Vertical AI pushes infrastructure beyond one-size-fits-all
While infrastructure requirements may be commonly shared in enterprise IT, the path for AI deployment can vary significantly depending on the organization. Healthcare, financial services, manufacturing, telecommunications and public sector organizations bring unique data, governance and operational challenges to the table when it comes to AI implementation. This means that the future of AI is increasingly vertical, and domain expertise, industry-specific workloads and proprietary data have emerged as competitive advantages. This future, however, can be a complex one as enterprises seek to tailor their technology stacks to generate meaningful results from AI investment.
“For the enterprise customers to build the AI factory as an AI purpose-built infrastructure, there has been complexity in that,” explained Vince Chen (pictured, bottom right), senior director of solutions architecture at Super Micro Computer Inc. “The ingredients — compute, storage, network and even up to the vertical integration of the software stack — the complexity has been there. So, what we are doing here is working with our partners to build vertically integrated and then pre-validated T-shirt size options for our customers to choose from.”
Chen spoke with theCUBE Research’s Rob Strechay for the Supermicro Open Storage Summit interview series, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. He was joined by Anders Graham (top, right), senior director of SSD marketing and business development at Kioxia Holdings Corp.; Brad Warbiany (top, left), director of technical marketing at Western Digital Corp.; and Moiz Kohari (bottom, left), vice president of enterprise AI and data intelligence at DataDirect Networks Inc.
They discussed how vertical AI solutions are emerging, where common infrastructure patterns exist, and how organizations can accelerate deployment while addressing industry-specific requirements. ( Disclosure below.)*
Vertical needs for AI deployment
The vertical solutions that are emerging reflect a need for the tech stack to perform responsively as business requirements evolve. At the private data storage and intelligence company DataDirect Networks, compute processes for its financial customers must facilitate the rapid movement of data to keep capital reserves flowing.
“If you’re a company like BlackRock or State Street where you’re looking at a trillion dollars on your books and you have 6% to 12% capital lockup, that’s a big deal,” Kohari told theCUBE. “You’re carrying that risk on your books. We can do those calculations because we can move that data back and forth quickly. When you do that, you’re unlocking 6% of that capital, so that capital can be leveraged to do anything else. You can reinvest it; you can hedge it. This same case applies in life sciences as well.”
To meet these kinds of vertical needs, Supermicro’s partners have developed technical solutions that help drive AI data processing workflows. At the computer memory manufacturer Kioxia, this means offering solid-state drives, such as its BiCS portfolio, that can maximize storage density in data centers.
“If we look at the different stages, on the data ingestion side, this is somewhere we see tiered architectures are fairly common based on the different cost points and use cases,” Graham said. “We have our BiCS8 technology now in our CM9 and CD9P drives, which are replacing the CM7 and CD8P drives in the Supermicro portfolio here as we move through this year. Those just have massive power efficiency gains, performance gains.”
Moving from experimentation to production
One key element in the growth of vertical AI solutions has been the development of domain-specific open weight models. Nvidia Corp. released a major offering in this area for the automotive industry earlier this year, and a number of open-weight models have become available in the healthcare sector as well.
The expert panel discussed how vertical industries must decide how to move from experimentation with open weights into production and what infrastructure will be needed to facilitate this transition.
“I’d say the biggest difference between experimentation and production is scale,” Warbiany said. “Things change drastically when you go from terabytes to petabytes and exabytes, and there’s a tremendous amount of complexity that has to be built into a system architecture. Having solutions like DDN and Supermicro that bring everything together helps customers that are making that scale as they go from small amounts of data to what they’re going to need day to day.”
Another factor for enterprises involves whether to operationalize AI in the cloud or a private data center environment. For many verticals, data management will be a key element in the path chosen, according to Chen.
“We know that the concept is for enterprise customers to start in the cloud; it’s easy, and it’s being encouraged,” Chen told theCUBE. “But at the same time … there’s the confidentiality and then the control of the data, control of the know-how, intelligence, that’s a critical one.”
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.)*
Photo: SiliconANGLE
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