Edge

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.

Android

Deploy ExecuTorch on Android devices with hardware acceleration support.

→ Android — 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

iOS

Deploy ExecuTorch on iOS devices with Apple hardware acceleration.

→ iOS — Complete iOS deployment guide

Key features:

• Apple hardware optimization (CoreML, MPS, XNNPACK)

• Swift and Objective-C integration

• LLM and computer vision examples

Desktop & Laptop Platforms

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

→ Desktop & Laptop Platforms — Complete desktop deployment guide

Key features:

• Cross-platform C++ runtime

• Platform-specific optimization (OpenVINO, CoreML, MPS)

• CPU and GPU acceleration options

Embedded Systems

Deploy ExecuTorch on constrained embedded systems and microcontrollers.

→ Embedded Systems — 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

Troubleshooting & Support

• Profiling and Debugging - Common issues and solutions across all platforms

Next Steps

After choosing your platform:

• Backends - Deep dive into backend selection and optimization

• llms-section - Working with Large Language Models on edge devices