> ARM macbook

Funnily, the only real competitor for Nvidias' GPUs are Macbooks with 128GB of RAM.

And they don't compete in performance.

I see your contemporary hardware choices and raise you my P900 ThinkStation with 256GB of RAM and 48 Xeon cores. Eventually it might even acquire modern graphics hardware.

Can you expand on this a bit? My recent experiences with MLX have been really positive, so I'm curious what footguns you're alluding to here.

(I don't do most of my work locally, but for smaller models its pretty convenient to work on my mbp).

MPS implementations generally lag behind CUDA kernels, especially for new and cutting edge stuff. Sure, if you're only running CPU inference or only want to use the GPU for simple or well established models, then things have gotten to the point where you can almost get the plug and play experience on Apple silicon. But if you're doing research level stuff and training your own models, the hassle is just not worth it once you see how convenient ML has become in the cloud. Especially since you don't really want to store large training datasets locally anyways.

For real

I wish my company would understand this and let us use something else. Luckily, they don't really seem to care that I use my Linux based gaming machine most of the time

What can I say, I enjoy pain!?

Nahh im l33t - intel macbook and no troubles.

can't read? parent clearly says "ARM macbook".