this post was submitted on 30 Jul 2024
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I run LibreTranslate on matapacos.dog for inline post translation (and at home to avoid Google knowing every scrap of foreign language text I read) and it is a similar story. It runs locally (doesn't even require a GPU) and makes no remote requests. Language models developed for specific purposes can accomplish great things for accessibility with much lower performance requirements than the gimmicky shit Silicon Valley tries to pawn off as "artificial general intelligence."
Exactly! PCs today are powerful enough to run them in decent time without acceleration too, it would just be more efficient to have it, ultimately saving time and energy. I would be interested in seeing how much processing power is wasted to calculate what are effectively edge cases in a models real work load. What percentage of GPT-4 queries could not be answered accurately by GPT-3 or a local LLaMA model? I'm willing to bet it's less than 10%. Terawatt-hours and hundreds of gallons of water to run a model that, for 90% of users, could be ran locally.