| ▲ | clhodapp 7 hours ago | ||||||||||||||||
At risk of being blunt: Why the surprise that a model that activates 12B parameters is faster than one that activates 16B parameters which is faster than one that activates 42B parameters? It feels like that would be the expected result, all else equal. | |||||||||||||||||
| ▲ | Imustaskforhelp 7 hours ago | parent [-] | ||||||||||||||||
I think it's because of the fact that Deepseek at 16B can be comparable enough in tasks to Mimo with 42B I am just extremely impressed and surprised by the fact at how small we are going. My dream of local inference is perhaps coming close. Regarding 12B one (Nemotron), still, there are other models even comparing it to qwen 9b, its tps difference is: 435 tokens/s vs 68 tokens[0] I am not sure if this is because of how AA does its calculations for the speed but my main point was that I am impressed by the speed improvements happening in general hardware. I remember this speed when it was only expected from cerebras or groq. Also speaking of this I found K2 Horizon[1] which seems to have 1,061.5 tps at 23b and it seems to be really close to actual open source (sharing their training code, pre-training, post-training, checkpoints etc.), I am quite impressed by them as well! [And I think that I wouldn't have found them if it wasn't for their token speed perhaps] [0]: https://artificialanalysis.ai/models/comparisons/nemotron-3-... [1]: https://artificialanalysis.ai/models/k2-horizon-375b-a23b & https://ifm.ai/blog/k2/ | |||||||||||||||||
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