Hype and confusion.

OpenClaw is hyped for running local/private LLMs and controlling your data, but these people don't realize the difference between

(1) running local open source LLMs

(2) and API calls to cloud LLMs.

The vast majority will do #2. To your point, a Raspberry Pi is sufficient.

For the former, you still need a lot of RAM (+32GB for larger models) so most minis are underpowered despite having unified memory and higher efficiency.

If you're running local models, Apple Silicon's shared memory architecture makes them much better at it than other similarly-specced platforms.

If you want your "skills" to include sending iMessage (quite important in the USA), then you need a Mac of some kind.

If you don't care about iMessage and you're just doing API calls for the inference, then it's good old Mass Abundance. Nice excuse to get that cool little Mini you've been wanting.

While others will point to hardware or local LLMs or such IMO the biggest reason...

Because it's the easiest way to give "claw" iMessage access and that's the primary communication channel for a lot of the claw users I've seen.

Mac minis are particularly suited to running AI models because they can have a pretty good quantity of RAM (64GB) assigned to the GPU at a reasonable price compared to Nvidia offerings. Mac minis have unified memory which means it can be split between CPU and GPU in a configurable way. I think apple didn’t price mac minis with AI stuff in mind, so they end up being good value.

Where do you get the AI acceleration? Apple Silicon chips are decent AI perf for the price afaiu