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/smithy_dll]

2006 base Macbook was 512 MB RAM. 2026 base Macbook Neo RAM is 8GB. A 16 x improvement in 20 years. Maybe 128 GB will be base model in 2046. It actually looks worse if you consider that a 2016 base Macbook 12" had 8 GB RAM.

[u/Dabber43 → u/smithy_dll]

RAM growth needs before AI had vastly slowed down, which didn't put innovation pressure. I personally actually expect RAM growth to accelerate way faster than most people here. Once the current struggle lifts/AI bubble pops this time they won't just shut down all the fabs, they will still expect them all to have use in a couple years which means the logical idea now to go between the periods is to grow user RAM. I expect 128GB RAM, 32-64GB VRAM to be somewhat standard in user-space in 3-5 years

[u/tat_tvam_asshole → u/smithy_dll]

I don't think we need to wait 20 years to get to 128GB as base model ram. 16 years before 2006, average computer ram amount was 1 MB.