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.
Built for current edge workloads
| 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 · Nemotron 3 Diarization · Phonon 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
torch.export capture plus operator, runtime, and selected-backend coverage;
validate numerical accuracy, memory use, and performance on the target. For a
new language-model architecture, see the
custom LLM guide.
Why ExecuTorch
- PyTorch-native workflow: work directly from
torch.export, with PyTorch
program metadata and source mappings available to export and debugging tools.
- Partitioned hardware acceleration: delegate supported graph regions to
CPU, GPU, NPU, or DSP backends while retaining portable CPU kernels for
fallback.
- One source model, explicit targets: reuse the PyTorch model and export
flow, while producing a backend-specific
.pte for each target that needs
hardware specialization.
- A runtime you can right-size: link only the operators, kernels, and
delegates a deployment needs;
selective build
keeps only the required operator kernels.
- Inspect, profile, and extend: use
ETDump,
ETRecord, and
numeric debugging,
or add custom operators and backends.
- Versioned deployment contract: a
.pte created with stable APIs is
guaranteed to load and execute for at least one following non-patch runtime
release; see the
runtime compatibility and
API lifecycle policies.
Install
Install the latest stable Python package in a Python 3.11–3.15 environment:
pip install executorch
On Linux this is the CUDA build, matching the torch package on PyPI. On macOS
and Windows it is the CPU build. For the CPU-only Linux build or another CUDA
version, see
Getting Started.
Install a nightly built from main to use the newest features:
pip install --upgrade --pre executorch torch --extra-index-url https://download.pytorch.org/whl/nightly/cpu
torch is explicit because nightly ExecuTorch wheels do not declare it as a
dependency. This command installs a CPU-only PyTorch nightly. On Linux with an
NVIDIA GPU, select the nightly command matching your CUDA version in the
PyTorch installation selector and
use its nightly/cu* index instead; otherwise, pip can replace a CUDA-enabled
PyTorch installation with the CPU build. ExecuTorch CUDA wheels are published
for Linux only.
This is the beginning of the README. Read the rest at the original. Read the full README ↗
