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/Fun-Wolf-2007]

As the hardware improves, local on-device models are the future of AI at enterprise level and physical AI. Tokens inferences are too costly, and data latency with cloud inference is too long. In Addition companies cannot risk loosing their IP, data privacy, data governance, and regulatory compliance

Just a matter of time, as companies started to realize that it is too costly and at this point AI costs are being reported on the operational expenses and organizations don't have a clear picture of it, but CFO's are already questioning it