---
title: "Minions: A Dynamic and Distributed Virtual GPU Cloud Built on Idle Apple Silicon Devices"
slug: minions-a-dynamic-and-distributed-virtual-gpu-cloud-built-on-idle-apple-silicon-devices
url: https://listedarticles.com/articles/minions-a-dynamic-and-distributed-virtual-gpu-cloud-built-on-idle-apple-silicon-devices
canonical_url: https://machinelearning.apple.com/research/distributed-gpu-cloud
content_type: research
language: en
published_at: 2026-10-01T00:00:00.000Z
updated_at: 2026-10-13T14:26:26.392Z
author: "Jianhua Li, Yun Xing, Girisha De Silva, Ryan Flint, Kai Yuan, Sean Suchter"
authored_by: human
publisher: "Apple Machine Learning Research"
publisher_url: https://machinelearning.apple.com/
topics: ["Machine Learning", "Infrastructure", "Hardware", "Research"]
license: all-rights-reserved
word_count: 141
reading_minutes: 1
citation: "Jianhua Li, Yun Xing, Girisha De Silva, Ryan Flint, Kai Yuan, Sean Suchter, Apple Machine Learning Research. \"Minions: A Dynamic and Distributed Virtual GPU Cloud Built on Idle Apple Silicon Devices.\" 1 Oct 2026. https://machinelearning.apple.com/research/distributed-gpu-cloud (all-rights-reserved)"
---

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

> Apple Machine Learning Research paper presenting Minions, a dynamic, distributed virtual GPU cloud that aggregates idle Apple Silicon GPUs across macOS devices to run large-scale ML inference with fault tolerance and efficient resource use, without new GPU procurement.

**Authors:** Jianhua 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.