It is much more likely for your model to fit in large unified memory of a Mac than the smaller more limited memory of a GPU. Even going with two 5090s, you now have to shard your model and that is a PITA.
But it turns out that MoE is the solution both for running models on macs of limited computer power means (not as fast as GPUs), and on multiple GPUs that require sharding the model.
I bristle at general statements like this when it obviously depends on the specific Mac and GPU in question. But yes, comparing a maxed out M5 Ultra with an RTX 6000, the Mac has much more memory.
> Even going with two 5090s, you now have to shard your model and that is a PITA.
Every modern tool does this for you automatically. It is absolutely not a pain in the least (e.g. llama.cpp ships with pipeline parallelism enabled by default).
If you are using multiple GPUs, MoE is basically going to be your only workable choice unless you can leverage pipeline parallelism (only half your GPUs can work on a prompt at a time, so you need to process prompts back to back in a pipeline setup, and they better be doing similar things because your vram is limited).
I’ve got a 4060 ti 16gb, and I’m thinking about getting another. I previously specced out a cluster using multiple 3090s. At the time, the 3090s were going for $700 on eBay. They’re more than that now, but there’s no need to spend $4k per GPU at all.