Data centers are the backbone of the digital world, powering everything from websites and apps to cloud servers and the operations of large enterprises. With the integration of AI, the next-gen AI data center is poised for even bigger growth.

There’s an insatiable appetite for AI, given that it can handle intensive computer applications. In fact, global spending on AI infrastructure will reach a whopping $422.55 billion by 2029.

There’s no doubt that AI can make data centers smarter, faster, and more secure. But can an AI system self-operate a data center without human intervention? Let’s find out.

What is the Current State of AI Data Centers?

The current state of AI is very encouraging, allowing an AI-powered data center to sift through massive volumes of data and make multiple computations simultaneously. AI and machine learning (ML) algorithms also automate data center tasks and improve their reliability, especially when servers are grappling with data-intensive workloads.

Here are a few ways in which AI makes data centers more efficient:

• Better Performance. AI manages and monitors the network traffic and redirects it, reducing energy consumption. It can also detect network or equipment anomalies, quickly leading the resolution process.
• Sustainability. AI enhances performance to achieve superior power and energy usage. This makes it is more sustainable than the existing resource-intensive data centers.
• Better Security. AI can detect unusual activity and deter cyberattacks, thus preventing data theft or loss.

Challenges of AI Data Centers

Currently, only large-scale organizations such Google, Microsoft, Amazon, and a few others have a fully functional AI data center. Let’s look at the challenges that are preventing AI’s large-scale adoption.

• Machine Learning. ML represents a significant opportunity. However, to operate ML programs, data center operators must train them with huge quantities of data—something that isn’t easy for small operators.
• Infrastructure. The infrastructure required to operate AI data centers is expensive as well as being challenging to deploy and maintain.
• Security. As AI solutions are not foolproof, data privacy is an ongoing concern.

The greatest obstacle, however, lies in implementation. Building an AI-enabled data center is a monumental task. Furthermore, you need high-performance computing (HPC) to handle the enormous computing power required. These data centers also need specialized hardware, enough storage, and an effective networking setup to manage large matrix computations.

The overall impact of these issues is that current AI data centers need a lot of energy to run the systems and also to cool them down. Indeed, a significant portion of the energy utilized by a data center goes toward cooling down the racks and servers.

As a result, we need a better solution for keeping servers at an optimal temperature.

How AI Data Centers Can Benefit from Immersion Cooling

Data centers are the backbone of the digital world, powering everything from websites and apps to cloud servers and the operations of large enterprises. With the integration of AI, the next-gen AI data center is poised for even bigger growth.

There’s an insatiable appetite for AI, given that it can handle intensive computer applications. In fact, global spending on AI infrastructure will reach a whopping $422.55 billion by 2029.

There’s no doubt that AI can make data centers smarter, faster, and more secure. But can an AI system self-operate a data center without human intervention? Let’s find out.

What is the Current State of AI Data Centers?

The current state of AI is very encouraging, allowing an AI-powered data center to sift through massive volumes of data and make multiple computations simultaneously. AI and machine learning (ML) algorithms also automate data center tasks and improve their reliability, especially when servers are grappling with data-intensive workloads.

Here are a few ways in which AI makes data centers more efficient:

• Better Performance. AI manages and monitors the network traffic and redirects it, reducing energy consumption. It can also detect network or equipment anomalies, quickly leading the resolution process.
• Sustainability. AI enhances performance to achieve superior power and energy usage. This makes it is more sustainable than the existing resource-intensive data centers.
• Better Security. AI can detect unusual activity and deter cyberattacks, thus preventing data theft or loss.

Challenges of AI Data Centers

Currently, only large-scale organizations such Google, Microsoft, Amazon, and a few others have a fully functional AI data center. Let’s look at the challenges that are preventing AI’s large-scale adoption.

• Machine Learning. ML represents a significant opportunity. However, to operate ML programs, data center operators must train them with huge quantities of data—something that isn’t easy for small operators.
• Infrastructure. The infrastructure required to operate AI data centers is expensive as well as being challenging to deploy and maintain.
• Security. As AI solutions are not foolproof, data privacy is an ongoing concern.

The greatest obstacle, however, lies in implementation. Building an AI-enabled data center is a monumental task. Furthermore, you need high-performance computing (HPC) to handle the enormous computing power required. These data centers also need specialized hardware, enough storage, and an effective networking setup to manage large matrix computations.

The overall impact of these issues is that current AI data centers need a lot of energy to run the systems and also to cool them down. Indeed, a significant portion of the energy utilized by a data center goes toward cooling down the racks and servers.

As a result, we need a better solution for keeping servers at an optimal temperature.Although AI allows data centers to work optimally, it also heats up the equipment. As such, heat-maximized data center equipment is at risk of degrading or even premature failure.

Liquid immersion cooling is a highly efficient and sustainable cooling method that knocks the socks off traditional air-cooling systems. By submerging the IT equipment in dielectric fluid rather than cooling them with chilled air, the servers are cooled far more efficiently and far less power is consumed.

Additionally, airflow methods use fans that create a lot of noise and lead to a significant amount of water wastage. Replacing these systems with immersion cooling leads to zero noise and no water wastage.

Combining immersion cooling with an AI-based data center results in a truly robust operating system. AI optimizes data server utilization with smart capacity planning, predictive maintenance, better agility, traffic monitoring, and enhanced security.

Immersion cooling systems are known to reduce cooling energy costs by up to 90%. That said, the technology is not without some challenges. For instance, immersion cooling infrastructure can be expensive to set up—especially because systems aren’t traditionally built for liquid cooling. This is one of the major reason why only larger-scale organizations are picking up this cooling solution.

To sum up, the implementation of AI in immersion cooling is overwhelmingly positive. This is especially true if you consult with GRC! We provide turnkey immersion cooling solutions, helping our clients to leverage the best of this technology.

How AI Data Centers Can Prepare for the Future of AI

In the future, data centers will be larger—and so will the racks. With rising demand, data center operators must have the requisite infrastructure to keep the servers running and processing information.

To prepare for an AI-oriented data center future, you’ll be required to:

• Have Scalable Infrastructure. Invest in scalable infrastructure to meet the demands of powerful AI-based applications. Be prepared well in advance to handle new equipment and its maintenance. This includes everything from cooling systems to storage servers and handling AI computing resources.
• Have the Right Skills and Resources. Hire skilled people who understand AI data center technology and where it’s headed. An AI-ready team will make it easier to cater to the growing demands of AI centers and prepare your infrastructure accordingly.

In addition to these suggestions, keep an eye on future trends. Generative AI is already gaining traction in several industries and will be used to create synthetic training data for AI models going forward. Edge AI (deployment of AI algorithms directly on edge devices such as smartphones) will also be big news. This can be integrated into data centers to build a high-performance, low-latency network.

Operate AI Data Centers With GRC Immersion Cooling

Innovation has led us to a point where electronic equipment can now be cooled down efficiently with a liquid. Because data centers produce substantial volumes of heat and drain electricity, AI can help bring down the costs.

GRC’s patented immersion cooling infrastructure has shown phenomenal results in addressing the heating issue—and reducing the power bills required for cooling the equipment.

Get in touch with us to find out how we are paving the way for low-cost data center operations of any size and shape.