Export a trained PyTorch model and run it inside an iOS app without any internet connection
Deploy a Llama language model to an Android phone to run offline text generation
Target multiple hardware chips from the same PyTorch model by changing a single export line
Run AI inference on a microcontroller using a model quantized down to fit in tight memory
| pytorch/executorch | zju3dv/easymocap | going-doer/paper2code | |
|---|---|---|---|
| Stars | 4,602 | 4,602 | 4,601 |
| Language | Python | Python | Python |
| Setup difficulty | hard | hard | moderate |
| Complexity | 4/5 | 4/5 | 3/5 |
| Audience | developer | researcher | researcher |
Figures from each repo's GitHub metadata at analysis time.
Requires matching PyTorch and ExecuTorch versions, hardware backends need additional SDK setup (Xcode for iOS, Android NDK for Android).
ExecuTorch is a tool from the PyTorch team that lets developers take AI models they have already built and trained using PyTorch, then run those models directly on phones, tablets, embedded devices, and microcontrollers, rather than requiring a connection to a cloud server. It is used inside Meta's products including Instagram, WhatsApp, the Quest 3 headset, and Ray-Ban Meta smart glasses. The general idea is that you prepare your model once on a regular computer, and ExecuTorch converts it into a compact file format that a small, lightweight program can execute on the target device. That on-device program has a base footprint of about 50 kilobytes, which is small enough to fit on quite constrained hardware. The conversion process can also apply optimizations such as quantization, which makes models smaller and faster at a small cost to precision. One of the notable aspects is hardware flexibility. The same converted model file can target many different processor types, including chips from Apple, Qualcomm, ARM, and MediaTek, as well as standard CPUs. Switching from one hardware backend to another requires changing a single line in the export step, not rewriting the model. Once a model is exported, you can run it from C++ code, from Swift on iOS, or from Kotlin on Android. The README includes short code examples for all three languages. Large language models such as Llama can also be exported and run on-device using the same workflow, with dedicated runner APIs for text generation. The project is open-source and installable via pip. Full documentation lives at the PyTorch documentation site, and a Discord community is available for questions and discussion.
ExecuTorch lets you run PyTorch AI models directly on phones, tablets, and microcontrollers with a 50KB on-device runtime, supporting chips from Apple, Qualcomm, ARM, and MediaTek with no cloud connection needed.
Mainly Python. The stack also includes Python, C++, Swift.
Setup difficulty is rated hard, with roughly 1h+ to a first successful run.
Mainly developer.
This repo across BitVibe Labs
Verify against the repo before relying on details.