Meta's ExecuTorch - Accelerating On-Device Machine Learning
The latest announcements highlight the growing importance of on-device machine learning, with Meta’s open-source ExecuTorch framework driving significant performance, privacy, and efficiency improvements for ML models across its major apps. ExecuTorch enables compact, stable deployment of AI features—including image cutouts, video quality enhancements, and music recommendations—demonstrating strong industry collaboration and scalability for edge AI solutions. Meta encourages community contributions to advance further innovation in on-device ML.
Accelerating on-device ML on Meta’s family of apps with ExecuTorch
Source: Meta AI
ExecuTorch, the PyTorch inference framework for edge devices developed by Meta with industry support, has significantly improved on-device ML models in Meta’s apps, enhancing performance and privacy. Its open-source nature and collaboration with industry leaders have resulted in stable, compact model deployment. ExecuTorch has enabled features like Cutouts on Instagram, improved video quality on WhatsApp, E2EE on Messenger, and background music suggestions on Facebook. Adoption of ExecuTorch has led to performance enhancements across different models, showing promise in scaling on-device AI solutions. The blog invites contributions to the ExecuTorch community for future innovations in on-device ML.