MNN VS serving

Compare MNN vs serving and see what are their differences.


MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba (by alibaba)


A flexible, high-performance serving system for machine learning models (by tensorflow)
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MNN serving
3 12
8,180 6,055
1.3% 0.5%
8.2 9.7
4 days ago 2 days ago
C++ C++
- Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.


Posts with mentions or reviews of MNN. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-10-03.


Posts with mentions or reviews of serving. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-06-12.
  • Llama.cpp: Full CUDA GPU Acceleration
    14 projects | | 12 Jun 2023
    Yet another TEDIOUS BATTLE: Python vs. C++/C stack.

    This project gained popularity due to the HIGH DEMAND for running large models with 1B+ parameters, like `llama`. Python dominates the interface and training ecosystem, but prior to llama.cpp, non-ML professionals showed little interest in a fast C++ interface library. While existing solutions like tensorflow-serving [1] in C++ were sufficiently fast with GPU support, llama.cpp took the initiative to optimize for CPU and trim unnecessary code, essentially code-golfing and sacrificing some algorithm correctness for improved performance, which isn't favored by "ML research".

    NOTE: In my opinion, a true pioneer was DarkNet, which implemented the YOLO model series and significantly outperformed others [2]. Same trick basically like llama.cpp


  • Would you use maturin for ML model serving?
    2 projects | /r/rust | 8 Jul 2022
    Which ML framework do you use? Tensorflow has You could also use the Rust bindings to load a saved model and expose it using one of the Rust HTTP servers. It doesn't matter whether you trained your model in Python as long as you export its saved model.
  • Popular Machine Learning Deployment Tools
    4 projects | | 16 Apr 2022
  • If data science uses a lot of computational power, then why is python the most used programming language?
    6 projects | /r/learnmachinelearning | 13 Apr 2022
    You serve models via which is written entirely in C++ (, no Python on the serving path or in the shipped product.
  • Exposing Tensorflow Serving’s gRPC Endpoints on Amazon EKS
    2 projects | | 10 Feb 2021
    gRPC only connects to a host and port — but we can use whatever service route we want. Above I use the path we configured in our k8s ingress object: /service1, and overwrite the base configuration provided by tensorflow serving. When we call the tfserving_metadata function above, we specify /service1 as an argument.

What are some alternatives?

When comparing MNN and serving you can also consider the following projects:

tensorflow - An Open Source Machine Learning Framework for Everyone

ML-examples - Arm Machine Learning tutorials and examples

TNN - TNN: developed by Tencent Youtu Lab and Guangying Lab, a uniform deep learning inference framework for mobile、desktop and server. TNN is distinguished by several outstanding features, including its cross-platform capability, high performance, model compression and code pruning. Based on ncnn and Rapidnet, TNN further strengthens the support and performance optimization for mobile devices, and also draws on the advantages of good extensibility and high performance from existed open source efforts. TNN has been deployed in multiple Apps from Tencent, such as Mobile QQ, Weishi, Pitu, etc. Contributions are welcome to work in collaborative with us and make TNN a better framework.

ncnn - ncnn is a high-performance neural network inference framework optimized for the mobile platform

oneflow - OneFlow is a deep learning framework designed to be user-friendly, scalable and efficient.

server - The Triton Inference Server provides an optimized cloud and edge inferencing solution.

flashlight - A C++ standalone library for machine learning

XLA.jl - Julia on TPUs

glow - Compiler for Neural Network hardware accelerators

OpenMLDB - OpenMLDB is an open-source machine learning database that provides a feature platform computing consistent features for training and inference.

julia - The Julia Programming Language

runtime - A performant and modular runtime for TensorFlow