I dunno, K3 thinks a lot before it actually replies, and you might be in the ~1 tok/speed region or even "seconds / tokens", and with K3, you'd wait days if not weeks for a reply in that case.

Don't get me wrong, slow is sometimes better than "not at all", but depending on the performance, it might end up way too slow to even work for batched/async jobs like that.

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walrus01 7 hours ago | parent [-]

I agree it's very likely to be painfully slow, I very much want to see some real world results from people who try it. Early testers will inform others on whether it's even worth trying. Results very much TBD right now. I don't have a system sitting around here with 2TB of greater of RAM that isn't already committed for other uses, regretfully.

	▲	embedding-shape 6 hours ago \| parent \| next [-]
		Lets say an easy response takes 32k tokens in total, and to be generous, let's say it does 1 tok/s. This is already ~9 hours, and 32k reasoning tokens isn't even that much and as mentioned, K3 probably does the longest/most reasoning/thinking out of the available open weights models today, much like GLM. Just lowering that performance to 0.5 tok/s, would lead to ~18 hours for a simple prompt to receive an answer. And then that's just for single prompts, what about agent harnesses, where before every tool call the model could reason a bunch? I agree with you that real world results would be interesting, but I wouldn't hold my breath nor expect it to realistically be able to be useful. Still, people should try it, for science if nothing else :)
	▲	justincormack 3 hours ago \| parent \| prev [-]
		You can rent one in the cloud to try it