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

🗺️ Find Your Path#

Not sure where to start? Use the guided pathways to navigate ExecuTorch based on your experience level, goal, and target platform.

🟢 New to ExecuTorch

Step-by-step learning sequence from installation to your first on-device deployment. Includes concept explanations and worked examples.

Beginner Pathway

🟡 Get Running Fast

Skip the theory — get a model running in 15 minutes. Includes export cheat sheets, backend selection tables, and platform quick starts.

Quick Start Pathway

🔴 Production & Advanced

Quantization, custom backends, C++ runtime, LLM deployment, and compiler internals for production-grade systems.

Advanced Pathway

🔀 Decision Matrix — Route by Goal, Platform & Model

Not sure which pathway fits? The decision matrix routes you by experience level, target platform, model status, and developer role to the exact documentation you need.

Find Your Path

🎯 Wins & Success Stories#

Quick Navigation#

Get Started

New to ExecuTorch? Start here for installation and your first model deployment.

Quick Start

Deploy on Edge Platforms

Deploy on Android, iOS, Laptops / Desktops and embedded platforms with optimized backends.

Edge

Work with LLMs

Export, optimize, and deploy Large Language Models on edge devices.

LLMs

🔧 Developer Tools

Profile, debug, and inspect your models with comprehensive tooling.

Tools

Explore Documentation#

Intro

Overview, architecture, and core concepts — Understand how ExecuTorch works and its benefits

Intro

Quick Start

Get started with ExecuTorch — Install, export your first model, and run inference

Quick Start

Edge

Android, iOS, Desktop, Embedded — Platform-specific deployment guides and examples

Edge

Backends

CPU, GPU, NPU/Accelerator backends — Hardware acceleration and backend selection

Backends

LLMs

LLM export, optimization, and deployment — Complete LLM workflow for edge devices

LLMs

Advanced

Quantization, memory planning, custom passes — Deep customization and optimization

Advanced

Tools

Developer tools, profiling, debugging — Comprehensive development and debugging suite

Tools

API

API Reference Usages & Examples — Detailed Python, C++, and Java API references

API

💬 Support

FAQ, troubleshooting, contributing — Get help and contribute to the project

Support

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