NVIDIA TensorRT

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Accelerate inference speeds up to 100x, optimize and deploy deep learning models quickly, compatible with popular frameworks.

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Saas

Company

NVIDIA

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About NVIDIA TensorRT

NVIDIA TensorRT is an AI-acceleration platform that provides maximum performance and fast inference times for deep learning applications. It is a high-performance deep learning inference optimizer and runtime for production deployment of AI models. With NVIDIA TensorRT, you can quickly optimize and deploy trained neural networks in production environments, enabling faster and more accurate inference.NVIDIA TensorRT enables developers to optimize, validate, and deploy trained deep learning models in production environments with dramatically higher inference performance. It features highly optimized graph optimizations, such as layer fusion, kernel auto-tuning, and half-precision FP16 support, to accelerate model inference by up to 100x compared to CPU-only platforms. Additionally, it offers built-in support for NVIDIA GPUs, and works with popular deep learning frameworks such as TensorFlow and PyTorch.NVIDIA TensorRT is ideal for developers and data scientists who need to quickly optimize and deploy trained deep learning models in production environments.

Key Features

Accelerate inference speeds up to 100x with NVIDIA TensorRT.

Optimize, validate, and deploy trained deep learning models quickly.

Compatible with popular deep learning frameworks like TensorFlow and PyTorch.

Pros & Cons

Pros

Achieves up to 36x inference speedup compared to CPU-only platforms
Supports a wide range of quantization precisions for reduced memory and latency
Integrated directly into popular frameworks like PyTorch and Hugging Face
Open-source TensorRT-LLM library for advanced LLM optimization
Cloud-based engine optimization service (TensorRT Cloud) automates tuning for target GPUs and KPIs
Comprehensive model optimization toolkit with pruning, distillation, and speculation

Cons

TensorRT Cloud is currently limited to select partners with restricted access
Requires NVIDIA GPUs (CUDA) and may not run on non-NVIDIA hardware
Optimization process can be complex for users unfamiliar with CUDA and model quantization

Best For

Accelerate inference speeds up to 100x with NVIDIA TensorRT.Optimize, validate, and deploy trained deep learning models quickly.Compatible with popular deep learning frameworks like TensorFlow and PyTorch.

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FAQ

What is NVIDIA TensorRT?

NVIDIA TensorRT is an SDK ecosystem for high-performance deep learning inference, including compilers, runtimes, and model optimizations that deliver low latency and high throughput for production applications.

What kind of speedup does TensorRT offer?

TensorRT can accelerate inference by up to 36 times compared to CPU-only platforms, using optimizations like quantization, layer fusion, and kernel tuning.

Which frameworks does TensorRT support?

TensorRT integrates directly into PyTorch and Hugging Face, and provides an ONNX parser to import models from other frameworks. It also supports MATLAB through GPU Coder.

Is TensorRT-LLM open-source?

Yes, TensorRT-LLM is an open-source library that accelerates and optimizes inference performance of large language models on NVIDIA GPUs with a simplified Python API.

What is TensorRT Cloud?

TensorRT Cloud is a developer-focused service that generates hyper-optimized TensorRT-LLM or ONNX engines for given constraints and KPIs. It is currently available with limited access to select partners.