ExecuTorch is PyTorch's open source stack for running AI locally on phones,
wearables, laptops, browsers, embedded systems, and microcontrollers. Start with
a PyTorch model, capture it with torch.export, optimize it for target hardware,
and run it through C++, Python, Swift/Objective-C, Kotlin/Java, or JavaScript
APIs.
ExecuTorch powers on-device experiences across Instagram, WhatsApp, Facebook, and Messenger, serving billions of people. It also runs AI features on Meta Quest and Ray-Ban Meta devices. See where ExecuTorch is shipping.
Important
Release channels: use the
latest release with
the stable documentation.
This README tracks main; features labeled Main / nightly may change
before release. Use the main documentation
with a source checkout or nightly package.
| Workload | Representative capabilities | Start here |
|---|---|---|
| Local LLM and agent building blocks | Quantized text models, long context, tool calling, speculative decoding, multi-session execution, and an experimental OpenAI-compatible local server | Muse Glimmer · LLM guide · LLM server |
| Voice | Streaming and offline speech recognition, speech synthesis, voice activity detection, and speaker diarization | Voxtral Realtime · Parakeet · Sortformer diarization · Voxtral TTS |
| Multimodal | Text, image, and audio runners; mobile vision-language models | Gemma 4 · Multimodal runner |
| Computer vision | Image classification, object detection, semantic segmentation, and promptable segmentation | MobileNet V2 · YOLO26 · DeepLabV3 · EfficientSAM |
| Embedded AI | Cortex-M CPU kernels, Ethos-U and NXP NPUs, Cadence DSPs, Zephyr, Arduino, and Raspberry Pi Pico workflows | Embedded guide · Cortex-M · Arduino |
These are starting points, not a compatibility list. A model does not need to appear here to run with ExecuTorch: use the standard export guide or adapt the closest model example. Deployment depends on