It’s no secret that high-performance computing like artificial intelligence (AI) and machine learning (ML) are changing the demands and expectations of both society and the digital infrastructure industry at a rapid pace.
In the last issue of InterGlobix Magazine, Flexential CINO Jason Carolan discussed how traditional data centers need to evolve to keep up with the evolving landscape of these technological advancements and computing requirements. This article takes a closer look at the interconnection side of things. Specifically, I explore why scalable data interconnection plays such an important role in meeting the ever-increasing demands of AI—and how we, as the leaders, innovators, and playmakers of digital infrastructure, can adapt our products, services, and approach to support those shifting needs.
Larger Models Means More Processing Power
Behind every AI application—from image creation, music streaming DJs, and essay-drafting chatbots to virtual assistants, logistics automation, and service provision—lies data. A whole lot of it.
As the large language models (LLMs) needed to train, run, and continually develop AI successfully become bigger and bigger, the amount of hardware needed to support them becomes increasingly complex. A single machine can’t provide enough power, and as more and more computing nodes get added to the equation, effortless and efficient interconnection between these compute centers becomes more and more imperative to keep the system running smoothly. The same goes for connectivity-centric concerns like bandwidth, latency, and redundancy: If interconnection services don’t anticipate potential problems and evolve in stride, the infrastructure can cause processing bottlenecks at best and service failures or errors at worst.
