@francoisfleuret They likely won't serve big models from NVMe, so it's unclear to me if the number of layers and sparsity are true limitations or just not worth exploring.
@francoisfleuret You got me curious, and I'm similarly ignorant, so I had claude make this: https://t.co/D5gvDgjl3X
<=1B active is reasonably fast, but I don't know if 1000:1 sparsity works to get to 1T params. Deepseek/Kimi are ~3% sparse
@francoisfleuret@redchethan Fast but maybe not blazing.
Assume FP8, so 100 MB off SSD. 5 GB/s would be 20 ms per token, or 50 tok/s.
But the 5 GB/s is even optimistic, because our queue depth is low and we can't buffer streaming experts. More like 30/s. Impressive for $2k hardware, but not blazingly fast
@jun_song check out Deepseek's conditional memory paper from a few months ago. Something like that might have the effect you're referring to.
Take a larger memory bank sitting in system RAM or even on disk and load up a comparatively small n gram embedding onto GPU at each inference step
@KyeGomezB@realsigridjin@yuekun_yao@hayden_prairie Do you think Opus 4.7 could be a T=1 loop of the same model as mythos?
The number of regressions of 4.7 makes me think they've tried something novel for it, rather than iterative improvement on 4.6
@davepl1968 The problem is that the big matrices get chopped up and run in non deterministic ordering, dependent on the GPU scheduler. And floating point arithmetic is not commutative.
Also temperature in the model is random and not a fixed seed