Episode Details

In this video interview, HyperFrame Analyst Don Gentile explains how data storage is shifting from a passive to an active participant in AI, raising a defining question: bring compute to the data, or data to the compute? He says months of supply chain pressure is pushing enterprises to squeeze more from existing infrastructure and lean on private cloud and neo-clouds.

Get tech leader insights to move faster and smarter.

Get more stories by subscribing to The Forecast.

Video transcript:

Don Gentile: With the advent with GPUs now, and we need to constantly feed those GPUs, the concept of performance and the AI data pipeline, it really a point of contention is in the storage. The bottleneck happens there. And so you have to start to think about how is that storage media, whatever that McFlash, for example, going to feed those GPUs to make sure that they’re not starved, that they’re constantly being fed the pipeline data. So that’s a big shift.

Ken Kaplan: IT teams, are they having to use a variety of different types of storage technologies today or can they get it all done simply with one kind of storage?

Don Gentile: That is a question for every organization to deal with, right? In some cases, an organization might want to reuse their existing storage for cost performance reasons. They might have the ability to bring on new type of storage platforms. And so it’s a little bit of a purpose built exercise there. Certainly you have three tiered environments where you’ve got your hot, your medium, your cold storage for cost and performance reasons as well. There can be offsite archives, data archives, which is the least expensive and also air gapped for data protection reasons. So there’s a variety of different storage platforms that exist out there. The evolution then has been about thinking about how are we going to feed those AI pipelines and how does storage become an active participant in the AI process?

Ken Kaplan: Storage seems like it’s a very dynamic environment. There’s just a lot of evolution around that. Why did that happen and why do people want new types of storage capabilities?

Don Gentile: Maybe that started about 10 years ago. I could probably put a pin on it and say, if you start to think about where the workloads are going and you start to think about the advent of AI, which ChatGPT had not happened yet, but that moment was going to happen. So a number of companies started thinking about how do we need to design for those future workloads? And so you can rethink storage as more of a substrate that is managing and coordinating and governing across within a data center from cloud to on – prem and of course across geographies as well. And so things like global namespaces emerged where you have to start keeping track of where all that data sits. And then the ultimate question I think for companies is going to be, do you bring the compute to the data or do you bring the data to the compute?

And so that also will influence your storage decisions.

Ken Kaplan: How is storage evolving when we have more activity at the edge?

Don Gentile: Right. Well, if it’s a real time inferencing kind of experience that you have to have, you don’t have the time for that round trip. And so vehicles are part of that autonomous vehicles. And so if the decision has to happen in the moment, there’s no round trip to the cloud for that inferencing to happen. So you have data collection, you have data analysis, and then you have the action from that happening all at the edge. And in some cases you can aggregate, you can collect data from the edge and you can bring it to a central location. It really is going to depend on the application.

Ken Kaplan: You have a perspective