I do deepseek at 5tk/sec at home and I'm happy with it. I don't need to do agent stuff to gain from it, I was saving to eventually build out enough to run it at 10tk/sec, but with kimi k2, plan has changed and the savings continue with a goal to run it at 5 tk/sec at home.
Also works perfectly fine in fire-and-forget, non-interactive agentic workflows. My dream scenario is that I create a bunch of kanban tickets and assign them to one or more AI personas[1], and wake up to some Pull Requests the next morning. I'd me more concerned about tickets-per-day, and not tk/s as I have no interest in watching the inner-workings of the model.
1. Some more creative than others, with slightly different injected prompts or perhaps even different models entirely.
> I create a bunch of kanban tickets and assign them to one or more AI personas[1],
Yeah that. Why can't we just `find ./tasks/ | grep \.md$ | xargs llm`. Can't we just write up a government proposal style document, have LLM recursively down into sub-sub-projects and back up until the original proposal document can be translated into a completion report. Constantly correcting a humongous LLM with infinite context length that can keep everything in its head doesn't feel like the right approach.
In my experience, this sort of thing nearly works... But never quite works well enough and errors and misunderstandings build at every stage and the output is garbage.
I had hoped that this recursive breakdown approach could remove the need for bigger and bigger monolithic LLM for ever bigger tasks, by allowing every tasks to be at same granularity, but... I guess I should just try building one myself.
