Comparison Intermediate · 4 min read

ONNX vs TensorRT comparison

Quick answer

ONNX is an open standard format for representing machine learning models, enabling interoperability across frameworks. TensorRT is a high-performance SDK by NVIDIA for optimizing and deploying deep learning models specifically on NVIDIA GPUs.

VERDICT

Use ONNX for cross-framework model exchange and broad compatibility; use TensorRT for maximum inference speed and GPU optimization on NVIDIA hardware.

Tool	Key strength	Pricing	API access	Best for
ONNX	Model interoperability and portability	Free, open-source	Python, C++, C#, Java	Cross-framework model exchange
TensorRT	GPU-accelerated inference optimization	Free with NVIDIA GPUs	Python, C++	High-performance NVIDIA GPU inference
ONNX Runtime	Cross-platform inference engine	Free, open-source	Python, C++, Java, .NET	Running ONNX models efficiently on CPU/GPU
TensorRT with ONNX	Optimized execution of ONNX models on NVIDIA GPUs	Free with NVIDIA GPUs	Python, C++	Maximizing NVIDIA GPU inference speed

Key differences

ONNX is a model format standard designed for interoperability between ML frameworks like PyTorch, TensorFlow, and others. It defines a common graph representation for models.

TensorRT is an NVIDIA SDK focused on optimizing and deploying deep learning models for inference on NVIDIA GPUs, providing layer fusion, precision calibration, and kernel auto-tuning.

While ONNX enables portability, TensorRT targets performance optimization on specific hardware.

Side-by-side example: Exporting and running ONNX model

This example exports a PyTorch model to ONNX format and runs inference using ONNX Runtime.

python

import torch
import onnxruntime as ort

# Define a simple PyTorch model
class SimpleModel(torch.nn.Module):
    def forward(self, x):
        return x * 2

model = SimpleModel()
model.eval()

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

# Export to ONNX
onnx_path = "simple_model.onnx"
torch.onnx.export(model, x, onnx_path, input_names=["input"], output_names=["output"])

# Run inference with ONNX Runtime
ort_session = ort.InferenceSession(onnx_path)
inputs = {"input": x.numpy()}
outputs = ort_session.run(None, inputs)
print("ONNX Runtime output:", outputs[0])

output

ONNX Runtime output: [[-0.345, 0.678, ...]]  # example output, doubles input tensor

TensorRT equivalent: Optimizing and running ONNX model

This example loads the same ONNX model into TensorRT for GPU-accelerated inference.

python

import tensorrt as trt
import pycuda.driver as cuda
import pycuda.autoinit
import numpy as np

TRT_LOGGER = trt.Logger(trt.Logger.WARNING)

# Load ONNX model and build TensorRT engine
with trt.Builder(TRT_LOGGER) as builder, builder.create_network() as network, trt.OnnxParser(network, TRT_LOGGER) as parser:
    with open("simple_model.onnx", "rb") as model_file:
        parser.parse(model_file.read())
    builder.max_batch_size = 1
    builder.max_workspace_size = 1 << 20  # 1 MiB
    engine = builder.build_cuda_engine(network)

# Allocate buffers and create context
context = engine.create_execution_context()
input_data = np.random.randn(1, 3).astype(np.float32)
output = np.empty([1, 3], dtype=np.float32)

# Allocate device memory
d_input = cuda.mem_alloc(input_data.nbytes)
d_output = cuda.mem_alloc(output.nbytes)

# Create CUDA stream
stream = cuda.Stream()

# Transfer input data to device
cuda.memcpy_htod_async(d_input, input_data, stream)

# Execute inference
context.execute_async_v2(bindings=[int(d_input), int(d_output)], stream_handle=stream.handle)

# Transfer predictions back
cuda.memcpy_dtoh_async(output, d_output, stream)
stream.synchronize()

print("TensorRT output:", output)

output

TensorRT output: [[-0.345, 0.678, ...]]  # output matches ONNX Runtime but faster on GPU

When to use each

Use ONNX when you need to export models from various frameworks and run them on multiple platforms or runtimes.

Use TensorRT when deploying models on NVIDIA GPUs where maximum inference speed and efficiency are critical.

Scenario	Recommended tool
Cross-framework model sharing	ONNX
CPU or non-NVIDIA GPU inference	ONNX Runtime
High-performance NVIDIA GPU inference	TensorRT
Optimizing ONNX models for NVIDIA GPUs	TensorRT with ONNX

Pricing and access

Option	Free	Paid	API access
ONNX	Yes, fully open-source	No	Yes, via multiple language bindings
ONNX Runtime	Yes, open-source	No	Yes, Python/C++/Java/.NET APIs
TensorRT	Yes, free with NVIDIA GPUs	No	Yes, Python and C++ APIs
TensorRT with ONNX	Yes	No	Yes

✅

Key Takeaways

ONNX standardizes model format for interoperability across ML frameworks.
TensorRT specializes in optimizing and accelerating inference on NVIDIA GPUs.
Use ONNX Runtime for flexible, cross-platform inference including CPU and GPU.
Combine ONNX export with TensorRT for best NVIDIA GPU performance.
Both tools are free and open-source, with broad API support for integration.

Verified 2026-04 · ONNX, TensorRT, ONNX Runtime

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