Comment by kpw94
9 hours ago
But isn't the prefill speed the bottleneck in some systems* ?
Sure it's order of magnitude faster (10x on Apple Metal?) but there's also order of magnitude more tokens to process, especially for tasks involving summarization of some sort.
But point taken that the parent numbers are probably decode
* Specifically, Mac metal, which is what parent numbers are about
Yes, definitely it's the bottleneck for most use cases besides "chatting". It's the reason I have never bought a Mac for LLM purposes.
It's frustrating when trying to find benchmarks because almost everyone gives decode speed without mentioning prefill speed.
oMLX makes prefill effectively instantaneous on a Mac.
Storing an LRU KV Cache of all your conversations both in memory, and on (plenty fast enough) SSD, especially including the fixed agent context every conversation starts with, means we go from "painfully slow" to "faster than using Claude" most of the time. It's kind of shocking this much perf was lying on the ground waiting to be picked up.
Open models are still dumber than leading closed models, especially for editing existing code. But I use it as essentially free "analyze this code, look for problem <x|y|z>" which Claude is happy to do for an enormous amount of consumed tokens.
But speed is no longer a problem. It's pretty awesome over here in unified memory Mac land :)