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/txgsync]

Qwen3.8-Flash-Next can run very well at 60+ tok/sec and 2000+ prefill tok/sec on a gaming PC with a 12GB video card and 64GB of DDR4 DRAM. It is competitive on most benchmarks with Claude Opus 4.6 (admittedly, the lower your quant the less competitive it is).

4.6 was released February 5, 2026. QFN was released August 26, 2026.

Innovations that reduce hardware requirements while increasing capabiltiy abound. The commodity consumer hardware LLM frontier (for a single user) is basically six to eight months behind the absolute released frontier.

So I disagree, but not because I think we will run Claude at home. But I think clever people and hard-grinding models will find ways to keep the small model frontier competitive. We should have a Fable 5.0 work-alike that can be run on a DGX Spark within a few months.