You're correct about some things but mostly wrong.
Yes, a Mac with 128GB+ will let you load some pretty big models.
However, you're still not going to be able to run them at usable speeds. Here are some M5 Max benchmarks on a Qwen 27B model w/ 290K context.... 12 tokens/sec output.
https://www.reddit.com/r/oMLX/comments/1swztoh/m5_max_128gb_...
And that's a 27B model. So yes, a M5 Max 128GB will let you load some pretty big models - can probably fit 120B in there with room left over for context. But the M5 Max still doesn't have the compute to make it practical, at least from an interactive usage standpoint - 120B dense model is going to be like an order of magnitude slower than 27B. You have to understand the computation going on here. LLMs are basically a huge many-to-many operation, and those operations themselves are pretty heavy.
So back to my previous post... you need three things. You need fast memory, you need a lot of it, and you need GPU compute with direct access to that fast memory. The M5 Max has like, 1.5 of the 3.
The M5 Ultra (if it ever exists) could kinda hit all 3, although actually getting your hands on one will be quite the lottery ticket.
My understanding is this is the advantage that’s pushing huge Mac Studio demand.
This is true, but also, people who made this investment found that they're still not very usable for those HUGE models. Don't take my word for it though. Lots of benchmarks out there. r/localllama is pretty active too.
12 tok/s can absolutely be "usable output" depending on what you're doing. I agree though that the 27B dense model often feels slow due to an overall weakness of memory throughput on that particular platform. Most real-world 120B models though will be MoE-based with only a small fraction of active parameters, and these run quite well. Also, dense models can benefit from batching, which is at least marginally viable with Qwen if you stick to shorter contexts and smaller batches.