MNN – fast, lightweight deep learning framework (github.com)

MNN, a fast and lightweight deep learning framework, has announced multiple updates and integrations, boosting support for various large language models (LLMs) and applications. Notably, MNN now supports the Qwen3-VL series and introduces the MNN TaoAvatar app, enabling offline conversation with a 3D avatar through advanced models for language processing, speech recognition (ASR), and text-to-speech (TTS) all operating locally. These developments illustrate MNN's commitment to enhancing user experience and performance across mobile and embedded devices. The significance of MNN lies in its efficient design for deploying LLMs and image generation models on personal devices, offering an optimal alternative to heavier frameworks. With support for multiple quantization formats (FP16 and Int8), MNN can reduce model sizes by up to 70%, making it highly accessible for developers. Integration into over 30 Alibaba applications, alongside its role in the Walle System—a pioneering platform for device-cloud collaborative ML—positions MNN as an essential tool in the AI/ML landscape, particularly for mobile deployments and IoT applications.