GPUs in the Wine Cellar: Why Techies Are Hoarding AI Compute in Their Homes

AI Summary

Some AI developers are building home GPU systems rather than renting computing capacity from cloud providers, citing lower costs and greater ownership. Former Microsoft executive Gary Flake assembled a seven-GPU machine for under $6,000, while spending an additional $10,000–$20,000 on electrical and cooling upgrades.

The basement of Gary Flake’s contemporary home in a quiet neighborhood in Bellevue, Wash., is furnished with everything you’d expect in a multipurpose man cave. There’s a cozy-looking couch situated between a large screen and a digital projector, a Peloton treadmill and a rowing machine. The real showpiece of the space, however, is a custom computer that resembles an oversized erector set, one with seven large Nvidia graphics processing units dangling from its frame. Flake, a former Microsoft executive and startup founder, built the computer to train custom AI models on terabytes of data from electroencephalograms, the tests that measure electrical activity in the brain—part of a startup project he has since moved on from. To train his AI models, Flake could have paid a cloud service to train them on the industrial-strength GPUs inside their data centers, but he said it was far cheaper to assemble his own machine out of discounted parts (total cost: under $6,000) than to fork over fees to Amazon Web Services or Microsoft every month. That’s true even when including the $10,000 to $20,000 he spent for electrical upgrades and a new HVAC system he bought to keep his basement cool while his AI computer throws off a lot of heat. “I want to own,” said Flake, who plans to begin using the computer again for a new AI project in the coming months. “I don’t want to rent.”