// local-llm · alibaba/MNN

MNN

MNN é um projeto de LLM local de código aberto de alibaba (alibaba/MNN no GitHub). Tem 16.192 estrelas no GitHub e 2.467 forks, é publicado sob a licença Apache-2.0 e foi atualizado pela última vez no GitHub em 8 de outubro de 2026.

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

★ 16,2 mil estrelas⑂ 2,5 mil forksApache-2.0atualizado out 2026
▦ visual: o README deste projeto não inclui nenhuma captura incorporável,
então aqui vai um marcador. O guia abaixo está completo mesmo assim.
// dados do projeto

Dados do projeto

Repositório
alibaba/MNN
Estrelas no GitHub
16.192
Forks
2.467
Licença
Apache-2.0
Última atualização no GitHub
8 de outubro de 2026
Categoria
LLM local
Tipo de ficha
Guia de instalação completo
// como funciona

Como funciona

  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.

// recursos

Por que as pessoas gostam tanto

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.

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// execute você mesmo

Execute você mesmo

  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
// casos de uso

O que você pode criar

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// perguntas frequentes

Perguntas frequentes

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.

// continue explorando

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// alternativas

Alternativas em LLM local

Os outros projetos de LLM local com mais estrelas no catálogo do LibreAI — a lista realista de alternativas se MNN não for o ideal para você.

// mais em local-llm

Mais em LLM local

Projetos de LLM local próximos no catálogo, os mais próximos em estrelas de MNN: continue explorando dos dois lados dele.

Baixe o MNN

Grátis e aberto: pegue o código-fonte, dê uma estrela no repositório e comece a criar.