Apple's Minions turns idle Macs into an internal virtual GPU cloud
Apple researchers published a paper describing Minions, an internal "virtual GPU cloud" that aggregates idle Apple Silicon Macs across the network to run machine learning inference. A Minion Server splits submitted jobs into subtasks stored in PostgreSQL, a Minion Executor on each Mac pulls subtasks over REST APIs and runs them on the local GPU with inputs and outputs in Amazon S3, and a Minion Watchdog requeues unfinished work when a machine sleeps, goes offline, powers off or its user stops the executor, using five-minute heartbeats. The paper says Minions only handles inference — embeddings with Hugging Face transformer models and text generation through Ollama installed on each Mac — and Apple reports that some jobs that once faced 10-plus-hour centralized GPU queues could start within minutes, figures that come from Apple's own testing and leave cross-team data confidentiality as an open concern.
Apple's paper says idle macOS devices can be transformed into a practical distributed GPU inference platform in environments where centralized GPU resources are constrained.
research area Speech and Natural Language Processing, research area Tools, Platforms, Frameworks
content type paperpublished October 2026
Minions: A Dynamic and Distributed Virtual GPU Cloud Built on Idle Apple Silicon Devices
AuthorsJianhua Li, Yun Xing, Girisha De Silva, Ryan Flint, Kai Yuan, Sean Suchter
The increasing demand for GPU-accelerated machine learning inference has exposed limitations in centralized GPU infrastructures, including high cost, limited elasticity, and long wait times under resource constraints. At the same time, modern Apple Silicon–based macOS devices are equipped with powerful GPUs that remain largely idle during normal usage. This paper presents Minions, a dynamic and distributed virtual GPU cloud that aggregates idle Apple Silicon GPUs across macOS devices to execute large-scale machine learning inference workloads. Minions enables teams to leverage existing hardware without requiring additional GPU procurement. The system provides flexible GPU inference task execution, fault tolerance, and efficient resource utilization. Our experience shows that idle macOS devices can be transformed into a practical distributed GPU inference platform in environments where centralized GPU resources are constrained.
#Apple#Minions#Apple Silicon#Mac#Ollama#Hugging Face#PostgreSQL#Amazon S3