The "upper end" of local models as of today would be something like MiMo 2.6 Pro or Kimi K3, either of which is significantly smarter than Qwen Flash-Next. Roughly comparable in smarts to the old GPT Terra series, not quite as capable as Sonnet. But yes, running these models locally at realtime-usable speeds and without heavy quantization would be even harder. They can be quite tolerable for plain Q&A chat and great for running multiple unattended workloads on a 24/7 basis, but that may be a bit of a niche.
Yeah, I should have worded that better. I'd say it's at the upper level of what can be ran on plain consumer hw (like a laptop, mac, etc). For ~k3 you'd realistically need dedicated hw anyways. Or a cluster of consumer hw, and that comes with its own problems and drawbacks (esp in pp which matters a lot for agentic stuff).
Kimi K3 has been demonstrated on a top-of-the-line laptop Mac running at several seconds per token (it has to fetch essentially all experts from SSD, since IIRC the dense part of the model takes around ~105GB on its own!). That's impressive enough if you only care about pure Q&A/chat or pure unattended tasks, but it's not what one would expect for agentic work. You'd need quite a bit of additional RAM to have good caching for the most common experts, and even then that would only help so much. MiMo Pro would be a roughly similar story.
> Or a cluster of consumer hw, and that comes with its own problems and drawbacks (esp in pp which matters a lot for agentic stuff).
Prefill actually ought to work well on a cluster AIUI, since it can leverage both pipeline parallelism and batching quite effectively.