O
About
ONNX Runtime (ORT) is an open-source, cross-platform inference and training engine built by Microsoft that dramatically speeds up machine learning workloads. It is designed to integrate seamlessly into existing technology stacks with minimal friction, supporting languages such as Python, C#, C++, Java, JavaScript, and Rust. ONNX Runtime runs on virtually every major platform—Linux, Windows, macOS, iOS, Android, and even web browsers—and optimizes performance across CPU, GPU, and NPU hardware targets, improving latency, throughput, memory utilization, and binary size. For inference, ONNX Runtime powers AI features in Microsoft's own products including Windows, Office, Azure Cognitive Services, and Bing, and is used in thousands of third-party applications worldwide. It supports ONNX Runtime Web for running models directly in browsers, and ONNX Runtime Mobile for on-device AI in Android and iOS apps. For training, ORT reduces costs associated with large model training and supports popular Hugging Face models such as Llama-2-7b. It also enables on-device training, allowing developers to fine-tune models locally for personalized, privacy-respecting experiences. With built-in support for Generative AI and Large Language Models (LLMs), ONNX Runtime makes it straightforward to integrate state-of-the-art image synthesis and text generation capabilities into any application. It is widely adopted by developers, researchers, and enterprises building production AI systems.
Key Features
- Cross-Platform Inference: Run ML models on Linux, Windows, macOS, iOS, Android, and web browsers with a single consistent API.
- Multi-Language Support: Native bindings for Python, C#, C++, Java, JavaScript, and Rust, making integration easy in any tech stack.
- Hardware Acceleration: Automatically optimizes performance across CPU, GPU, and NPU hardware targets, improving latency, throughput, and memory usage.
- Generative AI & LLM Support: Run and deploy large language models and generative AI workloads—including Llama-2-7b and other Hugging Face models—using onnxruntime-genai.
- On-Device & Large Model Training: Accelerate large model training in the cloud and enable on-device fine-tuning for personalized, privacy-respecting AI experiences.
Use Cases
- Deploying trained ML models in production applications across cloud, edge, and mobile environments with optimized inference performance.
- Running large language models and generative AI (image synthesis, text generation) on-device or in web browsers without server dependencies.
- Accelerating Hugging Face model training and fine-tuning for NLP, vision, and multimodal tasks in research and enterprise pipelines.
- Building cross-platform AI-powered mobile apps for Android and iOS using ONNX Runtime Mobile.
- Integrating ML inference into enterprise software using C#, Java, or JavaScript bindings without switching from existing technology stacks.
Pros
- Broad Platform & Language Coverage: Supports virtually every major OS, device type, and programming language, minimizing friction when integrating into diverse tech stacks.
- Production-Proven at Scale: Powers AI in Microsoft's flagship products (Windows, Office, Azure, Bing) and thousands of other enterprise applications worldwide.
- Open Source & Free: Fully open-source under Microsoft stewardship, with no licensing costs and an active community contributing improvements.
- Strong LLM & GenAI Ecosystem: onnxruntime-genai package provides first-class support for modern generative AI and large language models.
Cons
- Model Conversion Required: Models must be converted to the ONNX format before use, which can add complexity for frameworks with limited ONNX export support.
- Steep Learning Curve for Advanced Optimization: While basic inference is simple, leveraging advanced performance tuning and custom execution providers requires deep ML infrastructure knowledge.
- Not a Training Framework: ORT training is designed to accelerate existing training workflows rather than replace full-featured frameworks like PyTorch or TensorFlow.
Frequently Asked Questions
What is ONNX Runtime?
ONNX Runtime is an open-source, cross-platform engine developed by Microsoft for accelerating machine learning inference and training. It supports ONNX-format models and integrates with many languages and platforms.
How do I install ONNX Runtime?
You can install ONNX Runtime via pip: `pip install onnxruntime` for inference, or `pip install onnxruntime-genai` for generative AI support. Other language bindings are available via NuGet, npm, Maven, and more.
Which hardware does ONNX Runtime support?
ONNX Runtime supports CPU, GPU (via CUDA, DirectML, and others), and NPU acceleration. It automatically selects the best available execution provider for your hardware.
Can I run large language models with ONNX Runtime?
Yes. The onnxruntime-genai package enables running LLMs like Llama-2-7b and other Hugging Face models efficiently across supported platforms, including on-device and in-browser scenarios.
Is ONNX Runtime free to use?
Yes. ONNX Runtime is fully open-source and free to use, licensed by Microsoft under the MIT License. It is available on GitHub and actively maintained by Microsoft and the open-source community.
