A Miami startup says it has cracked a maths problem that has made AI models slow and power-hungry for almost a decade. The claim was bold enough to draw comparisons with Theranos. Now, though, the company has independent test results that back much of it up.
The startup is called Subquadratic. It came out of stealth in May with $29mn in seed funding and a new language model named SubQ. According to the company, SubQ is faster, cheaper, and far less energy-hungry than today’s leading models. It can also read up to 12 times as much text at once.
The decade-old bottleneck
To see why that matters, it helps to know how most large language models work. At their core sits a “transformer”, introduced by Google researchers in 2017. The transformer runs a process called dense attention.
Dense attention is thorough, but it is expensive. It compares every word in a text with every other word. So when you double the length of the text, the work roughly quadruples. That “quadratic” scaling is the main reason LLMs guzzle so much compute and power.
