This is what people forget when they see slow performance: at 1 t/s it's still roughly the equivalent of having another person work for you at no extra cost besides the initial purchase/sign-on-bonus.

Frontier models are amazing, but what will really be useful for us is having models and hardware so efficient that you can run useful LLMs locally. One of my favourite LLMs to this day is still my jail-broken gemma4 12b because it's small enough to run on my computer, but also 100% local and free as in liberty.

I would agree, but I want to add that I have real issues with combination of opencode plus slow inference (4-5tok/s).

I get weird interruptions. I can only guess its related to some kind of timeouts in the harness or something.

Its not a problem of the model of course, but it seems impractical atm.

I wonder if anyone else had this kind of thing happening.

> At 1t/s it's still faster than humans for a lot of tasks

Which tasks? I think you're underestimating how token hungry current proposed workflows are.