What is ONNX Runtime?
Bringing your machine learning models from research to production, or scaling up training, often involves navigating a complex maze of hardware, software, and performance bottlenecks. ONNX Runtime is engineered to simplify this journey, providing a unified, high-performance engine for running and training your models wherever you need them – from massive cloud clusters to edge devices and browsers. It integrates seamlessly into your existing workflow, allowing you to accelerate AI workloads without overhauling your stack.
Key Features Driving Performance and Flexibility
ONNX Runtime offers a robust set of capabilities designed to optimize and streamline your machine learning operations:
🚀 Accelerate Inference and Training: Leverage built-in optimizations and hardware acceleration (CPU, GPU, NPU) to significantly speed up model execution. ONNX Runtime automatically applies techniques like graph optimization to boost performance for both inference tasks and large model training, reducing latency and computational costs.
💻 Run Anywhere: Develop using your preferred language (Python, C++, C#, Java, JavaScript, Rust, and more) and deploy consistently across diverse platforms including Linux, Windows, macOS, iOS, Android, and even directly in web browsers via ONNX Runtime Web.
🧩 Integrate Seamlessly: Work with models from popular deep learning frameworks like PyTorch and TensorFlow/Keras, as well as traditional ML libraries such as scikit-learn, LightGBM, and XGBoost. Convert your existing models to the ONNX format and run them efficiently using the runtime.
💡 Power Generative AI: Integrate cutting-edge Generative AI and Large Language Models (LLMs) like Llama-2 into your applications. ONNX Runtime provides the performance needed for demanding tasks like image synthesis and text generation across various platforms.
📈 Optimize Training Workloads: Reduce the time and cost associated with training large models, including popular Hugging Face transformers. For PyTorch users, accelerating training can be as simple as adding a single line of code. It also enables on-device training for more personalized and privacy-preserving user experiences.
How Developers Use ONNX Runtime
Deploying a Computer Vision Model: You've trained an object detection model in PyTorch. To serve it efficiently via a web API running on Linux servers and also embed it directly into an Android application for offline use, you convert the model to ONNX format. You then use ONNX Runtime on your backend servers for low-latency inference and ONNX Runtime Mobile within the Android app, ensuring consistent behavior and optimized performance on both platforms without rewriting the core logic.
Speeding Up NLP Inference: Your customer support chatbot uses a transformer model for intent recognition. As user traffic grows, inference latency becomes an issue. By deploying the model with ONNX Runtime configured to utilize available GPU resources, you significantly reduce response times, improving the user experience and lowering the computational load per query.
Accelerating Large Model Training: Your team needs to fine-tune a large language model like Llama-2 on a multi-GPU cluster. Instead of complex manual optimizations, you integrate ONNX Runtime Training with your existing PyTorch training script. This accelerates the training process considerably, allowing for faster iteration and reduced computational expense.
Get Optimized Performance with Less Effort
ONNX Runtime acts as a versatile accelerator for your machine learning workloads. It tackles the challenges of deploying and training models across diverse environments by providing a consistent, high-performance execution layer. By supporting your existing tools and targeting a wide range of hardware and platforms, it allows you to focus more on building innovative AI-powered applications and less on the complexities of optimization and deployment. Trusted by companies like Microsoft, Adobe, SAS, and NVIDIA, it's a production-ready solution for demanding AI tasks.
More information on ONNX Runtime
Launched
2019-10
Pricing Model
Free
Starting Price
Global Rank
269675
Follow
Month Visit
155K
Tech used
Google Analytics,Google Tag Manager,Fastly,GitHub Pages,Gzip,OpenGraph,Varnish
Top 5 Countries
23.78%
12.27%
6.44%
6.02%
3.96%
China
United States
Russian Federation
Taiwan, Province of China
Hong Kong
Traffic Sources
48.96%
37.55%
11.3%
1.76%
0.35%
0.07%
Search
Direct
Referrals
Social
Paid Referrals
Mail
ONNX Runtime was manually vetted by our editorial team and was first featured on 2025-04-25.
ONNX Runtime Alternatives
Load more Alternatives-
Phi-3 Mini is a lightweight, state-of-the-art open model built upon datasets used for Phi-2 - synthetic data and filtered websites - with a focus on very high-quality, reasoning dense data.
-
Ray is the AI Compute Engine. It powers the world's top AI platforms, supports all AI/ML workloads, scales from laptop to thousands of GPUs, and is Python - native. Unlock AI potential with Ray!
-
Run ML models with Carton - decouples ML frameworks, low overhead, platform support. Fast experimentation, deployment flexibility, custom ops, in-browser ML.
-
Cortex is an OpenAI-compatible AI engine that developers can use to build LLM apps. It is packaged with a Docker-inspired command-line interface and client libraries. It can be used as a standalone server or imported as a library.
-
Revolutionize your AI infrastructure with Run:ai. Streamline workflows, optimize resources, and drive innovation. Book a demo to see how Run:ai enhances efficiency and maximizes ROI for your AI projects.