NVIDIA TensorRT vs ONNX Runtime

TL;DR

• ONNX Runtime is a versatile, hardware-agnostic inference engine with support for multiple execution providers, making it suitable for various deployment environments beyond NVIDIA hardware. It focuses on general-purpose optimization and cross-platform compatibility.

• NVIDIA TensorRT is a specialized inference engine for NVIDIA GPUs, offering advanced, GPU-specific optimizations that maximize performance, especially for low-latency and real-time applications.

NVIDIA TensorRT

It's a high-performance deep learning inference SDK specifically optimized for NVIDIA GPUs. It is tailored for maximum efficiency on NVIDIA hardware and offers deep integration with CUDA and cuDNN libraries to extract the best possible performance from GPU resources.

These are the main features:

1. NVIDIA Hardware-Specific and CUDA support.

2. Layer and Kernel Auto-Tuning (Kernel Auto-Tuning)

3. Quantization

4. Tensor Memory management: allocates memory on GPU only when needed. This allows for more available GPU memory and bigger batch sizes.

ONNX Runtime

It's an open-source inference engine that executes models in the Open Neural Network Exchange (ONNX) format. It's designed to be hardware-agnostic, allowing models to be deployed across various hardware platforms such as CPUs, GPUs, FPGAs, and specialized accelerators.

Main features:

1. Hardware-Agnostic

2. Model flexibility (TensorFlow, PyTorch, Keras, MXNet ...)

3. Graph optimizations like constant folding, operator fusion, and dead node elimination.

4. Quantization

5. Dynamic input shapes

Comparison of features

Features	ONNX Runtime	NVIDIA TensorRT
Hardware Support	Multi-platform: CPUs, NVIDIA GPUs, Intel hardware, FPGAs, custom accelerators.	NVIDIA GPUs only
Execution Providers	CUDA, TensorRT, OpenVINO, DirectML, etc.	CUDA and TensorRT for NVIDIA GPUs.
Model Format	Supports ONNX models (from TensorFlow, PyTorch, Keras, etc)	TensorFlow, PyTorch, ONNX, Caffe. Converts them to TensorRT's format.
Optimization Techniques	Graph optimizations, and basic kernel auto-tuning (depending on the execution provider).	Advanced kernel auto-tuning, mixed-precision, and INT8 quantization.
Precision Support	Supports FP32, FP16, and INT8 quantization.	Optimized support for FP16 and INT8 with extensive calibration tools.
Dynamic Shapes	Natively supports dynamic input sizes and batch dimensions.	Supports dynamic shapes but requires explicit optimization during engine building.
Use Case Flexibility	Suitable for a wide range of hardware and model types, adaptable to various deployment environments.	Best for high-performance, low-latency applications on NVIDIA GPUs.
Ease of Use	More flexible; integrates with different backends using execution providers.	More complex; requires NVIDIA GPU-specific setup and optimizations.