PyTorch vs TensorFlow: What Is the Difference?

PyTorch and TensorFlow are the two most widely used open-source frameworks for deep learning. Each gives you tensors (multi-dimensional arrays that run on a GPU), automatic calculation of gradients for training, ready-made neural network layers and tools to save and deploy the finished model.

TensorFlow came first. Google open-sourced it in November 2015, and TensorFlow 2.0 in 2019 made it easier to use by adopting Keras as its main interface. PyTorch was released by Facebook’s AI research lab, now Meta, in 2016 and is governed by the PyTorch Foundation under the Linux Foundation since 2022. Both are free under permissive open-source licences.

The original difference was how code runs. Early TensorFlow made you define a static computation graph first and then run it in a session, which was fast but awkward to debug. PyTorch ran each operation immediately, in what is called eager mode, so models behaved like normal Python and could be stepped through with a standard debugger.

The gap has narrowed. TensorFlow 2 runs eagerly by default and compiles graphs on request with tf.function, while PyTorch 2 added torch.compile to speed up eager code. The differences that remain are mostly about style, tooling and where each one is used.

Day-to-day tooling overlaps more than people expect. TensorBoard, the training dashboard that started with TensorFlow, also logs PyTorch runs, and both frameworks call the same NVIDIA CUDA libraries on a GPU. The bigger practical difference is the libraries built on top. Hugging Face Transformers, PyTorch Lightning and most robotics and generative AI toolkits are written for PyTorch first, while TensorFlow Extended (TFX) and TensorFlow.js cover production pipelines and models that run in a web browser.

In short, PyTorch is the default for new deep learning work, while TensorFlow remains common in production systems built before about 2020, in browser apps through TensorFlow.js and in Android apps that use LiteRT.

In research, PyTorch is clearly ahead: most new papers that publish code use it, and most models on the Hugging Face model hub, including nearly all open-weight large language models, are released as PyTorch weights. NVIDIA’s own robotics and generative AI tools are largely PyTorch-based.

TensorFlow is far from gone. Many companies run large TensorFlow codebases, and Google continues to develop it, although much of Google’s own research has moved to JAX. For a new project, the larger pool of examples and pretrained models usually favours PyTorch.

On edge hardware the framework matters less than it does during training. A typical path is to train in PyTorch or TensorFlow on a GPU server, export to ONNX, then compile the model with NVIDIA TensorRT for a Jetson or GPU target, usually at FP16 or INT8. Hailo’s Dataflow Compiler accepts ONNX and LiteRT models, and Intel OpenVINO takes both frameworks’ formats.

On Jetson, NVIDIA publishes PyTorch builds for JetPack and supplies containers with PyTorch preinstalled; TensorFlow builds have also been published for some JetPack releases, though PyTorch is the more common choice. Our Jetson Orin computers and industrial GPU computers run models from either framework once converted. Our guide to the NVIDIA JetPack software stack explains the libraries underneath.

A few rules of thumb cover most projects.

We supply Jetson and GPU computers with JetPack or the NVIDIA driver stack, CUDA and your chosen framework installed as standard, and validate the system before it ships. Tell us which framework and model you use, and we will set up the conversion and runtime to match.