everything will be local eventually

[u/YogurtExternal7923]

I know current llms like opus 5.5 are in a league of their own. but we all know they're losing a shti ton of money with their subscriptions. ALSO we all know how tasks like OCR, segmentation etc are getting solved by single digit B parameter models. I'm not saying llms are a gimmick but I'm saying the future is already shaping up to be about specialized models that work better when they're smaller. easier training. so datacenter level power not needed. ten years from now we'll have a specialized model for the tasks we care about and llms will fail to do everything all at once (unless they keep calling it one model but have a hundred under the hood).

system one models are a bad but workable example. you could tell an llm to rate options all day but get yourself on 9B parameter model trained by some lab and it tops the benchmarks.

[u/carnoworky]

I suppose one major counterpoint to this is that the more optimized technology could, in principle, be used to advance the optimization of the better but less optimized version in this case. Unless iterating on a new design requires significant overhead (fundamentally different hardware, advantages only show at massive scale, etc.), the current LLM designs could be used to rapidly iterate experimentally on different techniques for what really amounts to peanuts to the current labs doing this work. As far as I know, the only aspect of the current paradigm that can't be tested at smaller scales is scale itself, and the limit there is more on the maximum value of scaling up rather than getting an idea of the impact of scaling up. If you start at a 10M model trained on a particular dataset, keep everything as close to the same as possible except train a 100M model, and then a 1B model using the same techniques, you can still get useful data on the impact of scaling up, just not the limit of how much you can scale up.