Open Specs Could Cut AI Data Center Hardware Integration From a Year to Months

As AI factories scale, hardware interoperability is becoming as important as raw performance, according to SiliconANGLE, which broadcast the interview on theCUBE. Accelerators, memory and storage now come from many suppliers, and every mismatch between them slows deployment across the data center. Tavallaei said the pressure is mounting because AI infrastructure bottlenecks are shifting attention from individual chips to entire systems.

Open communities such as the Open Compute Project integrate separately developed technologies, from Peripheral Component Interconnect Express to Compute Express Link, into complete systems that many suppliers can build. "There are different companies skilled in different aspects of the problem. And you would like to, as an integrator, as an open industry, would like to take advantage of all of that," Tavallaei said. "Therefore, interoperability matters a lot."

His work with OCP traces back to his time at Microsoft's Azure cloud, where he co-led OCP's server project. Large cloud operators kept asking suppliers to build the same hardware, but each supplier read the specification differently, so server fleets drifted apart. "Software was what Microsoft was doing well, but we wanted hardware to be common so a number of our suppliers could build the same thing," he said.

A finished system handed to OCP as a monolithic contribution may never catch on, while one shaped in the open draws input from many companies, according to Tavallaei. That broader buy-in brings volume and more engineers working on the same problems. "If you were to work that with the community, different people will interject their own ideas, and at the end, story will be something that a number of different companies would like, and therefore adoption goes up," he said. "When adoption goes up, volume goes up."

Memory is the next proving ground. Samsung recently demonstrated CXL memory pooling with Advanced Micro Devices at the OCP summit, and Tavallaei is bringing the CXL specification into OCP's Composable Memory Systems subproject. The bet is that hardware interoperability will sharply cut the time hyperscalers need to adopt new designs.

"When a specific thing is built for, for example, one hyperscaler, a different hyperscaler trying to reintegrate that takes time, about nine months, 12 months of reintegrating," Tavallaei said. "Whereas if things are crisply defined in a specification, when something is good and is recognized, integrating that into a new hyperscale data center might take three months or six months instead of a year or year and a half."

The interview was part of SiliconANGLE's and theCUBE's coverage of the AI Data Pipeline Forum. ScaleFlux sponsored the segment; neither ScaleFlux nor other sponsors had editorial control over content on theCUBE or SiliconANGLE, according to the disclosure accompanying the broadcast.

Editor's Summary Open specifications are being positioned as a way to shorten the integration of new accelerators, memory and storage into hyperscale AI data centers from roughly a year to three to six months. Samsung Semiconductor's Siamak Tavallaei cites OCP work dating to Microsoft Azure and a CXL memory pooling demonstration with AMD as evidence that shared definitions raise adoption and volume. The claim rests on the argument that open development draws more suppliers and engineers than hardware handed over as a single contribution.