Local AI on four Macs: MLX capacity, speed and trust boundaries

Apple has demonstrated running large language models, like Meta's Muse Spark Open, across multiple Mac Studio computers using its MLX framework. This development allows for models that exceed the memory capacity of a single machine, though adding more computers doesn't always proportionally increase speed due to communication bottlenecks. For AI/ML practitioners, this highlights the growing importance of hardware considerations when deploying large models locally and emphasizes the need to test specific workloads to understand performance implications.

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