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Desktop & Laptop Platforms

ExecuTorch provides robust, high-performance deployment capabilities for desktop and laptop environments across macOS, Linux, and Windows. By leveraging native hardware acceleration and a cross-platform C++ runtime, developers can execute PyTorch models efficiently on CPUs, GPUs, and dedicated AI accelerators (NPUs/ANEs).

This section provides comprehensive, platform-specific guides for setting up, building, and optimizing ExecuTorch for native desktop execution.

Platform-Specific Guides

Select your target operating system below for detailed setup instructions, prerequisites, and backend integration steps.

::::{grid} 3 :::{grid-item-card} macOS :class-card: card-prerequisites → {doc}desktop-macos

Native execution on Apple Silicon and Intel Macs using Core ML, MPS, and XNNPACK. ::: :::{grid-item-card} Linux :class-card: card-prerequisites → {doc}desktop-linux

High-performance deployment on Linux distributions using XNNPACK and OpenVINO. ::: :::{grid-item-card} Windows :class-card: card-prerequisites → {doc}desktop-windows

Native Windows and WSL support using XNNPACK and OpenVINO with Visual Studio. ::: ::::

Backend Hardware Support

ExecuTorch relies on specialized backends to map model execution to the underlying desktop hardware. The table below summarizes the available backends and their supported platforms.

Backend Primary Hardware Target macOS Linux Windows Key Features
XNNPACK CPU (ARM64, x86-64) ✅ ✅ ✅ Highly optimized CPU execution; supports fp32, fp16, and 8-bit quantization. Included by default.
Core ML Apple CPU, GPU, ANE ✅ ❌ ❌ Dynamic dispatch across Apple hardware; recommended for Apple Silicon.
MPS Apple Silicon GPU ✅ ❌ ❌ Direct execution on Metal Performance Shaders for high-throughput GPU inference.
OpenVINO Intel CPU, GPU, NPU ❌ ✅ ✅ Intel-optimized execution across integrated graphics, discrete GPUs, and NPUs.
Vulkan Cross-platform GPU ❌ ✅ ❌ GPU execution via GLSL compute shaders; primarily focused on Android but supports Linux.

Core Runtime Integration

Regardless of the target desktop platform, integrating ExecuTorch into a native application follows a consistent pattern using the C++ Module API.

  • {doc}using-executorch-cpp — Learn how to use the C++ Module API to load .pte files, configure memory allocation, and execute inferences natively.
  • {doc}using-executorch-building-from-source — Comprehensive reference for the CMake build system, configuration options, and presets used across all desktop platforms.

Tutorials

  • {doc}raspberry_pi_llama_tutorial — Cross compiling ExecuTorch for the Raspberry Pi on a Linux Host.
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:maxdepth: 2
:caption: Desktop Platforms

desktop-macos
desktop-linux
desktop-windows