Welcome to the ExecuTorch Documentation (original) (raw)
ExecuTorch is PyTorch’s solution for efficient AI inference on edge devices — from mobile phones to embedded systems.
Key Value Propositions#
- Portability: Run on diverse platforms, from high-end mobile to constrained microcontrollers
- Performance: Lightweight runtime with full hardware acceleration (CPU, GPU, NPU, DSP)
- Productivity: Use familiar PyTorch tools from authoring to deployment
🎯 Wins & Success Stories#
Quick Navigation#
Get Started
New to ExecuTorch? Start here for installation and your first model deployment.
Deploy on Edge Platforms
Deploy on Android, iOS, Laptops / Desktops and embedded platforms with optimized backends.
Work with LLMs
Export, optimize, and deploy Large Language Models on edge devices.
🔧 Developer Tools
Profile, debug, and inspect your models with comprehensive tooling.
Explore Documentation#
Intro
Overview, architecture, and core concepts — Understand how ExecuTorch works and its benefits
Quick Start
Get started with ExecuTorch — Install, export your first model, and run inference
Edge
Android, iOS, Desktop, Embedded — Platform-specific deployment guides and examples
Backends
CPU, GPU, NPU/Accelerator backends — Hardware acceleration and backend selection
LLMs
LLM export, optimization, and deployment — Complete LLM workflow for edge devices
Advanced
Quantization, memory planning, custom passes — Deep customization and optimization
Tools
Developer tools, profiling, debugging — Comprehensive development and debugging suite
API
API Reference Usages & Examples — Detailed Python, C++, and Java API references
💬 Support
FAQ, troubleshooting, contributing — Get help and contribute to the project
What’s Supported#
Model Types
- Large Language Models (LLMs)
- Computer Vision (CV)
- Speech Recognition (ASR)
- Text-to-Speech (TTS)
- More …
Platforms
- Android & iOS
- Linux, macOS, Windows
- Embedded & MCUs
- Go → Edge
Rich Acceleration
- CPU
- GPU
- NPU
- DSP
- Go → Backends