TensorRT and High-Performance AI Inference: CUDA, ONNX, TensorRT-LLM and GPU Optimization

🖲 TensorRT

NVIDIA TensorRT is a C++ library that facilitates high-performance inference on NVIDIA graphics processing units (GPUs).

TensorRT takes a trained network and produces a highly optimized runtime engine that performs inference for that network.

What is TensorRT?

NVIDIA’s high-performance deep learning inference SDK designed to optimize and accelerate trained AI models on NVIDIA GPUs.

It takes trained models from frameworks such as PyTorch, TensorFlow, and ONNX, and optimizes them for high-performance deployment with support for mixed precision (FP32/FP16/BF16/FP8/INT8), dynamic shapes, and specialized optimizations for transformers and large language models (LLMs).

It is mainly used for:

• Low-latency inference
• High-throughput AI serving
• Real-time AI applications
• LLM inference optimization
• Edge AI deployments

TensorRT takes trained models from frameworks like PyTorch or TensorFlow and converts them into highly optimized GPU execution engines.

Why TensorRT Is Fast

TensorRT improves performance through:

• Kernel fusion
• Reduced precision inference
• GPU-specific tuning
• Optimized memory reuse
• Parallel CUDA execution
• Reduced data movement
• Efficient batching

This can often produce:

• 2x–10x faster inference
• lower latency
• lower GPU memory usage

compared to standard framework inference.

TensorRT Architecture

flowchart TD

    A["Trained Model 🎛<br/>PyTorch / TensorFlow"]
        --> B["ONNX Export"]

    B --> C["TensorRT Optimizer<br/><br/>• Layer Fusion<br/>• Quantization<br/>• Kernel Tuning"]

    C --> D["TensorRT Engine 🖲" ]

    D --> E["CUDA Runtime 📟"]

    E --> F["NVIDIA GPU 🧮" ]

TensorRT vs PyTorch Inference

Feature	PyTorch	TensorRT
Ease of use	Easier	More optimization setup
Training support	Yes	No
Inference speed	Good	Excellent
GPU optimization	General	Highly optimized
Production deployment	Moderate	Excellent
Latency	Higher	Lower

Running TensorRT

TensorRT is available as standalone Docker Image

# Pull the latest TensorRT Docker image
docker pull nvcr.io/nvidia/tensorrt:26.04-py3

# Run a container with TensorRT
docker run --gpus all -it --rm nvcr.io/nvidia/tensorrt:26.04-py3

Common TensorRT Use Cases

• LLM serving
• Real-time computer vision
• Autonomous driving
• Recommendation systems
• Speech AI
• Video analytics
• Edge AI devices
• Robotics
• Medical imaging

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How TensorRT Works Under the Hood

TensorRT Ecosystem

Component	Purpose
CUDA	GPU compute platform
cuDNN	Deep learning kernels
TensorRT	Inference optimization
Triton Server	Model serving
TensorRT-LLM	LLM optimization
NCCL	Multi-GPU communication

1. Model Import

TensorRT typically imports models using ONNX.

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

Supported sources:

• PyTorch
• TensorFlow
• ONNX
• Hugging Face Transformers
• TensorFlow-TRT integration

2. Graph Optimization

TensorRT analyzes the neural network computation graph and applies optimizations such as:

• Layer fusion
• Kernel auto-tuning
• Precision calibration
• Memory optimization
• Tensor layout optimization

Example:

    Conv + BatchNorm + ReLU
            ↓
    Single fused GPU kernel

This reduces:

• GPU memory reads/writes
• Kernel launch overhead
• Latency

Precision Optimization

TensorRT supports multiple precision modes:

Precision	Description
FP32	Standard floating point
FP16	Half precision for faster inference
INT8	Quantized inference for maximum speed
FP8	Newer ultra-efficient precision on modern GPUs

Lower precision:

• reduces memory usage
• increases throughput
• improves latency

FP32 vs FP16 vs INT8

Mode	Speed	Accuracy	Memory Usage
FP32	Slowest	Highest	Highest
FP16	Faster	Very close	Lower
INT8	Fastest	Slight drop possible	Lowest

Example:

config.set_flag(trt.BuilderFlag.FP16)

3. CUDA Kernel Selection

TensorRT benchmarks multiple CUDA kernels internally and selects the fastest implementation for the target GPU.

This is called:

Kernel Auto-Tuning

Different GPUs may produce different optimized engines.

4. Engine Generation

TensorRT builds a serialized inference engine.

serialized_engine = engine.serialize()

This engine contains:

• optimized kernels
• memory plans
• execution graphs
• scheduling strategies

The engine is GPU-specific.

5. Runtime Execution

Inference executes directly on the GPU with minimal CPU overhead.

context.execute_v2(bindings)

TensorRT optimizes:

• memory reuse
• asynchronous execution
• CUDA stream utilization
• batching

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TensorRT + LLM Inference

TensorRT is heavily used for LLM acceleration.

NVIDIA provides:

• TensorRT-LLM (its predecessor, FasterTransformer, has been folded into and superseded by TensorRT-LLM)
• Triton Inference Server integration

Optimizations for LLMs include:

• KV cache optimization
• Attention kernel fusion
• Paged attention
• Tensor parallelism
• Continuous batching

TensorRT LLM Example

1. Convert ONNX model to TensorRT engine

trtexec --onnx=model.onnx --fp16 --saveEngine=model.engine

2. Python inference example

import tensorrt as trt

logger = trt.Logger(trt.Logger.WARNING)
runtime = trt.Runtime(logger)

with open("model.engine", "rb") as f:
    engine = runtime.deserialize_cuda_engine(f.read())

context = engine.create_execution_context()

Related Posts

• NVIDIA NIM: Optimized Inference Microservices — NIM profiles are pre-compiled TRT-LLM engines; this post explains what's inside those profiles
• NVIDIA Triton Inference Server — Triton loads TensorRT engines as its TensorRT-LLM backend for LLM serving
• ONNX: Portable AI Models — ONNX is the standard interchange format used to import models into TensorRT for compilation
• Optimizing AI Inference at Scale — where TRT-LLM optimizations (FP8, kernel fusion, in-flight batching) fit in the full inference optimization stack
• Training a Neural Network — the trained neural network produced by backpropagation + gradient descent is the input to TensorRT; TRT optimizes the inference side of this training pipeline