by nodja 3 days ago
Same reason why prompt processing is faster than text generation.
When you already know the tokens ahead of time you can calculate the probabilities of all tokens batched together, incurring significant bandwidth savings. This won't work if you're already compute bound so people with macs/etc. won't get as much benefits from this.
Are Macs/etc compute bound with their 'it fits in unified memory' language models? Certainly by the time you're streaming weights from SSD you must be back in a bandwidth-bound regime.
From what I understood, if we’re talking a single user on a mac (not batching) you’re rarely compute bound in the first place. More rows per pass is nearly free that way when cores were sitting idle anyway.
If that’s wrong I would certainly appreciate being corrected, though. But if it’s right, a 2.9x speed-up after rejected tokens, nearly for free, sounds amazing.
That will depend on the model, but they'll hit compute limits before a typical GPU in almost all cases. Macs will still benefit a speedup from this, just not one as big as the one reported.