Most semiconductor companies try to make chips smaller. Andrew Feldman went the other direction.
The Cerebras Systems CEO built what is currently the largest single computer chip designed for AI workloads. Called the Wafer Scale Engine, it occupies an entire 300 mm silicon wafer, the same circular platter that traditional chipmakers slice into hundreds of individual processors. Cerebras just skipped the slicing part.
The result is a processor roughly 50 times larger than the biggest chips from Nvidia and Intel. And Feldman’s bet that bigger means faster for AI has attracted serious money: a $1.1B Series G funding round, a $5.55B IPO, and a first-day market cap of approximately $95B.
Why one giant chip instead of many small ones
Here’s the thing about training large language models. The standard approach involves stringing together thousands of GPUs and getting them to communicate with each other constantly. Think of it like hiring 10,000 workers who all need to be on the same conference call simultaneously. The work itself is fast. The coordination is the bottleneck.
