executorch

LFM3-1.7B and LFM3-VL-2B now run fully offline on iPhone and Android via the open-source Private Mind app, with detailed notes on what it took to export LFM3's novel architecture (gated convolutions, hybrid attention, sliding-window cache tweaks) to ExecuTorch, achieving ~40 tok/s on an iPhone 16 Pro GPU.

ExecuTorch 已成为 Zephyr RTOS 的官方外部模块，使开发者能通过固件构建配置变更轻松在微控制器等边缘设备上部署 PyTorch 模型；演示中量化 MobileNetV2 在 Alif Ensemble E8 DevKit 上以 28ms 完成推理，卷积图运行于 Arm Ethos-U55 NPU。

PyTorch 的 ExecuTorch 新增 Arm Cortex-M 后端，量化模型可直接编译到 CMSIS-NN 内核，核心运行时低于 20 KiB flash，并在 MLPerf Tiny 基准上实现低延迟与低内存占用，使开发者无需离开 PyTorch 生态即可在微控制器上部署模型。

The ExecuTorch Hackathon is a two-day event in San Francisco where developers form teams to build and deploy PyTorch models on edge hardware using the ExecuTorch framework, with tracks for compute, mobile+XR, and IoT, sponsored by Meta, Qualcomm, and others.

A two-day hackathon at San Francisco State University, organized by Meta and Arm, focused on building AI applications on the edge using ExecuTorch on Arm-powered devices, with technical talks, workshops, and prizes.

React Native ExecuTorch v0.10 achieves significant speedups, up to 92x for AI models like FastSAM, with TypeScript pipelines for better inspectability and cross-platform support.

Meta introduced Muse Glimmer, an open-weight 30B-parameter model distilled from Muse Spark for on-device agentic workflows, with ExecuTorch now supporting running it on NVIDIA GPUs and Apple silicon.

The PyTorch Foundation supported the ExecuTorch Hackathon in San Francisco, where over 100 participants built real-time on-device AI applications using PyTorch and ExecuTorch on Snapdragon-powered Samsung Galaxy S25 Ultra devices. Winning projects included SafeScreen AI, SixthSense, and Toddle AI, showcasing local execution benefits for responsiveness, privacy, and offline capability.

The react-native-executorch library now integrates Google's Gemma 4 model, enabling fully offline, GPU-accelerated inference in React Native apps using Vulkan on Android and MLX on Apple Silicon.

ExecuTorch now has an MLX delegate that enables GPU-accelerated inference for PyTorch models on Apple Silicon Macs, supporting LLMs, speech-to-text, and MoE models with quantization via TorchAO.

ExecuTorch, PyTorch's on-device AI deployment framework, won the Best Industry Paper Award at MLSysConf 2026. The paper introduces a unified solution for running models on diverse hardware, from microcontrollers to SoCs.

This article introduces ExecuTorch, a unified PyTorch-native deployment framework designed to run AI models on diverse edge devices without requiring model conversion or reimplementation.