AI-driven data infrastructure a spotlight for Supermicro

High-performance computing, long confined to academic labs, has today turn out to be the backbone of AI-driven business transformations. But regardless of the use case, a large amount of processing is required to handle the info and support the heavy demands of AI-driven data infrastructure and HPC tasks.

To handle these challenges, firms such s Super Micro Computer Inc. and its partners have sought to deliver AI-driven data infrastructure and storage solutions designed to fulfill the high-performance demands of recent computing. It’s an exciting time to be involved within the industry, based on CJ Newburn (pictured, back row, right), distinguished engineer at Nvidia Corp.

“One in all the things that characterizes a few of what’s occurring now’s the speed at which usage models are changing and evolving,” Newburn said. “Every couple of years, now we have radical changes. That’s resulting in a necessity for brand new infrastructure. With the intention to give you the option to make best use of that, latest technologies are needed and are quickly emerging out of that.”

Newburn; Randy Kreiser (front row, right), senior storage architect at Supermicro; Balaji Venkateshwaran (front row, left), vp of product management at DataDirect Networks Inc.; and Bill Panos (back row, left), senior product marketing engineering manager at Solidigm, spoke with theCUBE Research’s Rob Strechay on the Supermicro Open Storage Summit event series, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed the evolving role of AI-driven data infrastructure in HPC workloads and the critical importance of advanced storage solutions to support these demands. (* Disclosure below.)

AI-driven data infrastructure looks to support large-scale AI models

Recent usage models are defined by their large scale. In addition they involve increasingly fine-grained access patterns, based on Newburn.

“A number of the different applications that we see on this space are LLMs, large language models, GNNs, graph neural networks, and RAG, for retrieval augmented generation,” he said. “Those latest applications really want latest infrastructure. They operate at a giant scale. You possibly can see the Supermicro NVLink rack-level integration, where that whole rack acts as one GPU, all NVLink connected.”

As mentioned, the brand new world of AI and machine learning involves a considerable amount of data, in addition to quite a lot of data, block, file and object. DataDirect Networks has been looking for to make data management easy and seamless for patrons, based on Venkateshwaran.

“When a customer is buying a GPU infrastructure, computer infrastructure for his or her AI and ML application, what DDN desires to do is be the one-stop shop by way of storage and data management,” he said. “There are quite a lot of things we’ve done through the years and proceed to do in partnership with everyone that’s here.”

Holistic AI infrastructure and tailored storage solutions

For Solidigm, the main target is on the physical level with the corporate’s SSDs. When considered holistically, there’s areas inside the stage for certain media products that an organization will engage with, based on Panos.

“Either with, say, DDN or with Nvidia or Supermicro, as you arrange that infrastructure, it isn’t a one-size-fits-all, so you will have to give it some thought holistically and search for each of the stages and what the vital requirements could be,” Panos said. “You possibly can, definitely, as you’re serious about your infrastructure and rolling that out, engage with Supermicro, Solidigm or DDN or Nvidia to enable you to make those right decisions.”

At the tip of the day, it’s about providing solutions for firms. It involves various latest technologies from Supermicro and its partners, based on Kreiser.

“We’re talking in regards to the ability to bring trillions of all of those threads …. in the entire modeling and so forth, especially with large language models and so forth, which I see just exploding in every single place,” he said.

That’s going to guide to quite a lot of possibilities, in Kreiser’s view. The examples are quite a few: “Whether it’s seismic processing to get to work out where the following major drill place goes to be for oil, or whether that be medical reasons to search out a discovery for cancer, or whatever it might be, the power to take larger models and give you the option to only compute all the way down to the nth degree to deliver effectively these results.”

Collaboration can also be driving improvements in efficiency and performance for the AI data life cycles. That goes right to the center of what the partnership is all about, based on Venkateshwaran.

“As use cases and data types explode, the goal is to make it simpler and abstract away the complexity from the shopper, so the shopper can give attention to running their applications and doing what they wish to do best,” he said. “What we’re doing here is working behind the scenes to abstract away all that complexity for the purchasers.”

Stay tuned for the whole video interview, a part of SiliconANGLE’s and theCUBE Research’s coverage of the Supermicro Open Storage Summit event series.

(* Disclosure: TheCUBE is a paid media partner for the Supermicro Open Storage Summit event series. Neither Super Micro Computer Inc., the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE