Minions is an internal enterprise system already in use at Apple. It pools idle Mac GPUs into a shared compute cluster for ML inference.
There is no distributed execution of a single LLM across multiple GPUs using tensor parallelism here. Instead, it relies on task-level parallelism-breaking down a large workload into independent subtasks that run across separate machines. For example, generating embeddings for a million documents.
My university ran its render farm across idle workstations using the screensaver back in early 2000. Same concept.
This pattern has been implemented across hundreds if not thousands of different organizations (and between organizations). I have a patent on data distribution strategies from my time at Google running a large (>1M cores 24/7 for 3 years) idle cycle harvesting project.
it's a good idea but you often end up with huge tail latencies, slight incompatibilities due to fleet heterogeneity, and a database/scheduler management problem.