TensorRT

TensorRT is an SDK with an optimizer for high-performance deep learning inference on NVIDIA GPUs.

NVIDIA TensorRT is an ecosystem of tools for developers to achieve high-performance deep learning inference. It includes inference compilers, runtimes, and model optimizations that deliver low latency and high throughput for production applications. Built on the NVIDIA CUDA parallel programming model, TensorRT includes libraries that optimize neural network models trained on all major frameworks, calibrate them for lower precision with high accuracy, and deploy them to hyperscale data centers, workstations, laptops, and edge devices. TensorRT optimizes inference using quantization, layer and tensor fusion, and kernel tuning techniques. It integrates directly into PyTorch and Hugging Face, and supports ONNX models and MATLAB via GPU Coder.

TensorRT at a glance

Best for: Software engineer
Runs on: Linux, Windows, API, CLI, self-hosted
Pricing: Free
Released: May 2019
Category: AI coding tool
Website: developer.nvidia.com

Key features

Inference compilers and runtimes
Quantization and pruning techniques
Layer and tensor fusion
Kernel tuning
TensorRT-LLM integration
ONNX parser support
Model compression

Use it for

Optimizing large language model inference
Deploying deep learning models to edge devices
Compressing models for production frameworks
Generating high-performance inference engines

Alternatives to TensorRT

ncnn — No runtime dependencies and supports CPU and Vulkan GPU backends.
Reflex — It is optimized for cold-start latency and single-tenant decisions.
MNN — It delivers high-performance inference and training with a lightweight footprint.
NVIDIA Model Optimizer — Compress deep learning models to accelerate inference performance.
fal — Access 1,000+ generative models and high-performance serverless GPUs.
Flare Local Inference — Optimizes memory and bandwidth for workstations and compact systems.
AirLLM — Runs massive 70B+ parameter models on small 4GB consumer GPUs.
ort — Provides an ergonomic Rust API for running high-performance ONNX models.
iwant — Spins up cloud GPUs and open models instantly via a single command.
Dlgo — Run LLM inference quickly in pure Go without Python or external runtimes.
Cube Studio — An open source, end-to-end platform for MLOps and distributed model training.
LMDeploy — Accelerates inference with high-performance engines and quantization tools.

All TensorRT alternatives