| ▲ | neumann 2 hours ago | ||||||||||||||||
compared to claude - how 'fast' is it in terms of throughput on your laptop? | |||||||||||||||||
| ▲ | Groxx 10 minutes ago | parent | next [-] | ||||||||||||||||
On an 8GB GPU and 32GB laptop: ~5 words/s while running in Qubes via ollama (I don't have an install at the moment that'll tell me tokens/s). Not exactly a highly tuned setup, but it's a ballpark at least :) Tolerable and usable for some things, though thinking makes it take about a minute to reply in many cases. But getting this kind of thing to run on 8GB of VRAM is the main benefit of the mix-of-experts setup: it can do partial GPU loading and get a ton better throughput than a similarly-sized dense model. | |||||||||||||||||
| ▲ | syntaxing 2 hours ago | parent | prev | next [-] | ||||||||||||||||
I use it with a strix halo server. 35B runs stupidly fast. 27B is about 700 TPS prefill and 30 TPS token generation. Which interestedly is about what Kimi K3 gives me depending on provider. | |||||||||||||||||
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| ▲ | brucehoult 2 hours ago | parent | prev | next [-] | ||||||||||||||||
On my SpacemiT K3 SBC with 32GB RAM (where models run on the eight A100 RISC-V cores with 1024 bit vectors) doing the same task I got 5, 5.8, 6.5 tok/s using gemma-4-26B-A4B-it-QAT-Q4_0.gguf, Qwen3.6-35B-A3B-Q4_K_M.gguf, Qwen3.5-35B-A3B-Q4_K_M.gguf. The corresponding dense models are more in the 2.5-3 tok/s range. Kind of slow, but using only 14W of electricity so the Wh per task is twice as good as using my i9-13900 laptop with 4060 GPU. | |||||||||||||||||
| ▲ | nozzlegear 2 hours ago | parent | prev | next [-] | ||||||||||||||||
It's pretty fast, faster than I could type anyway, but not as fast as Claude of course. My oMLX dashboard says I get about 45 tokens per second from the Qwen model I'm running (I host it on my M1 Mac Studio, not on my laptop). | |||||||||||||||||
| ▲ | 2 hours ago | parent | prev [-] | ||||||||||||||||
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