The README lacks the most important thing: how many more tokens/sec at the same quantization, compared to llama.cpp / MLX? It is worth to switch default platforms only if there is a major improvement.
Thank you. Strange. If the memory numbers are accurate, it is so slow because likely layers are loaded from disk before inference of each layer or something like that, otherwise it could not do the inference of such model in 500MB. But if that's what it does, 33% of the speed would be already too fast, likely.
Interesting. Does this mean larger models could be ran on less memory? It looks like it uses 15-20x less memory. Could a 671B DeepSeek R1 be ran in just ~40-50GB of memory? It sounds like it'd be 1/3 as fast though (<1tk/sec).
What hardware are you on? Most models are memory bandwidth limited. ANE was limited to 64GB/s prior to M3 Max or M4 pro. If you are on M1, GPU will be significantly faster for 3-8B models due to memory bandwidth rather then ANE capabilities.
M4 max should work at 120GB for ANE and 500+ for GPU. So GPU will be 3-4 times faster for anything over 1-3B. ANE is likely as fast for prefill due to higher FLOPs
Can you add a recent build of llama.cpp (arm64) to the results pool? I'm really interested in comparing mlx to llama.cpp, but setting up the mlx seems too difficult for me to do by myself.
I ran them again several times to make sure the results were fair. My previous runs also had a different 30B model loaded in the background that I forgot about.
LM Studio is an easy way to use both mlx and llama.cpp
Yeah, I looked all over for a comparison and couldn't find anything in the repo, on their social media, etc. I saw some other comments here that said it's supposed to be "15.8 fp16 ops compared to 14.7 fp32 ops" but that isn't really enough to go on. Maybe when I have the time I'll install their TestFlight app and do some comparisons myself.
