MNN
Blazing-fast, lightweight inference engine for high-performance on-device LLMs and Edge AI
GraphCanon updated 2w · GitHub synced 2w
Decision brief
MNN is a highly efficient and lightweight deep learning framework designed for high-performance inference on-device. Developed by Alibaba, it supports various applications across multiple Alibaba platforms.
Good fit when
- - When you need lightning-fast and low-memory usage performance on mobile devices or edge computing environments.
- - If your application needs to run locally without cloud dependency, maximizing privacy and reducing latency.
Avoid when
- - If your primary requirement is training deep learning models, since MNN mainly focuses on fast and lightweight inference rather than heavy-duty training tasks.
- - For applications requiring significant external data access or continuous cloud updates, as MNN emphasizes local processing.
- Requirements:
- Min 2 GB RAM
Observed Jul 11, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of 2w
- Provenance
- Not a fork · Organization account
- As of 2w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Backing
Company context for Alibaba. Display-only - separate from trust and ranking.
- Company
- Alibaba·GitHub org profile·1mo
- Commercial model
- Pure OSS·GitHub org profile (public repos)·1mo
Install
git clone https://github.com/alibaba/MNNSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
MNN is a powerful and efficient inference engine designed for deploying deep learning models at the edge, facilitating real-time performance on various devices.
Capability facts
- Languages
- c++
Source: github.language · Aug 7, 2026
Categories
Graph entities
Tags
README
News 🔥
- [2026/07/22] MNN 3.6.1 is released with a new 🚀 Hexagon backend, enabling accelerated model inference on Qualcomm Hexagon DSPs. Learn more.
History News
- [2026/03/05] Support Qwen3.5 Series.
- [2026/02/13] MNN-Sana-Edit-V2 is now available at apps, offering cartoon-style photo editing based on Sana.
- [2025/10/16] Support Qwen3-VL Series.
- [2025/06/11] New App MNN TaoAvatar released, you can talk with 3DAvatar offline with LLM, ASR, TTS, A2BS and NNR models all run local on your device!! MNN TaoAvatar
- [2025/05/12] android app support qwen2.5 omni 3b and 7b MNN Chat App.
- [2025/04/30] android app support qwen3 and dark mode MNN Chat App.
- [2025/02/18] iOS multimodal LLM App is released MNN LLM iOS.
- [2025/02/11] android app support for deepseek r1 1.5b.
- [2025/01/23] We released our full multimodal LLM Android App:MNN-LLM-Android. including text-to-text, image-to-text, audio-to-text, and text-to-image generation.
Intro
MNN is a highly efficient and lightweight deep learning framework. It supports inference and training of deep learning models and has industry-leading performance for inference and training on-device. At present, MNN has been integrated into more than 30 apps of Alibaba Inc, such as Taobao, Tmall, Youku, DingTalk, Xianyu, etc., covering more than 70 usage scenarios such as live broadcast, short video capture, search recommendation, product searching by image, interactive marketing, equity distribution, security risk control. In addition, MNN is also used on embedded devices, such as IoT.
MNN-LLM is a large language model runtime solution developed based on the MNN engine. The mission of this project
For agents
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