(edge-platforms-section)=

Deploy ExecuTorch on mobile, desktop, and embedded platforms with optimized backends for each.

ExecuTorch supports deployment across a wide variety of edge computing platforms, from high-end mobile devices to constrained embedded systems and microcontrollers.

Deploy ExecuTorch on Android devices with hardware acceleration support.

→ {doc}android-section — Complete Android deployment guide

Key features:

• Hardware acceleration support (CPU, GPU, NPU)
• Multiple backend options (XNNPACK, Vulkan, Qualcomm, MediaTek, ARM, Samsung)
• Comprehensive examples and demos

Deploy ExecuTorch on iOS devices with Apple hardware acceleration.

→ {doc}ios-section — Complete iOS deployment guide

Key features:

• Apple hardware optimization (CoreML, MPS, XNNPACK)
• Swift and Objective-C integration
• LLM and computer vision examples

Deploy ExecuTorch on Linux, macOS, and Windows with optimized backends.

→ {doc}desktop-section — Complete desktop deployment guide

Key features:

• Cross-platform C++ runtime
• Platform-specific optimization (OpenVINO, CoreML, MPS)
• CPU and GPU acceleration options

Deploy ExecuTorch on constrained embedded systems and microcontrollers.

→ {doc}embedded-section — Complete embedded deployment guide

Key features:

• Resource-constrained deployment
• DSP and NPU acceleration (Cadence, ARM Ethos-U, NXP)
• Custom backend development support
• LLM and computer vision examples

• {doc}using-executorch-troubleshooting - Common issues and solutions across all platforms

After choosing your platform:

• {doc}backends-section - Deep dive into backend selection and optimization
• {doc}llm/working-with-llms - Working with Large Language Models on edge devices

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:maxdepth: 3
:caption: Edge Platforms

android-section
ios-section
desktop-section
embedded-section
using-executorch-troubleshooting