[oxcidized]

> That's small enough to run well on ~$5,000 of hardware...

Honestly curious where you got this number. Unless you're talking about extremely small quants. Even just a Q4 quant gguf is ~130GB. Am I missing out on a relatively cheap way to run models well that are this large?

I suppose you might be referring to a Mac Studio, but (while I don't have one to be a primary source of information) it seems like there is some argument to be made on whether they run models "well"?

[simonw]

Yes, I mean a Mac Studio with MLX.

An M3 Ultra with 256GB of RAM is $5599. That should just about be enough to fit MiniMax M2 at 8bit for MLX: https://huggingface.co/mlx-community/MiniMax-M2-8bit

Or maybe run a smaller quantized one to leave more memory for other apps!

Here are performance numbers for the 4bit MLX one: https://x.com/ivanfioravanti/status/1983590151910781298 - 30+ tokens per second.

[zht]

It’s kinda misleading to omit the generally terrible prompt processing speed on Macs

30 tokens per second looks good until you have to wait minutes for the first token

[simonw]

The tweet I linked to includes that information in the chart.

[oxcidized]

Thanks for the info! Definitely much better than I expected.

[fzzzy]

Running in cpu ram works fine. It’s not hard to build a machine with a terabyte of RAM.

[oxcidized]

Admittedly I've not tried running on system RAM often, but every time I've tried it's been abysmally slow (< 1 T/s) when I've tried on something like KoboldCPP or ollama. Is there any particular method required to run them faster? Or is it just "get faster RAM"? I fully admit my DDR3 system has quite slow RAM...