Dell and H2O.ai target the token-cost problem with vertical AI models
As artificial intelligence adoption accelerates inside enterprises, the economics of generative AI are forcing a fundamental rethink. Runaway token costs, data sovereignty demands and a growing gap between AI pilots and production ROI are pushing organizations to reconsider where their models run — and what kind of vertical AI models they actually need.
The answer increasingly points toward on-premises infrastructure and vertical AI models purpose-built for specific industries, rather than large general-purpose models consuming tokens at scale in the cloud, according to Satish Iyer (pictured, right), vice president and chief technology officer of technology innovation and ecosystems at Dell Technologies Inc.
“Our aspire[ation] in Dell [is] to bring AI to where the customer data is, as simple as that,” Iyer told theCUBE, SiliconANGLE Media’s livestreaming studio. “There is no AI without data, and most of enterprise data stays on-prem. It’s important for us to support an enterprise journey where we can make sure that enterprises are able to leverage AI to drive the right outcomes within that business without worrying about token cost.”
