// local-llm · alibaba/MNN

MNN

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

★ 16.2k stars⑂ 2.5k forksApache-2.0updated 2026-10
▦ visual — this project's README ships no embeddable screenshot,
so here's a placeholder. The guide below is complete regardless.
// project facts

Project facts

MNN is an open-source local llm project by alibaba (alibaba/MNN on GitHub). It has 16,192 GitHub stars and 2,467 forks, is released under the Apache-2.0 license, and was last updated on GitHub on 8 October 2026.

Repository
alibaba/MNN
GitHub stars
16,192
Forks
2,467
License
Apache-2.0
Last updated on GitHub
8 October 2026
Category
Local LLM
Listing
Full install guide
// how it works

How it works

  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.

// features

Why people love it

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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// run it

Run it yourself

  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
// use cases

What you can build

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// faq

Questions, answered

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.

// keep exploring

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// alternatives

Alternatives in Local LLM

The most-starred other Local LLM projects in the LibreAI catalogue — the realistic shortlist if MNN is not the right fit.

Get MNN

Free and open — grab the source, star the repo, and start creating.