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NVIDIA TensorRT vs TensorFlow

Both stand on deep learning software. Which one you can start free with, which one lets you read the code, and what each costs once you pay.

NVIDIA TensorRT

Free plan LinuxSelf-hostedWindows

Choose NVIDIA TensorRT if you need a free development toolkit to optimize deep learning inference on supported NVIDIA GPUs, with integrations for common model workflows and Triton serving. Look elsewhere if you need serialized engines to move between operating systems or require an SDK NVIDIA has tested and certified for critical applications.

Visit NVIDIA TensorRT

TensorFlow

Free plan AndroidAPIiPhone+5

TensorFlow provides a free platform spanning model development and deployment across multiple environments. Check the system support details if GPU acceleration on macOS or WSL2 is important to your workflow.

Visit TensorFlow

The facts

NVIDIA TensorRTTensorFlow
Free planYesYes
Open sourceNot publishedNot published
Runs onLinux, Self-hosted, WindowsAndroid, API, iPhone, Linux, Mac, Self-hosted, Web, Windows
Lowest paid planFree or on requestFree or on request
Plans published21
MakerNot publishedNot published
Websitedeveloper.nvidia.comtensorflow.org
On deep learning software#21 of 35#25 of 35

NVIDIA TensorRT: what it does well

  • Free development SDK with binary and NVIDIA NGC container downloads.
  • Optimization includes quantization, layer and tensor fusion, and kernel tuning.
  • TensorRT Model Optimizer supports FP8, FP4, INT8, INT4, and AWQ.
  • Integrates with PyTorch and Hugging Face and imports ONNX models.
  • Triton backend supports dynamic batching and concurrent model execution.

What to know first

  • Requires NVIDIA hardware with compute capability SM 7.5 or higher.
  • Serialized engines are not portable between platforms such as Linux and Windows.
  • TensorRT 10.0 GA download requires NVIDIA Developer Program membership.
  • NVIDIA says it has not tested or certified the SDK for critical applications.

TensorFlow: what it does well

  • Free and open source under Apache 2.0.
  • Supports distributed training.
  • Deploys to servers, edge devices, and web.
  • Works with Python, Java, Go, and JavaScript.
  • Offers privacy tools for training and federated learning.

What to know first

  • macOS has no GPU support.
  • WSL2 GPU support is marked experimental.

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