vs Colocation

Building an AI data center can be quite expensive. To avoid building and maintaining an AI data center from scratch, organizations can access various options available in the market, such as hybrid cloud, renting space in hyperscale data centers, and opting for colocation for AI hardware.

Knowing about the key difference between hyperscale data centers and colocation data centers is essential. It is crucial to choose the right partner whom you can trust with your critical data.

Users can choose among the two most popular options to access AI data center facilities on rent, one is hyperscale data center, which are massive-scale data center facilities designed to handle huge amounts of workload. It carries more than 50,000 servers and spans over 10,000 sq.ft of area.

Another popular option is colocation, which are third-party rental facilities designed to access modern data center services on-demand.

Many big tech giants, such as Amazon Web Services, Google Cloud Platform, and Microsoft Azure, lease bulk servers to further provide space to their customers on rent. 

Colocation data centers offer great control over infrastructure placement, network path,s and hardware configurations that help organizations to fulfill their needs that may be expensive to achieve in public cloud facilities. 

Moreover, colocation providers offer more fluid cost structures, making it feasible for organizations.

You can explore the top colocation providers to avail data center colocation facilities offered by some of the major tech giants and improve your operational efficiency by renting space on demand as per your requirements.

The Future of AI Data Centers:

The surge in the use of Artificial intelligence and machine learning in day-to-day life has majorly contributed to an increased demand for space and IT infrastructure required to train and deploy AI.

A strong shift towards nuclear energy sources has also been observed, in addition to renewable energy to cater the increasing energy requirements of AI data centers. Emerging technologies, such as neuromorphic computing, are continuously improving the operational efficiency of AI data centers.

Where are the Latest AI Data Center Facilities Located?

Are you seeking a platform that provides reliable, high-quality, and timely project insights for global data center facility projects?

Discover the Global Project Tracking (GPT) platform by Blackridge Research, designed to provide you with the most recent Global Data Center Facility Projects better and faster across various stages of development :

• Upcoming projects 

• Tender notices 

• Contract award announcement 

• Projects in progress or under construction 

• Successfully completed projects 

Book a free demo to learn more about the Global Data Center Facility Projects database and how we can help you achieve your goals