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

# Minions: A Dynamic and Distributed Virtual GPU Cloud Built on Idle Apple Silicon Devices Source: [https://machinelearning.apple.com/research/distributed-gpu-cloud](https://machinelearning.apple.com/research/distributed-gpu-cloud) 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\. ## Related readings and updates\. Mac with Apple silicon is increasingly popular among AI developers and researchers interested in using their Mac to experiment with the latest models and techniques\. With MLX, users can explore and run LLMs efficiently on Mac\. It allows researchers to experiment with new inference or fine\-tuning techniques, or investigate AI techniques in a private environment, on their own hardware\. MLX works with all Apple silicon systems, and with the latest… [Read more](https://machinelearning.apple.com/research/exploring-llms-mlx-m5) Accelerating LLM inference is an important ML research problem, as auto\-regressive token generation is computationally expensive and relatively slow, and improving inference efficiency can reduce latency for users\. In addition to ongoing efforts to accelerate inference on Apple silicon, we have recently made significant progress in accelerating LLM inference for the NVIDIA GPUs widely used for production applications across the industry… [Read more](https://machinelearning.apple.com/research/redrafter-nvidia-tensorrt-llm)