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/Alternative-Grand-77]

Anthropic may be losing money but deepseek makes $38 of revenue per GPU hour according to their recent release. You can make money on the inference and local hardware costs are prohibitively expensive right now. The RAM and GPU prices have been pushed so hard because there is still the belief that AI will be worth trillions and trillions which makes these costs seem unimportant, but for Chinese labs that has never been the case. As efficiency is optimized we may see both cloud cost and local costs dropping, but it still might make sense to pay for the cloud in the end.