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

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AI 服务暂时不可用,以下为来源摘要,待恢复后补全翻译: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…

待翻译:Minions: A Dynamic and Distributed Virtual GPU Cloud Built on Idle Apple Silicon Devices
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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 View publication 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. Exploring LLMs with MLX and the Neural Accelerators in the M5 GPU November 19, 2025 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 Accelerating LLM Inference on NVIDIA GPUs with ReDrafter December 18, 2024 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

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  • The increasing demand for GPU-accelerated machine learning inference has exposed limitations in centralized GPU infrastructures, including high cost, limited elasticity, and long…

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