// local-llm · alibaba/MNN

MNN

MNN est un projet de LLM locaux open source, créé par alibaba (alibaba/MNN sur GitHub). Il compte 16 192 étoiles sur GitHub et 2 467 forks, il est publié sous la licence Apache-2.0 et a été mis à jour pour la dernière fois sur GitHub le 8 octobre 2026.

Alibaba's lightweight, blazing-fast inference engine for on-device LLMs and edge AI.

★ 16,2 k étoiles⑂ 2,5 k forksApache-2.0mis à jour oct. 2026
▦ visuel — le README de ce projet ne contient aucune capture intégrable,
voici donc un emplacement réservé. Le guide ci-dessous est complet malgré tout.
// détails du projet

Détails du projet

Étoiles GitHub
16 192
Forks
2 467
Licence
Apache-2.0
Dernière mise à jour sur GitHub
8 octobre 2026
Catégorie
LLM locaux
Type de fiche
Guide d'installation complet
// fonctionnement

Comment ça marche

  1. Pick your target device and model

    Decide whether you are targeting Android, iOS, embedded Linux or desktop, and choose a model to convert, such as a Qwen-series LLM for on-device chat.

  2. Clone the engine

    Get the MNN source, which includes the converter, runtime and sample apps including the MNN LLM chat apps.

  3. Convert and build

    Convert your TensorFlow, ONNX, Caffe or TorchScript model to MNN format and build the library for your platform, choosing CPU or GPU backends.

  4. Run inference on-device

    Integrate the runtime into your app or run the included chat app, then tune quantization (FP16/INT8) for size and speed.

// fonctionnalités

Pourquoi on l'adore

Lightweight on-device runtime

Optimized for devices with no dependencies: the Android core library is about 800KB, and FP16/INT8 quantization can cut model size by 50-70%.

Broad model conversion

The converter supports models from TensorFlow, Caffe, ONNX and TorchScript, covering CNN, RNN, GAN and Transformer networks.

Many acceleration backends

Runs on ARM and x64 CPUs with optimized assembly, plus Metal, OpenCL and Vulkan on mobile GPUs, CUDA on NVIDIA, and a Hexagon DSP backend.

On-device LLM support

Powers local LLM apps, with recent releases adding support for Qwen3 and Qwen3-VL series models and multimodal chat on Android and iOS.

Python API

An MNN Python API lets ML engineers run inference, training and image processing without writing C++.

Training as well as inference

MNN supports training models on PC or mobile in addition to inference, plus a lightweight OpenCV-like image processing module.

Advertisement
// lancez-le

Lancez-le vous-même

  1. Clone the repository

    Clone the MNN source, including the converter, runtimes and sample apps.

    git clone https://github.com/alibaba/MNN.git
    cd MNN
  2. Convert a model

    Convert an ONNX, TensorFlow, Caffe or TorchScript model into MNN format with the included converter before deploying it.

    # Convert your model with the MNN converter - see README for the exact command
  3. Build and run on your platform

    Build MNN for Android, iOS or desktop following the platform guide, or open one of the included LLM chat sample apps.

    # Build for your target platform - see README for the exact command
// cas d'usage

Ce que vous pouvez créer

Advertisement
// faq

Questions fréquentes

Who maintains MNN?

MNN is developed by Alibaba and the open-source community; it is battle-tested in Alibaba's own products.

Is MNN free to use?

Yes. It is released under the Apache-2.0 license, which allows commercial use.

Does MNN need a paid API key?

No. MNN runs models locally on your device, so there is no cloud API or key involved.

How does MNN compare with TensorFlow Lite or ONNX Runtime?

MNN is a lightweight C++ engine focused on fast on-device inference, with its own converter for TensorFlow, Caffe, ONNX and TorchScript models and strong mobile GPU and LLM support; the best choice depends on your model and target hardware.

How do I get started?

Clone the repo, convert a model with the MNN converter, and try one of the included sample apps, such as the Android or iOS LLM chat app.

// continuez l'exploration

Outils similaires

LLM locaux

nobodywho

Lightweight inference engine for running LLMs efficiently on any device.

★ 1,5 k étoilesEUPL-1.2
Voir le guide →
LLM locaux

vLLM

A high-throughput and memory-efficient inference and serving engine for LLMs

★ 93,1 k étoilesApache-2.0
Voir le guide →
LLM locaux

airunner

Offline inference engine for art.

★ 1,3 k étoilesGPL-3.0
Voir le guide →
LLM locaux

webclaw

Fast Rust scraper that turns web pages into clean data for LLMs.

★ 2,4 k étoilesAGPL-3.0
Voir le guide →
LLM locaux

mlc-llm

Compile and run LLMs anywhere - phones, laptops, browsers and GPUs - with one engine

★ 23,2 k étoilesApache-2.0
Voir le guide →
LLM locaux

omlx

A fast LLM inference server for Apple Silicon, managed from the macOS menu bar

★ 22,6 k étoilesApache-2.0
Voir le guide →
// alternatives

Alternatives en LLM locaux

Les autres projets de la catégorie LLM locaux avec le plus d'étoiles du catalogue LibreAI — la sélection réaliste d'alternatives si MNN ne vous convient pas.

// plus en local-llm

Plus en LLM locaux

Projets de la catégorie LLM locaux proches dans le catalogue, les plus proches en étoiles de MNN — continuez à explorer de part et d'autre.

Obtenir MNN

Gratuit et ouvert : récupérez le code source, mettez une étoile au dépôt et commencez à créer.