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Minions: A Dynamic and Distributed Virtual GPU Cloud Built on Idle Apple Silicon Devices

A paper published in October 2026 by Jianhua Li, Yun Xing, Girisha De Silva, Ryan Flint, Kai Yuan and Sean Suchter presents Minions, a dynamic and distributed virtual GPU cloud that aggregates idle Apple Silicon GPUs across macOS devices to run large-scale machine learning inference workloads. The system provides flexible GPU inference task execution, fault tolerance and efficient resource utilization, letting teams use existing hardware instead of procuring additional GPUs. The authors report that idle macOS devices can be turned into a practical distributed GPU inference platform where centralized GPU resources are constrained.

read2 min views2 publishedOct 12, 2026
Minions: A Dynamic and Distributed Virtual GPU Cloud Built on Idle Apple Silicon Devices
Image: Apple ML Research

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.

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