Artificial intelligence promises to reshape industries, enhance our daily lives, and enable innovations we can barely imagine today. As AI capabilities advance at a blistering pace, hyperscale computing has emerged as the indispensable engine room powering this transformation.

Just consider what AI can already achieve. Systems can generate human-like text and artwork on demand, translate languages in real-time, identify objects in live video, improving situational awareness and public safety across large cities. Not to mention the complete paradigm shift happening across households and businesses as AI tools like ChatGPT and others like it completely transform how people handle their daily business.

How does this AI thing work anyway?

The brains behind the magic of AI are the underpinning AI models. These operate by mimicking the networked structure of the human brain on a vast scale. They contain billions of adjustable parameters spread across hundreds of layers. The models optimize these weighted parameters by analyzing enormous training datasets over multiple iterations.

Even with thousands of CPUs and high-speed AI accelerator chips working in parallel, training a single advanced model can take weeks of non-stop data crunching. Once trained, these powerful models need dedicated compute clusters to handle inference at scales similar to the demands we see on search engines today; millions of requests every minute will be performed for various tasks ranging from mundane tasks to mind-blowing. AI is delivering on it all, from automating the monotony to leveling up our collective knowledge in cutting-edge research areas.

This rapid progress depends on the development and serving of AI models capable of processing truly massive datasets, and as you’d expect, this is where massive computational resources are required. So, where does this computing revolution take place? In the data center giants of hyperscale computing.

Hyperscalers, the horsepower behind AI. 

Hyperscalers house the vast data stores and computational power needed to develop advanced AI and serve the resulting innovations to the hungry businesses and individuals who want to consume them at the touch of a button.

Hyperscalers offer immense power, cooling, and space for AI hardware. Compared to legacy hardware, these clusters consume up to 5X more power per rack and require advanced cooling techniques like liquid cooling. Fiber optic cabling must also be rearchitected, with port densities up to ten times that of current infrastructure. Dedicated low-latency networks ensure AI doesn’t disturb normal data center workloads.

While it’s possible to share network infrastructure, dedicated low-latency backend networks are preferred for AI. This ensures that high-speed data exchange doesn’t interfere with normal workloads and applications.

The rising tide of AI

The AI computing tsunami has arrived on the shores of hyperscale, with surging demands already present, fueled by the promise of exponentially greater demand in autonomous vehicles, smart cities, and future generations of AI assistants, to name a few areas. Operators are aggressively installing large, dedicated AI clusters in both new and existing data centers.

This blog provides a high-level overview of the integral role hyperscale computing plays in advancing AI. To fully appreciate the massive computational demands, specialized hardware, integration approaches, and potential impacts on data centers, download the in-depth e-book The AI Tsunami and Hyperscale.