Executorch: On-device AI across mobile, embedded and edge for PyTorch

Summary

ExecuTorch 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. Why ExecuTorch? 🔒 Native PyTorch Export — Direct export from PyTorch. No .onnx, .tflite, or intermediate format conversions. Preserve model semantics. — Direct export from PyTorch. No .onnx, .tflite, or intermediate format conversions. Preserve model semantics. ⚡ Production-Proven — Powers billions of users at Meta with real-time on-device inference. — Powers billions of users at Meta with real-time on-device inference. 💾 Tiny Runtime — 50KB base footprint. Runs on microcontrollers to high-end smartphones. — 50KB base footprint. Runs on microcontrollers to high-end smartphones. 🚀 12+ Hardware Backends — Open-source acceleration for Apple, Qualcomm, ARM, MediaTek, Vulkan, and more. — Open-source acceleration for Apple, Qualcomm, ARM, MediaTek, Vulkan, and more. 🎯 One Export, Multiple Backends — Switch hardware targets with a single line change. Deploy the same model everywhere. How It Works ExecuTorch uses ahead-of-time (AOT) compilation to prepare PyTorch models for edge deployment: 🧩 Export — Capture your PyTorch model graph with torch.export() ⚙️ Compile — Quantize, optimize, and partition to hardware backends → .pte 🚀 Execute — Load .pte on-device via lightweight C++ runtime Models use a standardized Core ATen operator set. Partitioners delegate subgraphs to specialized hardware (NPU/GPU) with CPU fallback. Learn more: How ExecuTorch Works • Architecture Guide Quick Start Installation pip install exe...