ONNX (Open Neural Network Exchange): Portable AI Models, TensorRT and Cross-Framework Inference

Open Neural Network Exchange (ONNX) 📦

JPEG for AI world

What is ONNX?

ONNX is an open standard format for representing machine learning and deep learning models.

It allows models trained in one framework to run in another framework or runtime.

Why ONNX Exists

Different AI frameworks use different internal formats.

Example:

• PyTorch
• TensorFlow
• JAX
• MXNet

Without ONNX:

Models are tightly coupled to their original framework.

ONNX provides a common interoperability layer.

Why ONNX Became Popular

It simplifies:

Train anywhere → deploy everywhere

This is especially important for:

• production AI systems
• GPU inference
• edge devices
• heterogeneous hardware environments

ONNX Architecture

flowchart TD

    A["Training Framework 𖣘"]
        --> B["ONNX Export 📥"]

    B --> C["ONNX Graph 📦"]

    C --> D["Inference Runtime 📟"]

    D --> E["CPU / GPU / Edge 🧮"]

Typical ONNX Pipeline

1. Train model in PyTorch

import torch

model = MyModel()

2. Export model to ONNX

torch.onnx.export(
    model,
    sample_input,
    "model.onnx"
)

This creates:

model.onnx

3. Run anywhere

The ONNX model can now run on:

• CPU
• GPU
• TensorRT
• Edge devices
• Cloud inference servers

flowchart TD

    A["Train Model 𖣘 <br/>PyTorch / TensorFlow"]
        --> B["Export to ONNX 📥"]

    B --> C["ONNX Model 📦"]

    C --> D["TensorRT / ONNX Runtime / OpenVINO 📟"]

    D --> E["Optimized Inference 🎛"]

---

What an ONNX Model Contains

Portable representation of a neural network.

An ONNX file stores:

• computation graph
• operators
• weights
• tensor shapes
• metadata

ONNX Runtime

A common runtime is:

ONNX Runtime (ORT)

It is optimized for:

• CPU inference
• GPU inference
• TensorRT integration
• edge AI

Example:

import onnxruntime as ort

session = ort.InferenceSession("model.onnx")

ONNX + TensorRT

TensorRT commonly consumes ONNX models.

Pipeline:

flowchart TD

    A["PyTorch Model"]
        --> B["ONNX Export 📥"]

    B --> C["TensorRT Optimizer 🖲"]

    C --> D["TensorRT Engine 📟"]

    D --> E["Fast GPU Inference 🧮"]

Feature	ONNX	TensorRT
Purpose	Model portability	GPU acceleration
Vendor	Open standard	NVIDIA
Hardware specific	NO	YES
Training support	NO	NO
Inference support	Yes	Yes
Optimization level	Minimal	Aggressive
GPU optimization	Limited	Excellent
CPU support	YES	Limited
Cross-platform	YES	NVIDIA GPUs only

ONNX Operators

ONNX represents models as graphs of operators.

Examples:

• Conv
• MatMul
• ReLU
• Softmax
• Attention

These operators are standardized.

Why ONNX Is Important

ONNX enables:

• framework interoperability
• portable AI deployment
• hardware acceleration
• production inference optimization

Without ONNX:

• deploying models across ecosystems becomes difficult.

ONNX vs SavedModel vs TorchScript

Format	Ecosystem
ONNX	Cross-framework
TorchScript	PyTorch-specific
SavedModel	TensorFlow-specific

ONNX is the most portable.

---

Common ONNX Use Cases

• TensorRT optimization
• Edge AI deployment
• Cross-platform inference
• LLM serving
• Mobile AI
• Cloud inference
• Hardware acceleration

---

ONNX Ecosystem

Component	Purpose
PyTorch	Training
TensorFlow	Training
ONNX	Portable model format
ONNX Runtime	Inference
TensorRT	GPU optimization
OpenVINO	Intel optimization

---

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Related Posts

• TensorRT and High-Performance AI Inference — the runtime ONNX models are most commonly converted into for deployment
• NVIDIA NIM: Optimized Inference Microservices — one of the deployment targets for a converted ONNX/TensorRT model