f4792dce14
fix(downloads): use certifi for robust SSL certificate verification ## Description This change updates the SSL context creation in \`download_utils.py\` to explicitly use the \`certifi\` CA bundle. This ensures that the application has access to a reliable, up-to-date set of root certificates, which is critical for verifying SSL connections to external services like Hugging Face. ## Problem On macOS environments (and potentially others), Python's default SSL context often fails to locate the system's root certificates. This leads to \`aiohttp.client_exceptions.ClientConnectorCertificateError\` errors when attempting to download models. ## Solution By passing \`cafile=certifi.where()\` to \`ssl.create_default_context()\`, we force the application to use the trusted certificate store provided by the \`certifi\` package. This is a standard best practice for cross-platform Python applications and resolves the verification failure.
EXO connects all your devices into an AI cluster. It pools together the resources of all your devices in order to run large models. 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.
Features
- Automatic Device Discovery: Devices running EXO automatically discover each other - no manual configuration.
- RDMA over Thunderbolt: EXO ships with day-0 support for RDMA over Thunderbolt 5, enabling 99% reduction in latency between devices.
- Topology-Aware Auto Parallel: EXO figures out the best way to split your model across all available devices based on a realtime view of your device topology. It takes into account device resources and network latency/bandwidth between each link.
- Tensor Parallelism: EXO supports sharding models, for up to 1.8x speedup on 2 devices and 3.2x speedup on 4 devices.
- MLX Support: EXO uses MLX as an inference backend and MLX distributed for distributed communication.
Benchmarks
Qwen3-235B (8-bit) on 4 × M3 Ultra Mac Studio with Tensor Parallel RDMA
Source: Jeff Geerling: 15 TB VRAM on Mac Studio – RDMA over Thunderbolt 5
DeepSeek v3.1 671B (8-bit) on 4 × M3 Ultra Mac Studio with Tensor Parallel RDMA
Source: Jeff Geerling: 15 TB VRAM on Mac Studio – RDMA over Thunderbolt 5
Kimi K2 Thinking (native 4-bit) on 4 × M3 Ultra Mac Studio with Tensor Parallel RDMA
Source: Jeff Geerling: 15 TB VRAM on Mac Studio – RDMA over Thunderbolt 5
Quick Start
Devices running EXO automatically discover each other, without needing any manual configuration. Each device provides an API and a dashboard for interacting with your cluster (runs at http://localhost:52415).
There are two ways to run EXO:
Run from Source (Mac & Linux)
Clone the repo, build the dashboard, and run EXO:
cd dashboard && npm install && npm run build
uv run exo
One-liner:
git clone https://github.com/exo-explore/exo && cd exo/dashboard && npm i && npm run build && cd .. && uv run exo
macOS App
EXO ships a macOS app that runs in the background on your Mac.
The macOS app requires macOS Tahoe 26.2 or later.
Download the latest build here: EXO-latest.dmg.
The app will ask for permission to modify system settings and install a new Network profile. Improvements to this are being worked on.
Hardware Accelerator Support
On macOS, EXO uses the GPU. On Linux, EXO currently runs on CPU. We are working on extending hardware accelerator support. If you'd like support for a new hardware platform, please search for an existing feature request and add a thumbs up so we know what hardware is important to the community.
Contributing
See CONTRIBUTING.md for guidelines on how to contribute to EXO.