If you’ve ever worked with AI models, you know how exciting it is to see them in action. But here’s the catch — many models are slow to run, especially in production environments. That’s where ONNX Runtime comes in. It’s a game-changer for speeding up model inference without changing the model itself.

In this guide, you’ll learn exactly what ONNX Runtime is, why it’s useful, and how you can use it to run your AI models faster. Whether you’re a beginner in AI or an experienced developer looking for performance boosts, this post will break it down simply and clearly.

What Is ONNX Runtime (ORT)?

ONNX Runtime is an open-source, high-performance engine for running machine learning models. Developed by Microsoft, it supports models trained in popular frameworks like PyTorch, TensorFlow, and scikit-learn by converting them to the ONNX (Open Neural Network Exchange) format.

Think of ONNX Runtime as a universal language interpreter for AI models. You train your model in any framework, convert it to ONNX, and then ONNX Runtime takes care of running it efficiently across various hardware (CPU, GPU, even specialized accelerators).

Why Use ONNX Runtime?

Speed

ONNX Runtime is optimized for speed. It reduces inference time dramatically compared to native frameworks.

Cross-Platform

It runs on Windows, Linux, macOS, Android, and iOS. You can use it in cloud services, edge devices, or even mobile apps.

Flexibility

Supports models from PyTorch, TensorFlow, scikit-learn, XGBoost, and more — once converted to ONNX.

Cost-Efficient

Faster inference means fewer resources and lower cloud costs. Who doesn’t like saving money..?

How Does ONNX Runtime Work?

Here’s the simple flow:

1. Train your model using TensorFlow, PyTorch, or another framework.
2. Export the model to ONNX format.
3. Use ONNX Runtime to run inference — faster and more efficiently.

Running a Model with ONNX Runtime

Let’s see a basic Python example to understand how to use ONNX Runtime.

Install ONNX Runtime

pip install onnxruntime

This command installs the CPU version. If you have a GPU, you can install the GPU version like this:

pip install onnxruntime-gpu

Load an ONNX Model

Let’s say you have a model called model.onnx.

import onnxruntime as ort

# Create an inference session
session = ort.InferenceSession("model.onnx")

Prepare Input

You need to know the input names and shapes.

import numpy as np

# Get input name
input_name = session.get_inputs()[0].name

# Create dummy input
input_data = np.random.randn(1, 3, 224, 224).astype(np.float32)

Run Inference

# Run inference
outputs = session.run(None, {input_name: input_data})

print("Model Output:", outputs[0])

That’s it! You just ran an AI model using ONNX Runtime in a few lines of code.

How to Convert Models to ONNX Format

import torch

# Example PyTorch model
model = torch.hub.load('pytorch/vision', 'resnet18', pretrained=True)
model.eval()

# Dummy input
dummy_input = torch.randn(1, 3, 224, 224)

# Export to ONNX
torch.onnx.export(model, dummy_input, "resnet18.onnx")

Now you can use resnet18.onnx with ONNX Runtime for fast inference.

When Should You Use ONNX Runtime?

If you need consistent, fast model inference across different environments, ONNX Runtime is a solid choice.

ONNX Runtime vs Native Frameworks

Tips for Maximizing ONNX Runtime Performance

• Use ONNX Optimizer: Tools like onnxoptimizer help remove redundant operations.
• Enable Graph Optimizations: ONNX Runtime automatically optimizes computation graphs.
• Leverage Execution Providers: Choose CUDAExecutionProvider for GPU, CPUExecutionProvider for CPU, or others like TensorRT.
• Batch Inputs: Inference is faster with batched data.

Conclusion

ONNX Runtime is not just a tool — it’s a performance booster for AI inference. It simplifies deployment, cuts inference time, and makes your AI projects more scalable.

If you’ve been struggling with slow model inference or complicated deployments, ONNX Runtime is your friend. Install it, give it a try, and see the speed-up for yourself.

FAQs

Q: Is ONNX Runtime free?
 Yes, it’s completely open-source and free to use under the MIT license.

Q: Can I use ONNX Runtime with GPU?
 Absolutely. Just install onnxruntime-gpu and you’re good to go.

Q: Does ONNX Runtime support quantized models?
 Yes! It supports quantization for even faster and smaller models.