Supermicro Says AI Factory Speed, Not GPU Count, Is the Metric
Super Micro says AI factory speed, not GPU count, is the key metric as data pipelines and storage shape deployment.
Lee made the argument in an interview with theCUBE Research’s John Furrier at the AI Data Pipeline Forum, broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. He said AI factory deployment has long been a race to secure GPUs, but compute is only half the problem. Data pipelines, storage costs and the clock on getting a cluster running now weigh as heavily as raw horsepower, according to Lee.
Customers that once focused only on standing up the biggest GPU clusters are now puzzling over how to feed them with data, Lee said. “In the past, we really, really focused on the computational resources, right?” he said. “But recently we figure out that people need to start looking from the data perspective.”
Supermicro’s customers often discover the data problem after the GPU build is done, according to Lee. This year, soaring solid-state drive and memory prices have made them hesitant to size storage clusters, yet money already sunk into compute leaves little choice. Supermicro now plans storage alongside compute with partners such as Vast Data Inc. “We still talk about the GPU workload, size the GPU cluster. We know this is very important. But now we partner with a strong partner like VAST,” Lee said.
Supermicro treats the AI factory as one big computer and pitches single accountability for it, an approach that extends to its AI factory management work with Broadcom Inc. That work spans servers, storage built with partners such as ScaleFlux and its Data Center Building Block Solutions, which the company says scale from 5 megawatts to 1 gigawatt.
Lee said deployments depend on fundamentals. “Fully validate, because every small things will just create the big issues for the whole big clusters,” he said. “So you have to really go into every single detail for the validation, down to the component level, down to a system level, and to the rack level and the cluster level.”
That discipline now sets the pace of AI factory deployment, according to Lee. Supermicro brought xAI’s liquid-cooled Colossus-1 cluster online in 120 days, with a tiger team coordinating supply chain, rack and field engineering work, he said. “Then we talk about a time to deploy, and more important, time to be operational,” Lee said. “This is really important because when you start to put the money and you have to pay Supermicro in certain way, you want to see your whole system or whole cluster online in very, very short period.”
The interview was part of SiliconANGLE’s and theCUBE’s coverage of the AI Data Pipeline Forum. ScaleFlux sponsored the segment, and neither ScaleFlux nor other sponsors had editorial control over content on theCUBE or SiliconANGLE, according to the disclosure.