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ActorNightlytoday at 4:25 PM4 repliesview on HN

> Qwen3.8 27B (Q4_K_M, 17GB) generates at ~14 tokens/s on my Mac Studio M3 Ultra

>~14 tokens/s

For anyone reading that has never ran local llms, please understand that anything under 100 tok/sec is worthless. You are faster typing stuff into Gemini free version that you get with a google account and copy/pasting it in (and you can easily build browser automation with playwright or any other js runtime to have this available in a chat window)


Replies

0x457today at 4:40 PM

I get 80 tok/s on the same model, and it's pretty usable. I'm not chatting with it; it's either given a bag of tokens to generate an answer or it's doing some agentic coding.

https://openrouter.ai/anthropic/claude-opus-5 is it worthless because its 65 tps?

re: gemini

https://openrouter.ai/google/gemini-3.7-flash worthless as well?

That being said, 14 tok/s is pretty slow.

kgeisttoday at 5:13 PM

In agentic scenarios, an LLM has to read far more tokens than it outputs. I think focusing only on the decode speed is somewhat misleading. 14 tok/sec for decode is actually okayish. 93 tok/sec prefill is what's abysmal, my RTX 5090 goes above 2000 tok/sec with 5 bit quants.

wolvoleotoday at 4:34 PM

What? Below 100tk/s is worthless?

I agree that 14t/s is pretty tedious for interactive use, yes. But 50-60tk/s is faster than I can read. 100tk/s is outright fast. Don't forget there is a limit entering content into meatspace.

Also, Gemini may be free but what if I don't want to give all my data to Google? This is precisely why I have a lot of stuff locally.

And will it remain free? How are they going to make back all those trillions of investment?

But yeah I would kinda balk at 14tk/s too that's why I use old datacenter/workstation-class GPUs.

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dannywtoday at 4:58 PM

I mean, if you don't care about your inputs being trained on, you can just use one of the many free models on openrouter.