pytorch/executorch

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

4953 stars · 1122 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

ExecuTorch is PyTorch's framework for exporting and running AI models on-device across mobile, embedded, and edge hardware, with a tiny (~50KB) runtime and 12+ hardware backends. It supports LLMs, vision, speech, and multimodal models with native PyTorch export and no intermediate format conversions.

Use cases

• run llm inference on a smartphone offline
• deploy pytorch models to mobile without onnx conversion
• run computer vision models on embedded devices
• on-device speech recognition with privacy
• deploy ai models to microcontrollers with tiny runtime
• quantize and optimize models for edge hardware
• run multimodal ai on ar/vr headsets

When to choose

• you need privacy-preserving, offline inference on phones or embedded devices
• your models are already in PyTorch and you want direct export without format conversion
• you need a very small runtime footprint down to microcontroller scale
• you need to target many hardware backends (CPU, GPU, NPU) from one toolchain

When to avoid

• your inference runs server-side with abundant compute and no edge constraints
• your models are in TensorFlow/JAX and you prefer TFLite or ONNX ecosystems
• you need training on device rather than inference
• you want a turnkey app rather than an SDK to integrate into your own code

Facets

library · maturity active

machine-learning llm-inference deep-learning compiler sdk machine-learning deep-learning large-language-models embedded-systems mobile-development computer-vision speech-processing embedded python cpp cross-platform on-device-inference edge-ai pytorch model-export quantization microcontrollers runtime android ios mobile gpu

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markdown · JSON · MCP: product_card(name="pytorch/executorch")