Artificial intelligence is changing what storage and data management platforms look like.
In collaboration with Super Micro Computer Inc. and Solidigm, DataDirect Networks Inc. has introduced DDN Enterprise AI HyperPOD, built on Nvidia Corp.’s AI Data Platform. The goal is to simplify the storage, scaling and deployment of AI inference for enterprise workloads.
“Customers are preferring best of breed, but that can often result in complexity,” said Andrew Murphy (pictured, right), senior director of product management at DDN. “The question is how can we help to offload that complexity from our customers so they can focus on what’s most important, which is taking their data that they’ve been generating over the years … [and] add context so that it can be ready for their AI factories.”
Murphy, alongside Michael Ang (left), director of storage solutions at Supermicro, and Pompey Nagra (center), products and ecosystems lead at Solidigm, spoke with theCUBE’s Rob Strechay for the Supermicro Open Storage Summit interview series, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed the collaboration and new trends in enterprise AI. (* Disclosure below.)
One unified storage solution
While customers traditionally buy individual storage, networking and compute platforms and build their own solutions, that strategy often takes far too long, according to Ang. The better method, he argues, is to have one unified solution that enables organizations to start monetizing their data immediately.
“The number one challenge is [customers] still have the mindset of having to buy individual — compute, storage, networking — and put it together, but that takes a long time,” Ang said. “In order to help them be successful in this area, the customer should focus on how do they get the right solutions with the right platforms such that … they can start utilizing it and start doing the AI workloads.”
The HyperPOD collaboration can employ different types of flash memory to meet different AI tasks, Nagra added. Because solid-state drives reuse AI workloads without the need for recomputation, customers get more utilization out of their graphic processing units.
“We see the platforms change from being compute bound to storage bound, where LLMs need more data to more storage to enable the [key-value] cache data,” Nagra said. “The SSD storage becomes a great value as it’s capacity bound versus the [high-bandwidth memory] or primary memory, which requires greater amounts of recomputation for the same workload.”
DDN worked closely with the Nvidia AI Data Platform reference design to offer a turnkey solution that reduces complexity for customers and can be deployed on premises, giving organizations greater control over security, governance and data residency. The technology also supports multi-tenancy, which means that multiple teams can share compute, networking and storage but keep their environments separate.
“Most of the time the GPUs are sitting there idle, waiting for the data,” Murphy said. “That’s really what we’re focused on from a DDN perspective … how do we make sure that we provide a fast path to fully utilize GPUs so that way customers can get the most out of their investments, they can get the most out of their data to further their initiatives.”
Sovereign AI puts data control in focus
Companies and government organizations are increasingly seeking private or sovereign AI solutions, according to the panel. Nvidia’s secure reference design offers that privacy and security, while still giving customers the opportunity to be flexible with how they handle their data.
“If you take a look at organizations, they’ve always wanted to have control of their data, the IP, most importantly their costs,” Nagra said. “Being able to control all these elements enables greater flexibility … and at the end of the day, lets them live in their governance that they’ve orchestrated, whether it be on prem with the platforms provided by DDN and Supermicro, as well as in hybrid environments.”
In addition to offering environmental flexibility, HyperPOD can also scale up as necessary, which minimizes cost exposure. Rather than having to commit to large numbers of expensive GPUs, customers can try out the solution with one rack and then add more when needed.
“We are able to design this complete AI data platform solution that allows the customer to expand from small, medium, large,” Ang said. “Meaning they can continue to run the business when they see there’s a need for additional GPUs and storage utilizations. They can just pop in another rack next to it, connect the network cables, power it on, then continue online and serve customers.”
Stay tuned for the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of the Supermicro Open Storage Summit interview series.
(* Disclosure: TheCUBE is a paid media partner for the Supermicro Open Storage Summit interview series. Neither Supermicro, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)
