Can start with a simple request over Slack, but teams worry about their data running on devices outside their group.
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The paper, also shared on Apple’s Machine Learning Research site, describes the system as having a “Minion Server,” a “Minion Executor,” and a “Minion Watchdog.” The system relies specifically on Apple Silicon for AI inference, the only type of work it does. There are two kinds of jobs: embeddings with open-source Hugging Face transformer models, and text generation from large language models through Ollama, which is installed on each participating Mac.
The Minion Server works by splitting submitted tasks into subtasks, stored in a PostgreSQL database. Then, on each Mac, a Minion Executor is run to pull the next subtask over REST APIs; there is no central scheduler in this “pull-based execution model.” The Mac reads inputs from Amazon S3, grabs the model if needed, runs the subtask on its own GPU, then returns the result to S3.
Failures are caught by the Minion Watchdog, which tracks the heartbeats that Macs running work send every five minutes. Unfinished subtasks end up back in the queue when, for example, a Mac goes to sleep, drops off the network, powers off, or a user stops the executor to get control of the machine back. The owner can determine which jobs the Mac takes first, such as preferring one team’s or project’s jobs with “dynamic task binding,” or can rely on the default first-in, first-out scheme.
Startups want to rent your idle gaming PC for AI tasks
Key considerations
- Investor positioning can change fast
- Volatility remains possible near catalysts
- Macro rates and liquidity can dominate flows
Reference reading
- https://www.tomshardware.com/tech-industry/artificial-intelligence/SPONSORED_LINK_URL
- https://www.tomshardware.com/tech-industry/artificial-intelligence/apple-researchers-turn-idle-macs-into-a-virtual-gpu-cloud-with-minions-researchers-say-the-distributed-scheme-could-scale-globally-and-support-model-training#main
- https://www.tomshardware.com/membership
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