The short version
- Minions pools the idle GPUs of Apple silicon Macs to run inference jobs, such as embeddings and LLM completions, without buying new GPUs (Apple).
- It's built for one organisation: a server splits jobs into subtasks, Macs pull them, and a watchdog hands a subtask out again when its Mac goes quiet.
- The paper reports queue times falling from up to 10+ hours to minutes, but gives no device counts or speed benchmarks.
- MLX Network is a separate, independent project: anyone's Mac, answers checked against other Macs, rewards in MLX Tokens. It isn't affiliated with Apple.
1What Minions is
"Minions: A Dynamic and Distributed Virtual GPU Cloud Built on Idle Apple Silicon Devices" is a three-page paper by Jianhua Li, Yun Xing, Girisha De Silva, Ryan Flint, Kai Yuan and Sean Suchter of Apple, posted on 9 October 2026 (arXiv) and listed on Apple Machine Learning Research (Apple).
Its starting point: inference jobs such as embedding generation and LLM completion usually wait for central GPU clusters that are expensive to grow and often oversubscribed, while the GPUs in Apple silicon Macs sit largely idle in everyday use. Minions asks whether those idle GPUs can be pooled into a shared inference platform "without additional hardware investment" (paper).
2How it works
- Minion Server takes jobs through a web UI and APIs, checks them and splits each one into subtasks.
- A PostgreSQL database keeps every task, subtask and heartbeat. The Macs never touch it directly.
- Minion Executor runs on each Mac: it pulls subtasks over REST, downloads the inputs from object storage (Amazon S3) and the model if it isn't cached, runs it on the Mac's GPU and writes the results back.
- Minion Watchdog follows the heartbeats, every five minutes, of Macs that are running work, and puts a subtask back in the queue when its Mac sleeps, loses the network, powers off or is stopped by its owner.
There's no central scheduler: Macs race to claim a subtask, and a unique constraint in the database lets exactly one win. Jobs run first in, first out, but a Mac's owner can point it at one team's project first. Embeddings run with Hugging Face transformers and LLM completions with Ollama, on the Mac's GPU (paper).
3What the paper reports
Minions "has been used to support GPU inference workloads across multiple teams". Its one table compares before and after: GPU queue time from hours (up to 10+) to minutes, new GPU requests from required to none, infrastructure cost from high to zero, and scaling effort from manual to organic (paper).
It also reports the human side. Team members ask colleagues over Slack to run the executor for a project, and some people worried about their data running on devices outside their own team. Minions answers that with deployments scoped to one team or organisation, each with its own database and pool of Macs.
4What it doesn't claim
- No benchmarks. No tokens a second, no number of Macs, no comparison with data-centre GPUs.
- No training. Distributed training is named as future work.
- No single giant GPU. Each subtask runs on one Mac; the paper doesn't describe splitting one model across several Macs.
- No result checking. The paper doesn't describe verifying the answers Macs send back.
- No pay and no product. Participating Macs aren't rewarded, and the paper announces no Apple service.
Its conclusion looks further ahead: if idle Macs "across organizations—or potentially across the global community—can be securely federated", they could form a large shared GPU pool. In the paper that's a direction for the future, not something it built (paper).
5How MLX Network differs
| Minions (Apple research) | MLX Network | |
|---|---|---|
| Who takes part | Macs inside one team or organisation | Anyone with an Apple Silicon Mac and MLX Node |
| What runs | Embeddings (Hugging Face) and LLM completions (Ollama) | LLM jobs on Apple's MLX framework |
| Getting work | Macs pull subtasks from a central server | MLX Node asks the network for jobs, within the limits you set |
| Checking answers | Not described | Compared with other Macs' answers and known test jobs |
| Reward | None: existing hardware at zero cost | MLX Tokens every hour, locked for 30 days on a free seat |
| Status | Research paper; training is future work | Mainnet since 18 October 2026, every block on the public explorer |
Both start from the same observation: Apple silicon Macs have capable GPUs that sit idle most of the day. Minions keeps that inside one organisation, among Macs it already trusts. MLX Network opens it to anyone, which is why it checks answers against other Macs and pays for verified work. MLX Network is independent and not affiliated with Apple; its name comes from the MLX framework it runs on (why the name).
Want your Mac to take part? MLX Node is free and works only when and as hard as you allow; the rules are in the whitepaper, and every Mac on the network is on MLX World. MLX Tokens can lose value: read the risks.
6Questions
- Is Apple launching a GPU cloud made of Macs?
- The paper doesn't say so. It describes a system teams already use for inference, and names training and federating idle Macs across organisations as future directions. It announces no product.
- Does Minions combine several Macs into one GPU?
- No. A job is split into independent subtasks and each one runs on a single Mac. The paper doesn't describe running one model across several Macs.
- Which models does Minions run?
- Embedding models from Hugging Face, through the transformers library, and LLM completions through Ollama, on the Macs' GPUs. The paper names no specific models.
- Is MLX Network part of Apple or of Minions?
- No. MLX Network is an independent network of Macs running MLX Node. It isn't affiliated with Apple and has nothing to do with the Minions project.