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Welcome to the ExecuTorch Documentation

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

::::{grid} 1 :class-container: success-showcase :::{grid-item-card} :class-header: bg-primary text-white :class-body: text-center View All Success Stories → ::: ::::

Quick Navigation

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:::{grid-item-card} Get Started 🔗 quick-start-section :link-type: doc

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

:::{grid-item-card} Deploy on Edge Platforms 🔗 edge-platforms-section :link-type: doc

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

:::{grid-item-card} Work with LLMs 🔗 llm/working-with-llms :link-type: doc

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

:::{grid-item-card} 🔧 Developer Tools 🔗 tools-section :link-type: doc

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

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Explore Documentation

::::{grid} 1 :::{grid-item-card} Intro 🔗 intro-section :link-type: doc

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

::::{grid} 1 :::{grid-item-card} Quick Start 🔗 quick-start-section :link-type: doc

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

::::{grid} 1 :::{grid-item-card} Edge 🔗 edge-platforms-section :link-type: doc

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

::::{grid} 1 :::{grid-item-card} Backends 🔗 backends-section :link-type: doc

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

::::{grid} 1 :::{grid-item-card} LLMs 🔗 llm/working-with-llms :link-type: doc

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

::::{grid} 1 :::{grid-item-card} Advanced 🔗 advanced-topics-section :link-type: doc

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

::::{grid} 1 :::{grid-item-card} Tools 🔗 tools-section :link-type: doc

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

::::{grid} 1 :::{grid-item-card} API 🔗 api-section :link-type: doc

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

::::{grid} 1 :::{grid-item-card} 💬 Support 🔗 support-section :link-type: doc

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

What's Supported

::::{grid} 3

:::{grid-item} Model Types

  • Large Language Models (LLMs)
  • Computer Vision (CV)
  • Speech Recognition (ASR)
  • Text-to-Speech (TTS)
  • More ... :::

:::{grid-item} Platforms

  • Android & iOS
  • Linux, macOS, Windows
  • Embedded & MCUs
  • Go → {doc}edge-platforms-section :::

:::{grid-item} Rich Acceleration

  • CPU
  • GPU
  • NPU
  • DSP
  • Go → {doc}backends-section :::

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:maxdepth: 1

intro-section
quick-start-section
edge-platforms-section
backends-section
llm/working-with-llms
advanced-topics-section
tools-section
api-section
support-section