Why AI Workloads Need More Than Just GPUs

While many people are aware that companies use massive datasets to train AI models, they don’t always consider the infrastructure implications of this fact. It’s true that enterprises need GPUs to process all that training data, but there’s much more to AI infrastructure than just GPUs. For one thing, legacy data centers typically lack the power, cooling and connectivity capabilities needed to make the most of GPUs. To overcome these shortcomings, enterprises can deploy GPUs inside high-performance data centers instead.

There’s also much more to AI than just training workloads in a few core locations. Today’s AI workloads are inherently distributed, and enterprises need to be able to move AI datasets between different sources and processing locations. In the video clip below, Mary Johnston Turner, Research VP at IDC, describes how enterprises are investing in connectivity solutions that help them move data quickly and securely, while also meeting their data sovereignty requirements.

Today’s enterprises capture their AI data from a wide variety of different sources. They then process that data in many different places—both in different geographic locations and in different environments such as clouds and private data centers. To top it all off, they work with many different ecosystem partners to get the AI data, models and tools they need. All this means that the networking requirements of enterprise AI are extremely complex, and enterprises need to act now to ensure they’re ready to meet those requirements.

According to Jon Lin, Chief Business Officer at Equinix, enterprises have begun to implement distributed AI infrastructure to ensure the seamless flow of AI data wherever it needs to go:

At Equinix, we’re building the backbone enterprises need for distributed AI infrastructure that’s secure, compliant and globally consistent. Read the solution brief to learn more about Equinix Distributed AI™.