MegEngine vs ONNX Runtime
MegEngine
megengine.org.cnONNX Runtime
onnxruntime.aiSide by side
| Figure | MegEngine | ONNX Runtime |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | — | Yes |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| iOS | Yes | Yes |
| Android | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · Deep Learning Software | 7.9 (#15) | 8.4 (#6) |
| Integrations | MegFile provides Python file interfaces for S3, HTTP and local files. | The ecosystem documentation lists integrations with Azure Machine Learning, Azure Custom Vision, Azure SQL Edge, Azure Synapse Analytics, ML.NET, and NVIDIA Triton Inference Server. |
| Purpose | MegEngine is a fast, scalable deep learning framework with automatic differentiation. | ONNX Runtime is a cross-platform machine-learning model accelerator with interfaces for hardware-specific libraries. |
| Security guidance | MegEngine advises users to check environment, model, data and privacy risks and recommends sandboxing models from other sources. | The documentation warns that models from untrusted sources may consume excessive memory or compute resources and recommends inspection and safe testing. |
| Support | The project lists GitHub issues, a forum, QQ group and [email protected] for contact. | Documentation questions are directed to issue filing, and the project invites users to report bugs, suggest features, and submit pull requests on GitHub. |
Plans
In their own words
MegEngine
MegEngine is a free, open-source deep learning framework for developing and deploying models, with automatic differentiation. It uses one model for training and inference, including quantization and dynamic shapes. The project says enabling DTR can reduce GPU memory use to one-third of the original. Inference is supported across x86, Arm, CUDA, and ROCm; GPU use requires compatible device drivers. Python packages are listed for 64-bit Linux and Windows, macOS 10.14 or later, and Android 7 or later, though macOS and Android installations are CPU-only. The installation guide specifies Python 3.6–3.9. MegEngine Lite supplies C/C++, Rust, and Python runtimes for deployment. MgeConvert handles conversion between MegEngine and third-party model formats, MegFile provides Python file interfaces for S3, HTTP, and local files, and MegFlow is a streaming computation framework for AI applications. Tutorials address beginners and advanced developers. Support options include GitHub issues, a forum, a QQ group, and email.
ONNX Runtime
ONNX Runtime is a free engine for machine-learning inference and training within existing technology stacks. It can run models from frameworks including PyTorch, TensorFlow/Keras, TFLite, and scikit-learn. For inference, it optimizes latency, throughput, memory use, and binary size through graph transformations, accelerator-aware partitioning, and optimized computation kernels. Its Execution Providers connect ONNX models to hardware acceleration libraries for CPUs, GPUs, FPGAs, and specialized NPUs. Listed options include NVIDIA CUDA and TensorRT, Intel OpenVINO, DirectML, Qualcomm QNN, Android NNAPI, Apple CoreML, and WebGPU. Deployment spans cloud servers, edge and mobile devices, and browsers; its web runtime supports browser models and its mobile runtime supports Android and iOS. Supported languages include Python, C#, C++, Java, JavaScript, and Rust. Developers can create smaller web or mobile packages by including only the operators and opsets their models need. The open-source runtime is available on Android, iOS, Linux, macOS, self-hosted environments, web, and Windows.