What are AI Data Centers?

It’s undeniable that AI usage has grown significantly in the past decade, with usage in the past few years skyrocketing as a result of new solutions. Generative AI is flooding the market, AI usage across different industries like education and healthcare are becoming mainstays, and organizations both big and small are looking to get a slice of the AI pie by creating their own solutions. With all the hubbub surrounding AI and new solutions popping up almost every day, you’re probably wondering “what the heck is powering all this??”.

If the typical solution runs within the racks of a data center, then AI solutions operate within the confines of an AI data center. But do AI solutions have to be hosted within dedicated data centers? Well, yes AND no.

At the base of AI solutions are training and operations. While traditional data centers can take care of AI training and operations, AI requires specialized hardware and professionals to make training and operations more efficient and scalable in the long run. Without retrofitting, traditional data centers would struggle to accommodate growing AI solutions. AI data centers are designed to host AI solutions, offering performance hardware like graphics processing units (GPUs) and accelerators in lieu of CPUs, strong cooling systems, and even different data storage options to account for large amounts of unstructured data. 

AI’s growth and adoption shows no signs of slowing down, ushering in a new age of data centers and innovations, but also problems and competitiveness.

How Data Centers for AI Differ from Traditional Data Centers

As mentioned above, data centers for AI host more specialized hardware and software, focusing on scalability and storage. The biggest differentiator is the ability to run GPUs in a scalable fashion.

GPUs are central to AI, being responsible for powering the training and deployment of AI models. In comparison to using CPUs for AI processes, GPUs offer an efficient solution to AI development and deployment, also accelerating AI training and inference. Like all scalable architecture, the more complex an AI system is and the more datasets being introduced, the more GPU is required to run efficiently. Instead of accommodating all types of solutions like a traditional data center, AI data centers only account for AI, thus maximizing its ability to host, handle, and scale GPUs.

With new hardware comes new problems, and the problems of traditional data centers and AI data centers are entirely different. Where a traditional data center might struggle with scalability, costs, and so on, AI data centers are primarily focused on one core problem: power.

AI systems and models are complex, requiring tons of time and data to become optimized and precise. Even then, AI computations are more geared towards speed and distribution, with preciousness becoming a second priority. And with this drive towards speed and distribution comes high power consumption. What used to run 20-30 servers in a traditional data center now only powers 2 AI servers- with one of the biggest problems for AI data centers being the balance between maximizing power usage and available real estate. In addition, unlike traditional data centers, AI data centers use specialized cooling systems to disburse heat generated, though the environmental impact is certainly higher.

AI Impact on Data Centers

Despite the immense requirements of AI data centers, data centers around the world are retrofitting to be able to handle AI workloads and meet growing demand from data scientists. In fact, experts believe that data center capacity is going to triple by 2030, with hyperscalers being positioned to meet this capacity the best. Even if a data center doesn’t host AI solutions, retrofitting in this current landscape can generate valuable data center infrastructure and real estate.

Retrofitting doesn’t just mean preparing for AI handling, but also creating an ecosystem that can support data center development. Handling solutions is just one piece of the puzzle, with cornerstones like real estate and utility providers playing pivotal roles in the competitive data center landscape. The creation of this ecosystem has become a top priority for many data centers, but with the creation of this ecosystem comes new problems. The first problem that all data centers face is trying to generate power cost-effectively. The second problem is securing deals where resources, such as land and power, are finite.

In a landscape dominated by hyperscalers and big names, we’ve observed that local data centers are thriving in emerging markets due to their mastery of a supportive ecosystem. Where hyperscalers are often inapproachable on an interpersonal level, local data centers are tied with local energy providers, receiving more competitive pricing for energy sources than hyperscalers. This puts local data centers in the unique position to become the epicenters of AI solutions.

With the increased emphasis on artificial intelligence begs the question: “what about the other solutions in data centers?”. The unfortunate truth is that data centers may prioritize GPU retrofitting to maximize revenue, sidelining other solutions and operations. For traditional data centers, prices may rise to stay competitive with AI data centers. Although this situation seems dire, it isn’t the end of the world, as supply for all solutions will still exist.

In my opinion, AI’s impact on data centers is more than just requiring new hardware and more power, but changing how we interact with our data center ecosystems as a whole. Where hyperscalers might have dominated in a traditional format, local data centers are primed to take over because of their close ties to energy providers, something that AI desperately needs. This extends the understanding of data centers itself. No longer are data centers a means to deploy solutions, but are part of a breathing ecosystem- one where the layer above is strong infrastructure and the second layer above is a means to access solutions. At Lyrid, we’re pioneering that third layer, offering streamlined access to groundbreaking technology!

AI Data Centers Environmental Impact

With the amount of resources and energy needed, and with new liquid cooling developments, AI and the data centers running it certainly have a profound impact on our environment. 

AI generates more heat and yields higher power requirements than a traditional data center ever will. The cooling required to keep operational temperature down needs tons of water as well. AI’s total contributions to global warming are undeniable, and, at this moment in time, eerily unquantifiable. We don’t know what the end result of AI’s environmental impact will be, nor do we know the economic value and net productivity generated because of AI.

What we do know, however, is that we must mitigate the impact that AI has on the environment for future generations. The solutions we’re building at Lyrid are aimed at lowering the barrier of entry for anyone to be able to code or to build a business, especially using AI. We would love for generations down the line to innovate as freely as we do today, so preserving the sanctity of our environment today is of the utmost importance for the innovators of tomorrow.

Mobilizing Data Centers and AI with Lyrid

With the scope of AI solutions only growing and AI models only getting more and more advanced, it’s only right that the infrastructure hosting this technology evolves as well. Dedicated AI data centers host hardware and techniques unique to AI catering, including mass GPU hosting and water cooling, respectively. This immense shift in the data center landscape paints an unsure future for the industry as a whole, though we’re predicting local data centers and their relationships with local energy providers win this competitive space. With uncertain implications towards the environment, the future of AI is one that we’re weary about, but approach with curiosity.

Our mission at Lyrid is, and always will be, to lower the barrier to entry of cutting edge tech around the world. This includes artificial intelligence. We’re working on extending our platform to deliver GPU containers on top of the solutions we already host. For data centers, we’re also working on hardware and GPU provisioning capabilities, making reaching a new audience and selling easier than ever before!

Want to learn more? Let’s chat sometime! I’m always open to talking about all things tech and more!