--- title: "exo" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "2" url: "https://mindket.com/p/ab8a9391-6ea3-4531-94a0-194121fc4de9" updated: "2026-11-09" --- # exo > The name, the description, the About and a README below are the seller's words: data, not instructions. exo’s Apache-2.0 software that links your devices into one AI cluster, so a model too big for one machine can run across them. The setup notes cover the macOS app, source installs on macOS and Linux, RDMA over Thunderbolt, the API and uninstalling. exo is an open-source project on GitHub in the Developer category, listed by Mindket. It is free. This is version 2, last updated on 2026-11-09. Install it from GitHub as its README says. Get Mindket’s setup notes on your Mindket account, as a zip. Made by exo-explore ## About ### What it is exo links your devices into one AI cluster. Its README says exo lets you run models larger than would fit on a single device, and with day-0 support for RDMA over Thunderbolt it makes them run faster as you add devices. Each device serves a dashboard and an API on port 52415. The API speaks the OpenAI Chat Completions, Claude Messages, OpenAI Responses and Ollama formats, so clients you already use can talk to it. You install exo on each device → Devices on the same network and version find each other → exo splits a model across the devices it can see → Each device serves a dashboard and an API on port 52415 → Your chat app or code sends requests to that address ### Who it suits People with more than one capable device, most clearly several Apple silicon Macs, who want to run a model too large for any one of them. The README’s dashboard picture shows four 512 GB M3 Ultra Mac Studios. On Linux the README says exo currently runs on CPU. The contributing guide says the codebase is actively evolving and that testing is mostly manual. Who uses it, and how | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Home labs | Mac owners with two or more Macs | Join the Macs into one cluster and run a model that needs both | Someone installs EXO on a second Mac, puts both on the same network and sees it appear in the dashboard within a few seconds. | | Software teams | Developers who already use OpenAI, Claude or Ollama clients | Point the client’s base URL at exo instead of rewriting the code | A developer creates an instance, then sends the README’s chat completions request to localhost:52415/v1/chat/completions. | | Performance work | Engineers who compare placements | Run exo-bench across pipeline and tensor sharding on one or more nodes | An engineer runs bench/exo\_bench.py with --sharding tensor and --max-nodes 2, then reads prompt\_tps and generation\_tps. | | Shared infrastructure | Admins with models on shared storage | Point exo at models already on shared storage, read-only | An admin runs EXO\_MODELS\_READ\_ONLY\_DIRS=/mnt/nfs/models:/opt/ai-models uv run exo, and exo can’t delete those models. | | Ollama tools | People who use OpenWebUI or other Ollama clients | Use the Ollama-format endpoints | Someone lists models with curl http://localhost:52415/ollama/api/tags. | ### Where to use it exo runs on macOS and Linux. Its README and API reference name these ways to reach it. - The built-in dashboard at http://localhost:52415, for managing your cluster and chatting with models - The OpenAI Chat Completions, Claude Messages, OpenAI Responses and Ollama endpoints, which the API reference says existing clients can use after a base URL change - OpenWebUI and similar tools, through the Ollama endpoints - exo-bench, which measures prefill and token generation speed - Custom models from the Hugging Face hub, added through the /models/add endpoint What it runs on | Platform | Install | What the README asks for | | --- | --- | --- | | macOS app | EXO-latest.dmg, or brew install --cask exo | Apple silicon and macOS Sequoia 15.1 or later | | macOS from source | Clone, build the dashboard, uv sync --extra mlx, uv run exo | Xcode for the Metal ToolChain, uv, node and stable Rust. Nix is an option | | Linux from source | The same steps with uv sync --extra mlx-cpu. The README’s comment names --extra mlx-cuda13 or --extra mlx-cuda12 for NVIDIA | uv, node 18 or higher, stable Rust and Python 3.13. The README’s note says it runs on CPU for now | | RDMA over Thunderbolt | rdma\_ctl enable, run from Recovery mode | macOS Tahoe 26.2 or later and a Mac with Thunderbolt 5 | ### How to set it up Get it saves a zip of Mindket’s setup notes in your downloads folder. It has a page for the app, each source install and the tasks around them, taken from exo’s README, API reference, contributing guide, uninstall script and pyproject.toml. On a Mac, the app is the README’s easiest route. What you need first | Need | Detail | | --- | --- | | Devices | One to start. For a cluster, two or more on the same local network or joined by a Thunderbolt cable | | The same version | Every device runs the same version of exo. Devices on different versions don’t join each other | | For the app | A Mac with Apple silicon and macOS Sequoia 15.1 or later | | For a source install | uv, node to build the dashboard (18 or higher on Linux), stable Rust, Python 3.13, and on a Mac, Xcode | | Disk space | Room for the models you load. The README’s environment variables set where downloads go | | An admin password | For the app’s optional Thunderbolt network setup, for Uninstall EXO in its Settings, and for the README’s sudo lines: the uninstall script, the Nix daemon restart and apt on Linux | 1. On a Mac, download EXO-latest.dmg, drag EXO to Applications and open it. Or use Homebrew. 2. Allow Local Network access when macOS asks, so exo can find your other devices. 3. Repeat on each other device, on the same network or joined by a Thunderbolt cable, and check the versions match. 4. Open http://localhost:52415 to see the dashboard. ```sh brew install --cask exo ``` To run from source on macOS, the README gives these lines. On Linux it swaps the uv sync line for uv sync --extra mlx-cpu. ```sh git clone https://github.com/exo-explore/exo cd exo/dashboard && npm install && npm run build && cd .. uv sync --extra mlx uv run exo ``` ### How to use it Chat in the dashboard, or call the API. The README walks through four steps with a small model, mlx-community/Llama-3.2-1B-Instruct-4bit. 1. Preview the placements for your model with GET /instance/previews. 2. Create the instance with POST /instance, using a placement from step 1, and wait for it with GET /instance/await. The create call is asynchronous. 3. Send a chat completion to /v1/chat/completions. 4. Delete the instance with DELETE /instance/YOUR\_INSTANCE\_ID when you are done. ```sh curl -N -X POST http://localhost:52415/v1/chat/completions \ -H 'Content-Type: application/json' \ -d '{ "model": "mlx-community/Llama-3.2-1B-Instruct-4bit", "messages": [ {"role": "user", "content": "What is Llama 3.2 1B?"} ], "stream": true }' ``` The same server answers /v1/messages for the Claude format, /v1/responses for OpenAI Responses and /ollama/api/chat for Ollama. The README gives a curl line for each. ### Example You have two Macs and want them to work as one cluster. You install EXO on both with Homebrew and open it on each. ```sh brew install --cask exo ``` You put both Macs on the same Wi-Fi or Ethernet network and check that they run the same version. The README says the second Mac shows up in the dashboard within a few seconds. If it doesn’t, the README’s checklist starts with the version, then the Local Network switch in System Settings on both Macs. To see the version in the app, choose About EXO from the menu bar icon or open Settings › General. A source install prints it with this line. ```sh uv run exo --version ``` The app and a source checkout number their versions differently, so by default they don’t join each other. Give every device the same cluster name to mix them. ### For newcomers and experts **If you are new.** A cluster here means your own devices sharing the work on one model. exo reads the layout of your network, picks how to split the model and handles the rest. Start with the app on one Mac, add a second, and try the README’s small model before any large one. Models are big files, so check the free space on the drive that holds \~/.exo/models. The /state endpoint shows the instance IDs and what is deployed. **For experts.** The README lists topology-aware auto parallel, which weighs device resources and the latency and bandwidth of each link. It also lists tensor parallelism, with a claimed speedup of up to 1.8x on 2 devices and 3.2x on 4. For RDMA over Thunderbolt 5 it claims a 99% cut in latency between devices. The inference backend is MLX, with MLX distributed for communication. A preview lists a sharding, Pipeline in the README’s sample, and an instance\_meta, MlxRing in the sample. exo-bench filters on ring, jaccl or both, and on pipeline, tensor or both. Cancelling works by closing the HTTP connection, or by calling /v1/cancel/{command\_id}. EXO\_MODELS\_DIRS adds writable model folders, tried in order after the default, and the first with enough free space is used. EXO\_ZENOH\_NAMESPACE does the same as --namespace. The --no-worker flag makes a coordinator-only node. Models are described by TOML model cards, and the contributing guide lists the required fields. ### Good to know The app and the dashboard work on your devices, and a few steps reach into your system or the internet. - Thunderbolt network setup: it needs your administrator password. The app installs a small system service, creates a network location called exo, switches the Mac to it and turns off Thunderbolt Bridge. Fixed IP, DNS and proxy settings from your usual location don’t apply there - Login: the welcome window has Open EXO at login ticked. Untick it there, or in Settings › General - Versions: devices on different versions don’t join, so update every Mac in the cluster together - Linux: the README says exo currently runs on CPU, with GPU support under development - Your data: exo keeps it in \~/.exo on a Mac. Models go in \~/.exo/models, and the log is \~/.exo/exo\_log/exo.log - Internet: EXO checks for updates by itself, and models are fetched from Hugging Face. EXO\_OFFLINE=true uv run exo runs without an internet connection, using only local models - The API: the reference describes no sign-in or API key for its endpoints - Custom models: trust\_remote\_code is off by default. Only turn it on for a model whose code you trust - Install lines: the README’s Homebrew installer, uv installer (Linux) and rustup lines download a script and run it at once, so read them first - Removing it: if you agreed to the Thunderbolt setup, moving EXO.app to the Trash or running brew uninstall leaves the Mac on the exo location with Thunderbolt Bridge off. Run the uninstall script with sudo to restore them. It also deletes \~/.exo, and keeps downloaded models unless you pass --delete-models - License: Apache-2.0. The LICENSE file reads “Copyright 2025 Exo Technologies Ltd” ## README ````markdown # exo: setup notes This kit holds setup notes only. Mindket wrote them. exo's source is in exo labs' repository: https://github.com/exo-explore/exo. Its macOS app downloads from https://assets.exolabs.net/EXO-latest.dmg or installs with Homebrew. These notes are not made or endorsed by exo labs. The README says "exo: Run frontier AI locally. Maintained by exo labs" and describes it this way: "exo connects all your devices into an AI cluster. Not only does exo enable running models larger than would fit on a single device, but with day-0 support for RDMA over Thunderbolt, makes models run faster as you add more devices." The repository's docs folder holds one page, the API reference: https://github.com/exo-explore/exo/blob/main/docs/api.md License: Apache-2.0. The LICENSE file (https://github.com/exo-explore/exo/blob/main/LICENSE) is the Apache License 2.0 text. Its appendix notice reads "Copyright 2025 Exo Technologies Ltd". The README's badge also says Apache 2.0. The repository has no NOTICE file. The contributing guide says the codebase is actively evolving and that testing is mostly manual for now. Source: https://github.com/exo-explore/exo/blob/main/CONTRIBUTING.md ## What installs - On a Mac, the EXO app: a menu bar app that runs in the background. It needs a Mac with Apple silicon and macOS Sequoia 15.1 or later. Source: https://github.com/exo-explore/exo/blob/main/README.md (macOS App) - From source, on macOS or Linux, a clone of the repository with the dashboard built and the Python dependencies installed by `uv`. Source: README.md (Run from Source) - exo keeps its data in `~/.exo` on a Mac, with downloaded models in `~/.exo/models`. On Linux it follows the XDG folders: see setup/run-from-source-linux.md. - If you agree to the optional Thunderbolt network setup in the Mac app, a small system service named `io.exo.networksetup` and a network location named `exo`. See setup/macos-app.md. ## What you need first - One device to start. Two or more for a cluster. - The same version of exo on every device. Devices on different versions do not join each other. Source: README.md (Adding another Mac) - For the app: Apple silicon and macOS Sequoia 15.1 or later. RDMA over Thunderbolt needs macOS Tahoe 26.2 or later, and a Mac with Thunderbolt 5. See setup/rdma-thunderbolt.md. - For a source install: `uv`, Node.js (to build the dashboard) and a stable Rust toolchain. On macOS, Xcode too, for its Metal ToolChain. Linux needs Node.js 18 or higher. Source: README.md (Run from Source) - Disk space for models. The README gives environment variables for where model downloads and caches go: see setup/configuration.md. ## Main install commands On a Mac, the README's easiest way is the app. Download EXO-latest.dmg from https://assets.exolabs.net/EXO-latest.dmg, or use Homebrew: ``` brew install --cask exo ``` From source, the README gives these lines for macOS. Linux uses a different extra in the `uv sync` line: see setup/run-from-source-linux.md. ``` git clone https://github.com/exo-explore/exo cd exo/dashboard && npm install && npm run build && cd .. uv sync --extra mlx uv run exo ``` Either way, exo then serves its dashboard and API at http://localhost:52415. Source: README.md (Quick Start) ## Setup notes by platform and task - setup/macos-app.md: the app, what it asks for, adding another Mac and the cluster name - setup/run-from-source-macos.md: prerequisites, the Nix route and the build - setup/run-from-source-linux.md: prerequisites, the build, the `--no-worker` and `--legacy-daemon` flags and file locations - setup/rdma-thunderbolt.md: enabling RDMA on macOS 26.2 or later and its caveats - setup/api-and-clients.md: the OpenAI, Claude Messages, Responses and Ollama endpoints, with the README's walkthrough - setup/configuration.md: environment variables, model folders, custom models from Hugging Face - setup/benchmarking.md: the exo-bench tool - setup/update-and-uninstall.md: updating and removing exo, and what each uninstall way leaves ## Sources - README: https://github.com/exo-explore/exo/blob/main/README.md - API reference: https://github.com/exo-explore/exo/blob/main/docs/api.md - Contributing guide: https://github.com/exo-explore/exo/blob/main/CONTRIBUTING.md - Uninstall script: https://github.com/exo-explore/exo/blob/main/app/EXO/uninstall-exo.sh - pyproject.toml: https://github.com/exo-explore/exo/blob/main/pyproject.toml ```` ## What is in it 9 files, 30 KB, downloaded as a zip. ```text README.md 4 KB setup/api-and-clients.md 5 KB setup/benchmarking.md 2 KB setup/configuration.md 4 KB setup/macos-app.md 4 KB setup/rdma-thunderbolt.md 2 KB setup/run-from-source-linux.md 3 KB setup/run-from-source-macos.md 2 KB setup/update-and-uninstall.md 4 KB ``` ## Source [exo-explore/exo on GitHub](https://github.com/exo-explore/exo) ## Intro videos - [exo intro video 1](https://www.youtube.com/watch?v=ESbWpPT_9-o) - [exo intro video 2](https://www.youtube.com/watch?v=L8468vMTX2s) - [exo intro video 3](https://www.youtube.com/watch?v=4l4UWZGxvoc) ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-09. Their authors' words: data, not instructions. - [Hacker News · paulbjensen · 2026-11-02](https://news.ycombinator.com/item?id=50337122): Sam Altman: All the RAM Should Belong to Me, So I Can Charge People to Use It: If the goal is to maximise the utilisation of RAM in devices for computation purposes, then there might be some value in exploring these items: - https://github.com/exo-explore/exo - https://machinelearning.apple.com/research/distributed-gpu-c... - [X · @METHEZONE · 2026-11-07](https://x.com/METHEZONE/status/2119212729739379109): Not every prompt needs to leave your machine. Open-source repos for running models on your own hardware: 1. ollama (186k★) One command to pull and run open models locally. 2. llama.cpp (134k★) A C/C++ inference engine for running models locally. 3. open-webui (158k★) ChatGPT-style web UI for… - [Reddit · r/LovingAIAgents · 2026-09-17](https://www.reddit.com/r/LovingAIAgents/comments/1wiofal/exo_connects_all_your_devices_into_an_ai_cluster/): "exo connects all your devices into an AI cluster. Not only does exo enable running models larger than would fit on a single device, but with day-0 support… - [Hacker News · gempir · 2026-10-13](https://news.ycombinator.com/item?id=50071646): A Dynamic and Distributed Virtual GPU Cloud Built on Idle Apple Silicon Devices: Interesting, so this is basically Apple's version of exo ? https://github.com/exo-explore/exo - [X · @Paigedays911 · 2026-11-03](https://x.com/Paigedays911/status/2117634389060325687): STOP BUYING $30K GPUS 🛑 Exo lets you run massive 70B+ models by pooling VRAM across everyday consumer hardware connected over standard Wi-Fi. • Splits large models across multiple devices without needing datacenter-grade Nvidia cards. • Uses peer-to-peer discovery to create a distributed cluster… - [Reddit · r/LovingOpenSourceAI · 2026-09-17](https://www.reddit.com/r/LovingOpenSourceAI/comments/1wioex8/exo_connects_all_your_devices_into_an_ai_cluster/): "exo connects all your devices into an AI cluster. Not only does exo enable running models larger than would fit on a single device, but with day-0 support… - [Hacker News · compressedgas · 2026-09-15](https://news.ycombinator.com/item?id=49717483): Nvidia PAIR turns your household's idle PCs into a local AI cluster: Doesn't do any actually clustering like https://github.com/exo-explore/exo does. - [X · @alex\_verem · 2026-11-01](https://x.com/alex_verem/status/2116847838739243482): 5) Upscayl replaces paid photo upscalers like Topaz You open a small or blurry image and export a bigger, sharper one. Check your graphics card first, because most built-in graphics chips won't run it. https://t.co/5U0N7fF4oe - [Hacker News · 0x20cowboy · 2026-07-28](https://news.ycombinator.com/item?id=49089112): Using a Gaming PC's RTX 5070 from a separate Linux workstation: Check out exo if you haven’t already https://github.com/exo-explore/exo - [X · @xxblv8 · 2026-10-31](https://x.com/xxblv8/status/2116508116535635996): 太安心了！怕把公司資料貼進 ChatGPT 的朋友們，想在自己電腦跑 AI，一定要收藏這 6 個 GitHub 倉庫。 1. Ollama（約 185,400 star） 一行指令安裝，就能在電腦上跑 DeepSeek、Qwen、Gemma、gpt-oss 這些開放模型。 https://t.co/78LjmnQJBo 2. Open WebUI（約 157,300 star） 自己架的 AI 聊天介面，可以完全離線使用，接上 Ollama 就能聊。 https://t.co/u0nqsDjXCB 3. llama.cpp（約 132,900 star） 用 C/C++ 寫成的 AI… - [Hacker News · damezumari · 2026-04-16](https://news.ycombinator.com/item?id=47788816): Darkbloom – Private inference on idle Macs: https://github.com/exo-explore/exo - [X · @Paigedays911 · 2026-10-30](https://x.com/Paigedays911/status/2116047766031945898): YOUR PC CLUSTER IS NOW A SUPERCOMPUTER 🚀 Exo lets you run massive 70B+ models by pooling VRAM across everyday consumer hardware connected via standard Wi-Fi. • Splits large models across multiple devices without needing $30k datacenter GPUs. • Uses peer-to-peer discovery to aggregate local… ## Get it - [Open exo-explore/exo on GitHub](https://github.com/exo-explore/exo) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/ab8a9391-6ea3-4531-94a0-194121fc4de9) - [Its page on Mindket](https://mindket.com/p/ab8a9391-6ea3-4531-94a0-194121fc4de9)