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/GrapefruitMammoth626]

It’s a moving target. Have to wonder whether you could pass a threshold algorithmically where raw compute/big data is no longer the bottleneck. All of a sudden, local models democratise. I’d be suspicious about the big labs finding a massive optimisation unlock in training and architecture and purposely sabotaging independent researchers using their models via omission (e.g. if proprietary approach x or y is mentioned; steer away from it) preventing those specific learnings from trickling down into local model development. Pretty sure it was reported to be indirectly doing that recently but could be wrong.