This doesn't surprise me all that much, mmap support gets little attention in general and interacts poorly with GPU-side inference. (And that's with it being default, you don't even really need to specify it as a CLI option.) OP has raised a discussion with the llama.cpp folks https://github.com/ggml-org/llama.cpp/discussions/20852 but little interest so far
The probes I used seem to help identify good configurations, but are quite noisey. A small probe set was initially used to make the scan tractable, and then the higher ranked models were retested on a set ~10x larger.
This has nothing to do with Apple, and everything to do with MoE and that everyone forgot you can re-read the necessary bits of the model from disk for each token.
This is extremely inefficient though. For efficiency you need to batch many requests (like 32+, probably more like 128+), and when you do that with MoE you lose the advantage of only having to read a subset of the model during a single forward pass, so the trick does not work.
But this did remind me that with dense models you might be able to use disk to achieve high throughput at high latency on GPUs that don't have a lot of VRAM.
