TNN
A cross-platform deep learning inference framework for diverse computing environments, from mobile to desktop and server.
GraphCanon updated Oct 3, 2026 · GitHub synced Oct 3, 2026
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Decision brief
Developed by Tencent Labs, TNN offers strong cross-platform performance with efficient model compression and runtime optimization for mobile to server use.
Good fit when
- When developing AI apps for Tencent-affiliated software like Mobile QQ or Weishi
- For projects requiring high-performance inference across ARM devices with extensive acceleration solutions support
Avoid when
- If you prefer a framework that heavily integrates with TensorFlow's ecosystem, as TNN has a steeper learning curve when converting models
- When your project primarily relies on Python environments. TNN is C++-centric with no native Python interface.
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (511d since push)
- As of Oct 3, 2026
- Provenance
- Not a fork · Organization account
- As of Oct 3, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/Tencent/TNNSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Developed by Tencent's Youtu Lab and Guangying Lab, TNN is an advanced inference tool that supports model conversion, compilation, and runtime across various platforms. It excels in high performance, efficient model compression, and code optimization.
Capability facts
- Deploy
- Self-host
Source: dockerfile:Dockerfile · Oct 3, 2026
- Docker
- Dockerfile present
Source: dockerfile:Dockerfile · Oct 3, 2026
- Languages
- c++
Source: github.language · Oct 3, 2026
Categories
Tags
README
Quick Start It is very simple to use TNN. If you have a trained model, the model can be deployed on the target platform through three steps. 1. Convert the trained model into a TNN model. We provide a wealth of tools to help you complete this step, whether you are using Tensorflo...
For agents
This page has a .md twin and JSON over the API.