ExecuTorch -- A Unified PyTorch Solution to Run AI Models On-Device
Authors:
Mergen Nachin,
Digant Desai,
Sicheng Stephen Jia,
Chen Lai,
Mengwei Liu,
Jacob Szwejbka,
Raziel Alvarez,
RJ Ascani,
Dave Bort,
Manuel Candales,
Andrew Caples,
Yanan Cao,
Zhengxu Chen,
Soumith Chintala,
Gregory Comer,
Tanvir Islam,
Songhao Jia,
Tarun Karuturi,
Jack Khuu,
Abhinay Kukkadapu,
Tugsbayasgalan Manlaibaatar,
Andrew Or,
Kimish Patel,
Siddartha Pothapragada,
Lucy Qiu
, et al. (14 additional authors not shown)
Abstract:
Local execution of AI on edge devices is important for low latency and offline operation. However, deploying models on diverse hardware remains fragmented, often requiring model conversion or complete reimplementation outside the PyTorch ecosystem where the model was originally authored. We introduce ExecuTorch, a unified PyTorch-native deployment framework for edge AI. ExecuTorch enables seamless…
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Local execution of AI on edge devices is important for low latency and offline operation. However, deploying models on diverse hardware remains fragmented, often requiring model conversion or complete reimplementation outside the PyTorch ecosystem where the model was originally authored. We introduce ExecuTorch, a unified PyTorch-native deployment framework for edge AI. ExecuTorch enables seamless deployment of machine learning models across heterogeneous compute environments. It scales from embedded microcontrollers to complex system-on-chips (SoCs) with dedicated accelerators, powering devices ranging from wearables and smartphones to large compute clusters. ExecuTorch preserves PyTorch semantics while allowing customization, support for optimizations like quantization, and pluggable execution "backends". These features together enable fast experimentation, allowing researchers to validate deployment behavior entirely within PyTorch, bridging the gap between research and production.
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Submitted 5 May, 2026;
originally announced May 2026.