ONNX Runtime
ONNX Runtime is a cross-platform inference engine developed by Microsoft that executes machine learning models in the Open Neural Network Exchange (ONNX) format.
Publisher review
ONNX Runtime is a cross-platform inference engine developed by Microsoft that executes machine learning models in the Open Neural Network Exchange (ONNX) format. It targets developers and data scientists who need to deploy trained models across diverse environments—from cloud servers running Linux, Windows, or Mac to edge devices on ARM, Intel, or mobile hardware. By supporting ONNX 1.2 and higher, including the ONNX-ML profile for classical machine learning pipelines, the runtime accepts models from frameworks like PyTorch, TensorFlow, and scikit-learn after conversion. It is designed for production use, powering features in Microsoft products such as Windows ML and ML.NET, and is available as an open-source project on GitHub under an MIT license.
The engine achieves performance gains through a modular architecture that separates model parsing, graph optimizations, and execution. It provides a set of built-in optimizers—such as constant folding, operator fusion, and quantization—that can deliver average 2x speed improvements over baseline ONNX execution. For hardware acceleration, ONNX Runtime uses execution providers: on Android it taps NNAPI for GPU/NPU offloading, on iOS it leverages Core ML, and on desktop/server it supports CUDA, DirectML, and TensorRT. Recent benchmarks show that with Arm KleidiAI integration, inference on Arm-based PCs and mobile devices can be up to 2.6x faster. The runtime also supports on-device AI scenarios, including running large language models like DeepSeek R1 with up to 6.3x speed improvements over PyTorch, and enables WebGPU acceleration in browsers for tasks like background removal, achieving 20x speedup over multi-threaded CPU.
In the inference engine landscape, ONNX Runtime competes directly with NVIDIA TensorRT, Intel OpenVINO, Google TensorFlow XLA, and LLVM MLIR. While TensorRT often leads on NVIDIA GPUs (e.g., 2.2 ms per inference vs. ONNX Runtime's 22.6 ms on CPU for a specific model), ONNX Runtime's strength is its hardware-agnostic design: it can run on CPU, GPU, NPU, and mobile accelerators without vendor lock-in. The arXiv benchmark paper (2210.04323) notes that no single framework dominates across all configurations, and ONNX Runtime's flexibility makes it a strong choice for heterogeneous deployments. Its open ecosystem and integration with Olive optimization tools further differentiate it from more specialized alternatives like TensorRT or OpenVINO.
Honest trade-offs include a setup process that can be complex due to model conversion and optimization steps, especially when targeting multiple execution providers. Performance can lag behind specialized engines on specific hardware—for example, ONNX Runtime on CPU measured 22.6 ms per inference versus TensorRT's 2.2 ms on GPU in one benchmark. Its footprint is slightly larger than purpose-built mobile runtimes like MediaPipe, and users report a learning curve when tuning execution providers and graph optimizations. Additionally, for large batch sizes, inference can become slower than PyTorch in some scenarios, as noted in community testing. Despite these limitations, ONNX Runtime remains a versatile, production-tested choice for cross-platform AI inference.
How it works
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ONNX model support
Executes models in ONNX format, including ONNX 1.2+ and ONNX-ML profile for classical ML pipelines.
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Cross-platform compatibility
Runs on Linux, Windows, and Mac, with mobile support for Android (NNAPI) and iOS (Core ML).
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Hardware acceleration providers
Leverages CUDA, DirectML, TensorRT, NNAPI, Core ML, and Arm KleidiAI for GPU/NPU/CPU acceleration.
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Graph optimizations
Applies constant folding, operator fusion, and quantization to achieve average 2x speed improvements.
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On-device AI support
Optimized for ARM, Intel, and mobile hardware, enabling local inference with low latency.
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Open source ecosystem
Available on GitHub under MIT license, with integration into Windows ML, ML.NET, and Olive optimization tools.
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WebGPU and WebAssembly
Supports browser-based inference with WebGPU, achieving 20x speedup over CPU for background removal.
Strengths and trade-offs
Strengths
- Supports a wide range of ONNX-compatible models from PyTorch, TensorFlow, and scikit-learn without vendor lock-in.
- Delivers up to 2.6x faster inference on Arm-based devices through KleidiAI integration.
- Provides cross-platform consistency across cloud, desktop, mobile, and browser environments.
- Enables on-device AI with optimizations for ARM, Intel, and mobile hardware, including NPU support.
Trade-offs
- Setup can be complex due to model conversion and optimization steps required for different execution providers.
- Performance can be slower than specialized engines like TensorRT on GPU (22.6 ms vs. 2.2 ms per inference in one benchmark).
- Slightly larger footprint compared to purpose-built mobile runtimes like MediaPipe.
- Learning curve for users unfamiliar with ONNX conversion and execution provider configuration.
Pricing context
Open source (MIT license), free to use with no paid tiers.
Getting started with ONNX Runtime
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Install ONNX Runtime
Install the ONNX Runtime package for your platform using pip: pip install onnxruntime. For GPU support, install onnxruntime-gpu. Alternatively, download the pre-built binaries from the GitHub releases page for Linux, Windows, or Mac.
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Convert model to ONNX
Export your trained model from PyTorch, TensorFlow, or scikit-learn to ONNX format using the framework's export utilities. For example, in PyTorch use torch.onnx.export() with a sample input tensor to create a .onnx file.
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Configure execution provider
Choose a hardware accelerator by setting the execution provider. For CUDA, create an InferenceSession with providers=['CUDAExecutionProvider']. For CPU, use ['CPUExecutionProvider']. Refer to the documentation for DirectML, TensorRT, or NNAPI options.
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Run inference on model
Load the ONNX model into an InferenceSession and run inference by calling session.run() with your input data. Specify the output names and pass a dictionary of input tensors. The runtime applies graph optimizations automatically.
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Optimize for production
Apply quantization or operator fusion using ONNX Runtime's built-in optimizers or the Olive optimization tool. Benchmark performance across execution providers and adjust settings to balance latency and throughput for your deployment environment.
Frequently Asked Questions
What is ONNX Runtime and what does it do?
ONNX Runtime is a cross-platform inference engine from Microsoft that runs machine learning models in the ONNX format. It supports models from PyTorch, TensorFlow, and scikit-learn after conversion, and is designed for production use on cloud servers, edge devices, and mobile hardware.
How does ONNX Runtime achieve better performance?
ONNX Runtime uses a modular architecture with built-in optimizers like constant folding, operator fusion, and quantization. These optimizations can deliver average 2x speed improvements over baseline ONNX execution. It also leverages hardware acceleration providers such as CUDA, DirectML, and TensorRT for additional speed.
What hardware acceleration does ONNX Runtime support?
ONNX Runtime supports multiple execution providers: CUDA and TensorRT for NVIDIA GPUs, DirectML for Windows, NNAPI for Android, Core ML for iOS, and Arm KleidiAI for Arm-based devices. This allows it to accelerate inference on GPU, NPU, and CPU across different platforms.
How does ONNX Runtime compare to TensorRT?
TensorRT often leads on NVIDIA GPUs, achieving 2.2 ms per inference versus ONNX Runtime's 22.6 ms on CPU for one benchmark. However, ONNX Runtime is hardware-agnostic and runs on CPU, GPU, NPU, and mobile accelerators without vendor lock-in, making it flexible for heterogeneous deployments.
What are the trade-offs of using ONNX Runtime?
Setup can be complex due to model conversion and optimization steps for different execution providers. Performance may lag behind specialized engines on specific hardware, and its footprint is slightly larger than purpose-built mobile runtimes. Users also face a learning curve when tuning providers and optimizations.
Is ONNX Runtime free to use?
Yes, ONNX Runtime is open source under the MIT license and free to use with no paid tiers. It is available on GitHub and integrates with Microsoft products like Windows ML and ML.NET, as well as Olive optimization tools for further performance tuning.
Alternatives
How ONNX Runtime compares
Direct head-to-head against 2 competitors. Picked by 7wData.
ONNX Runtime
- Pricing
- Open source (MIT license), free to use with no paid tiers.
- Target
- ONNX Runtime is a cross-platform inference engine developed by Microsoft that executes machine learning models in the Open Neural Network Exchange (ONNX) format.
- Strength
- Supports a wide range of ONNX-compatible models from PyTorch, TensorFlow, and scikit-learn without vendor lock-in.
- Watch for
- Setup can be complex due to model conversion and optimization steps required for different execution providers.
TensorRT
- Pricing
- Free (NVIDIA hardware required)
- Target
- NVIDIA GPU-optimized inference
- Deployment
- CUDA-only
- Strength
- Hardware-specific optimizations for NVIDIA GPUs
- Watch for
- Vendor lock-in to NVIDIA ecosystem
PyTorch
- Pricing
- Free open-source
- Target
- Full ML development lifecycle
- Deployment
- Multi-platform
- Strength
- Native model execution without conversion
- Watch for
- Larger runtime footprint than optimized inference engines
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