Apple published a technical walkthrough October 15 describing Meta’s Muse Spark Open running with MLX and RDMA over Thunderbolt. A four-Mac-Studio setup produces more than 36 tokens per second, according to Apple’s own measurement.
This is an impressive example of unified memory and multi-Mac inference, but the full model is far beyond a 24GB M4 Mac mini. The article’s value for a small-machine owner is the architecture: models, quantization, memory bandwidth and the difference between local privacy and cloud convenience.
The useful next action is to read Apple’s MLX documentation and select a genuinely small model that fits the available RAM rather than chasing a nearly trillion-parameter headline. Measure token speed, memory pressure and task quality on a harmless sample prompt.
The larger implication for software products is that some AI workflows may migrate from remote APIs to local inference when model size and hardware make sense. That is a hypothesis to benchmark, not an automatic cost-saving claim.
What to do: Read Apple’s MLX benchmark and evaluate a small compatible local model on MiniMac; do not attempt the 816B-parameter model on 24GB RAM.