pytorch/executorch
On-device AI across mobile, embedded and edge for PyTorch
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
3 releases in the last 90 days, the latest 43 days ago, with 349 contributors on GitHub.
Use Cases & Benefits
- Provides a unified solution for deploying PyTorch AI models on-device across mobile, embedded, and edge platforms.
- Enables seamless export, optimization, and deployment with a tiny runtime and multiple hardware backend support without format conversions.
- Use for deploying real-time AI inference on smartphones and embedded devices with minimal footprint and high performance.
- Use for running large language models and multimodal AI on-device using familiar PyTorch APIs and advanced quantization.
- Use for developing cross-platform AI applications that require hardware acceleration on Android, iOS, Linux, Windows, and microcontrollers.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
Other
GitHub could not identify this license; read the LICENSE file before depending on it.
Community files
- README: present
- Contributing guide: present
- Code of conduct: present
Watch-outs
- License not identified by GitHub; read the LICENSE file before depending on it.
General guidance from repository metadata, not legal advice. How we score
From the README
On-device AI inference powered by PyTorch
ExecuTorch is PyTorch's unified solution for deploying AI models on-device—from smartphones to microcontrollers—built for privacy, performance, and portability. It powers Meta's on-device AI across Instagram, WhatsApp, Quest 3, Ray-Ban Meta Smart Glasses, and more.
Deploy LLMs, vision, speech, and multimodal models with the same PyTorch APIs you already know—accelerating research to production with seamless model export, optimization, and deployment. No manual C++ rewrites. No format conversions. No vendor lock-in.
ExecuTorch uses ahead-of-time (AOT) compilation to prepare PyTorch models for edge deployment:
Models use a standardized Core ATen operator set. Partitioners delegate subgraphs to specialized hardware (NPU/GPU) with CPU fallback.
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
Forks
1.2K+138
Open issues
1.4K+112
Compared with peers in Deep Learning
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 1, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| onnx/onnx | 21.6K | — | 63 | 31 days ago |
| mlc-ai/web-llm | 19.3K | — | 59 | 16 days ago |
| lukas-blecher/LaTeX-OCR | 16.6K | — | 18 | — |
| milesial/Pytorch-UNet | 11.7K | — | 18 | — |
| pytorch/executorch | 5.2K | — | 63 | 43 days ago |
| SharpAI/DeepCamera | 3.1K | — | 31 | 227 days ago |
| Category median | 16.6K | — | 31 | — |
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