{"data":{"slug":"nndeploy-nndeploy","name":"nndeploy","tagline":"一款简单易用和高性能的AI部署框架 | An Easy-to-Use and High-Performance AI Deployment Framework","github_url":"https://github.com/nndeploy/nndeploy","owner":"nndeploy","repo":"nndeploy","owner_avatar_url":"https://avatars.githubusercontent.com/u/147458884?v=4","primary_language":"C++","stars":1847,"forks":226,"topics":["ai","ascend","deep-learning","deployment","diffusers","genai","llm","low-code","low-code-platform","mnn","no-code","onnxruntime","openvino","python","pytorch","tensorrt","transformer","workflow"],"archived":false,"github_pushed_at":"2026-04-25T11:15:25+00:00","maintenance_label":"Steady","url":"https://www.graphcanon.com/tools/nndeploy-nndeploy","markdown_url":"https://www.graphcanon.com/tools/nndeploy-nndeploy.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/nndeploy-nndeploy","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=nndeploy-nndeploy","description":"一款简单易用和高性能的AI部署框架 | An Easy-to-Use and High-Performance AI Deployment Framework","homepage_url":"https://nndeploy-zh.readthedocs.io/zh-cn/latest/","license":"Apache-2.0","open_issues":23,"watchers":32,"ai_summary":null,"readme_excerpt":"[English](README_EN.md) | 简体中文\n\n<h3 align=\"center\">\nnndeploy：一款简单易用和高性能的AI部署框架\n</h3>\n\n<p align=\"center\">\n<a href=\"https://github.com/nndeploy/nndeploy/actions/workflows/linux.yml\">\n  <img src=\"https://github.com/nndeploy/nndeploy/actions/workflows/linux.yml/badge.svg\" alt=\"Linux\" style=\"height: 16px;\">\n</a>\n <a href=\"https://github.com/nndeploy/nndeploy/actions/workflows/windows.yml\">\n  <img src=\"https://github.com/nndeploy/nndeploy/actions/workflows/windows.yml/badge.svg\" alt=\"Windows\" style=\"height: 16px;\">\n</a>\n <a href=\"https://github.com/nndeploy/nndeploy/actions/workflows/android.yml\">\n  <img src=\"https://github.com/nndeploy/nndeploy/actions/workflows/android.yml/badge.svg\" alt=\"Android\" style=\"height: 16px;\">\n</a>\n <a href=\"https://github.com/nndeploy/nndeploy/actions/workflows/macos.yml\">\n  <img src=\"https://github.com/nndeploy/nndeploy/actions/workflows/macos.yml/badge.svg\" alt=\"macOS\" style=\"height: 16px;\">\n</a>\n <a href=\"https://github.com/nndeploy/nndeploy/actions/workflows/ios.yml\">\n  <img src=\"https://github.com/nndeploy/nndeploy/actions/workflows/ios.yml/badge.svg\" alt=\"iOS\" style=\"height: 16px;\">\n</a>\n \n</p>\n\n<p align=\"center\">\n<a href=\"https://nndeploy-zh.readthedocs.io/zh-cn/latest/\"><b>文档</b></a> \n| <a href=\"https://deepwiki.com/nndeploy/nndeploy\"><b>Ask DeepWiki</b></a>\n| <a href=\"docs/zh_cn/knowledge_shared/wechat.md\"><b>微信</b></a> \n| <a href=\"https://discord.gg/9rUwfAaMbr\"><b>Discord</b></a> \n\n\n</p>\n\n<p align=\"center\">\n  <picture>\n    <source media=\"(prefers-color-scheme: dark)\" srcset=\"docs/image/workflow.png\">\n    <img alt=\"nndeploy\" src=\"docs/image/workflow/worflow_llm.gif\" width=100%>\n  </picture>\n</p>\n\n---\n\n## 介绍\n\nnndeploy 是一款简单易用和高性能的 AI 部署框架。解决的是 AI 算法在端侧部署的问题，包含桌面端（Windows、macOS）、移动端（Android、iOS）、边缘计算设备（NVIDIA Jetson、Ascend310B、RK 等）以及单机服务器（RTX 系列、T4、Ascend310P 等），**基于可视化工作流和多端推理，可让 AI 算法在上述平台和硬件更高效、更高性能的落地。**\n\n**针对10B以上的大模型（如大语言模型和 AIGC 生成模型），nndeploy 适合作为一款可视化工作流工具。**\n\n### **简单易用**\n\n- **可视化工作流**：拖拽节点即可部署 AI 算法，参数实时可调，效果一目了然。\n- **自定义节点**：支持 Python/C++自定义节点，无论是用 Python 实现预处理，还是用 C++/CUDA 编写高性能节点，均可无缝集成到与可视化工作流。\n- **一键部署**：工作流支持导出为 JSON，可通过 C++/Python API 调用，适用于 Linux、Windows、macOS、Android 等平台\n\n  <table cellpadding=\"5\" cellspacing=\"0\" border=\"1\">\n  <tr>\n    <td>桌面端搭建AI工作流</td>\n    <td><a href=\"https://github.com/nndeploy/nndeploy/blob/main/app/android/README.md\">移动端部署</a></td>\n  </tr>\n  <tr>\n    <td><img src=\"docs/image/workflow/worflow_segment_rmbg.gif\" width=\"500px\"></td>\n    <td><img src=\"docs/image/android_app/app-seg-result.jpg\" width=\"100px\"></td>\n  </tr>\n  </table>\n\n### **高性能**\n\n- **并行优化**：支持串行、流水线并行、任务并行等执行模式\n- **内存优化**：零拷贝、内存池、内存复用等优化策略\n- **高性能优化**：内置 C++/CUDA/Ascend C/SIMD 等优化实现的节点\n- **多端推理**：一套工作流适配多端推理，深度集成 13 种主流推理框架，全面覆盖云端服务器、桌面应用、移动设备、边缘计算等全平台部署场景。框架支持灵活选择推理引擎，可按需编译减少依赖，同时支持接入自定义推理框架的独立运行模式。\n\n  | 推理框架                                                                         | 状态 |\n  | :------------------------------------------------------------------------------- | :--- |\n  | [ONNXRuntime](https://github.com/microsoft/onnxruntime)                          | ✅    |\n  | [TensorRT](https://github.com/NVIDIA/TensorRT)                                   | ✅    |\n  | [OpenVINO](https://github.com/openvinotoolkit/openvino)                          | ✅    |\n  | [MNN](https://github.com/alibaba/MNN)                                            | ✅    |\n  | [TNN](https://github.com/Tencent/TNN)                                            | ✅    |\n  | [ncnn](https://github.com/Tencent/ncnn)                                          | ✅    |\n  | [CoreML](https://github.com/apple/coremltools)                                   | ✅    |\n  | [AscendCL](https://www.hiascend.com/zh/)                                         | ✅    |\n  | [RKNN](https://www.rock-chips.com/a/cn/downloadcenter/BriefDatasheet/index.html) | ✅    |\n  | [SNPE](https://developer.qualcomm.com/software/qualcomm-neural-processing-sdk)   | ✅    |\n  | [TVM](https://github.com/apache/tvm)                                             | ✅","github_created_at":"2023-08-08T13:13:25+00:00","created_at":"2026-07-15T10:49:42.972589+00:00","updated_at":"2026-07-15T10:49:46.241048+00:00","categories":[{"slug":"inference-serving","name":"Inference & Serving","url":"https://www.graphcanon.com/categories/inference-serving","markdown_url":"https://www.graphcanon.com/categories/inference-serving.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/inference-serving"},{"slug":"llm-frameworks","name":"LLM 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