
A cross-platform machine learning engine for high-performance model inference.
Freemium

ONNX Runtime is a production-grade AI engine engineered to solve the common bottleneck of optimizing machine learning models for diverse hardware and software environments. By providing a unified interface for training and inference, it allows teams to deploy models across CPUs, GPUs, and NPUs without sacrificing performance. Whether you are working with Large Language Models (LLMs) or standard predictive models, this engine ensures that your applications maintain low latency and high throughput, regardless of the underlying infrastructure. Designed for flexibility, the runtime supports a wide array of programming languages—including Python, C#, C++, Java, JavaScript, and Rust—making it a versatile choice for complex technology stacks. It bridges the gap between development and production, enabling developers to maintain consistent model behavior across Linux, Windows, macOS, mobile platforms, and web browsers. By streamlining the execution of state-of-the-art models, it empowers engineers to focus on building intelligent features rather than troubleshooting hardware compatibility or performance degradation.
Optimizes performance for latency, throughput, and memory utilization across a wide range of hardware, including CPUs, GPUs, and NPUs, ensuring your models run efficiently on any device.
Provides robust compatibility across major operating systems like Linux, Windows, and macOS, as well as mobile platforms and web browsers, allowing for a truly portable AI strategy.
Offers native integration for developers using Python, C#, C++, Java, JavaScript, and Rust, making it easy to incorporate high-performance AI into diverse and existing technology stacks.
Enables the deployment of state-of-the-art Large Language Models, supporting advanced tasks like text generation and image synthesis directly within your production applications.
Developers can deploy high-performance AI models on resource-constrained devices like mobile phones or IoT hardware by leveraging optimized runtime configurations.
Engineers can reliably serve machine learning models in production environments, ensuring that end-user applications benefit from low latency and high throughput.
Teams building applications for multiple platforms can use a single, unified runtime to maintain consistent AI performance across desktop, mobile, and web environments.
Professionals focused on optimizing model inference speed and resource efficiency to ensure their AI applications meet production-grade performance standards.
Developers integrating AI into applications across various languages who need a reliable, high-performance execution engine that fits into their existing stack.