onnxruntime.ai

What is ONNX Runtime?

ONNX Runtime is a production-grade inference and training engine for machine learning models in the ONNX format. It takes a model you've already trained or exported and runs it efficiently across different hardware and operating systems, rather than being a framework for building models from scratch. The project describes it as a "production-grade AI engine to speed up training and inferencing in your existing technology stack," with built-in optimizations for latency, throughput, memory use and binary size.

What it actually does

• Runs inference — loads an .onnx model into an inference session and executes predictions. The documented pattern is a few lines: create an InferenceSession from a model path, then call session.run() with your input tensors.
• Supports training — the engine also covers training and fine-tuning workflows, not just deployment.
• Targets many runtimes — Python, C#, C++, Java, JavaScript and Rust are among the supported languages, running on Linux, Windows, macOS, iOS, Android and in web browsers.
• Uses available accelerators — CPU, GPU and NPU execution are all in scope, with additional model-optimization techniques and runtime configuration available when out-of-the-box performance isn't enough.
• Handles generative AI — there is a separate onnxruntime-genai package aimed at LLM and image-synthesis workloads.

Who it's for

The clearest fit is someone who has a model and needs to ship it: a team exporting from PyTorch or TensorFlow, a developer embedding a model in a desktop or mobile app, or an engineer who needs one model to run on both a cloud server and an edge device. If you're still choosing an architecture or doing exploratory research, a training framework is the more natural starting point — ONNX Runtime enters the picture once the model exists.

A practical next step

If you have a model file ready, install the Python package and confirm it loads before worrying about speed:

import onnxruntime as ort
session = ort.InferenceSession("path/to/model.onnx")
outputs = session.run(None, {"input": input_tensor})

If that runs, the next decision is execution provider — which hardware backend the session should use. Getting that wrong is the most common reason people see no speedup over their original framework.

Where to read more

The official documentation, tutorials and model zoo live at ONNX Runtime, and the source and issue tracker are at GitHub. For exporting models into the format in the first place, see ONNX.

How do I install ONNX Runtime for Python?

Install ONNX Runtime for Python with pip, then create an InferenceSession to load an ONNX model and run inference.

pip install onnxruntime

For generative AI and LLM workloads, the site also lists a separate package:

pip install onnxruntime-genai

Basic Python usage

import onnxruntime as ort

session = ort.InferenceSession("path/to/your/onnx/model")
outputs = session.run(None, {"input": input_tensor})
print(outputs)

The key steps are: install the package, load your .onnx model into an InferenceSession, prepare your input tensor, and call session.run() with the input name your model expects.

Choosing the right package

The site notes that ONNX Runtime targets CPU, GPU and NPU acceleration, and supports Python, C#, C++, Java, JavaScript and Rust across Linux, Windows, Mac, iOS, Android and browsers. If you need GPU or NPU execution, that depends on your hardware and drivers rather than a different pip command alone.

Practical next step

Run the quickstart tutorial for Python on ONNX Runtime before adapting your own model — it walks through installation, hardware acceleration and a first session. If you are converting a model from PyTorch or TensorFlow, do that conversion first; ONNX Runtime only loads models already in ONNX format.

Can ONNX Runtime run on mobile and web platforms?

Yes. ONNX Runtime is built to run across platforms, and that explicitly includes mobile and the web. Its own description mentions running on Linux, Windows, Mac, iOS, Android, and "even in web browsers," and it supports languages commonly used in those environments such as JavaScript and Java alongside Python, C#, C++, and Rust.

Where it fits

The practical benefit is model portability: you export a model once and deploy it to a phone, a browser, or a server rather than maintaining separate stacks per platform.

Trade-offs to weigh

• Binary size and memory matter more on mobile and web than on a server. The page notes that runtime configurations and model optimization techniques are available to tune latency, throughput, memory, and binary size — useful when a default build is too heavy for a phone or a browser download.
• Hardware acceleration varies. The page frames acceleration broadly as CPU, GPU, or NPU, but what is actually available depends on the device and browser. A model that is fast on a desktop GPU may fall back to CPU on a mid-range phone.
• Web deployment has different constraints than native apps — download size, caching, and startup time become user-visible concerns.

Next step

If you are deciding, start by naming your two or three real target platforms, then check the language bindings and tutorials for each on ONNX Runtime. For example, a team shipping a browser demo and an Android app would look at the JavaScript and Java paths first, and only then worry about optimization settings.

How does ONNX Runtime accelerate training and inference?

ONNX Runtime accelerates training and inference by acting as a production-grade engine that runs your existing models with hardware-specific optimizations, rather than requiring you to rewrite them. You keep your current stack and swap in ONNX Runtime as the execution layer; it then optimizes for latency, throughput, memory use and binary size on CPU, GPU or NPU. The same runtime covers cloud, edge, web and mobile, and it supports Python, C#, C++, Java, JavaScript, Rust and more. For generative AI and LLMs, ONNX Runtime positions itself as a way to add image synthesis and text generation to apps across languages and platforms. It is also used inside Microsoft products such as Windows, Office, Azure Cognitive Services and Bing, which signals it is built for production workloads rather than only experiments.

Where the speed actually comes from

• Hardware targeting: it optimizes for whatever accelerator is present — CPU, GPU or NPU — instead of assuming one backend.
• Out-of-the-box optimization: common usage patterns get good performance without manual tuning.
• Further tuning for specific models: model optimization techniques and runtime configuration are available when defaults are not enough.
• One runtime for training and inference: you avoid maintaining separate serving and training paths.

A concrete decision path

If you already have a model and a working app, the usual first step is to export or convert the model to ONNX format, then load it with an inference session and run it. The page shows this pattern directly:

import onnxruntime as ort

model_path = "path/to/your/onnx/model"
session = ort.InferenceSession(model_path)

# Load and preprocess the input image
# inputTensor ...

outputs = session.run(None, {"input": inputTensor})
print(outputs)

Installation is shown as pip install onnxruntime, with pip install onnxruntime-genai for generative AI work; other language bindings are documented separately.

When it is the right tool

Choose ONNX Runtime when portability across operating systems and languages matters more than staying inside one framework's native runtime, or when you need to deploy the same model to cloud and edge. If your project is tightly coupled to a single framework's training loop and never leaves it, the conversion step may add friction without much payoff. If you are targeting browsers or mobile, the cross-platform reach is the main reason to adopt it.

Next step

Match your deployment target to the language binding and hardware backend you actually have, then benchmark one real model before committing. The quickstart tutorials and the generative AI section are the practical starting points on ONNX Runtime.

What languages and frameworks are supported by ONNX Runtime?

ONNX Runtime is designed to run your existing models across many languages and platforms rather than locking you into one stack. Its core value is a single inference and training engine that plugs into the language you already use.

Languages listed on the site: Python, C#, C++, Java, JavaScript, and Rust are named directly, with "many others" supported. The Python path is the most documented, with pip install onnxruntime and an InferenceSession example, plus a separate onnxruntime-genai package for generative AI and LLM work.

Platforms: Linux, Windows, Mac, iOS, Android, and web browsers. Hardware targets include CPU, GPU, and NPU.

Where it fits in a real project: if your team ships a Python service but also a C# desktop app and a JavaScript front end, ONNX Runtime lets all three call the same exported model instead of maintaining separate runtimes. That is the main trade-off: you gain consistency and built-in optimizations, but you take on a conversion step to the ONNX format and must verify each target's operator coverage.

Decision criterion: choose it when portability across languages or devices matters more than staying inside one framework's native tooling. If you only ever deploy PyTorch on one server, the extra conversion layer may not pay off.

Next step: start with the quickstart tutorial for your primary language, then confirm your model's operators run on your target hardware before committing. For background, see ONNX Runtime and the upstream format project at ONNX.

How can I use ONNX Runtime for generative AI and LLMs?

Use ONNX Runtime as the execution engine for generative AI and LLM workloads: you bring a model (typically converted to ONNX format), and it runs inference or training on your existing stack, with hardware acceleration and optimization applied at the runtime level.

Where it fits in a generative AI pipeline

A typical flow looks like this:

1. Obtain or fine-tune a model (for example, a text-generation or image-synthesis model).
2. Convert or export it to ONNX format.
3. Load it into an ONNX Runtime session in your app and run inference against your inputs.

The site's own Python example follows that shape: create an InferenceSession from a model path, then call session.run(...) with an input tensor and read the outputs. That is the same pattern whether the model is a small classifier or a large language model — the difference is mainly in model size, input handling, and the optimizations you enable.

For generative AI specifically, the page points to a companion package, onnxruntime-genai, installed alongside the core runtime. That package is aimed at the generative loop around a model — the repeated token-by-token generation that a plain inference session doesn't handle for you.

Language and platform reach

This is the main reason teams pick it over a single-framework solution. The page states support for Python, C#, C++, Java, JavaScript, and Rust, running on Linux, Windows, Mac, iOS, Android, and in browsers. So the same model can serve a cloud endpoint, a desktop app, a mobile feature, and an in-browser experience without rewriting the inference layer per platform.

Practical trade-offs

A concrete starting scenario

Say you are adding an in-app summarization or chat feature to a .NET desktop application. You would export your chosen model to ONNX, install the runtime for C#, create an inference session, and feed tokenized input. If you need streaming, multi-turn generation rather than a single forward pass, look at the generative AI tooling rather than the bare session API. If you later need the same feature on iOS or in the browser, the model and much of the surrounding logic carry over.

Next step

Start with the quickstart tutorial for the language you already use, run one small model end to end, then measure latency and memory before deciding whether your use case needs deeper optimization or a different accelerator. For anything LLM-shaped, evaluate the generative AI path separately from plain inference, since the generation loop is where most of the engineering effort lands.

Primary reference: ONNX Runtime.