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For all that is good in this world make it something that makes it require less memory to do more.

Archive: https://archive.today/Ridok

From the post:

>Miami-based AI startup Subquadratic came out of stealth mode last month with a huge claim. It announced that it had solved a mathematical bottleneck that had been holding back large language models for almost a decade. The details were thin, and many people were unconvinced. But Subquadratic has started to bring the receipts, sharing the results of an independent evaluation of its new tech. The results suggest that the company’s claims might be worth paying attention to.

For all that is good in this world make it something that makes it require less memory to do more. Archive: https://archive.today/Ridok From the post: >>Miami-based AI startup Subquadratic came out of stealth mode last month with a huge claim. It announced that it had solved a mathematical bottleneck that had been holding back large language models for almost a decade. The details were thin, and many people were unconvinced. But Subquadratic has started to bring the receipts, sharing the results of an independent evaluation of its new tech. The results suggest that the company’s claims might be worth paying attention to.
[–] 1 pt

Subquadratic's claims about cost are harder to verify because SubQ is not yet widely available. According to Dangel, it costs $2,600 to run Anthropic's LLM Opus 4.6 through RULER 128, a test developed by Nvidia to assess a model's ability to retrieve information from large data sets. And SubQ? "It cost us eight dollars," he says.

If this is legitimate their technique will eventually be replicated by open source LLMs and the massive data center requirement will vanish. Programmers will run private LLMs on their laptops that match what current, massive data center LLMs do.

[–] 1 pt

It's a big "if" but it would also probably blow up the hardware market too. No more ram modules that should cost $60 costing $350.