Capabilities: OpenNN vs PyTorch
OpenNN and PyTorch both build, train and run neural networks for tabular data, time series, images and text, on CPUs and NVIDIA GPUs. They share 10 of the 12 capabilities we compared. PyTorch offers a wider range of models and tools.
This page compares functionality as of September 2026: the OpenNN development version and PyTorch 2.13. The other benchmark pages compare performance on the networks that both libraries support.
Models
| Capability | OpenNN | PyTorch |
|---|---|---|
| Dense networks | Yes | Yes |
| Convolutional networks | Yes | Yes |
| LSTM networks | Yes | Yes |
| Transformers | Yes | Yes |
| Object detection | Built-in YOLO | Via TorchVision |
| Pretrained models | Selected models | Extensive selection |
| Text generation | Built-in GPT-2 and Qwen3 | Via Hugging Face Transformers |
| Image generation | None included | Via Hugging Face Diffusers |
Both libraries support the four network types we benchmark: dense, convolutional, LSTM and Transformer networks.
For object detection and text generation, OpenNN includes ready-to-use YOLO, GPT-2 and Qwen3 examples. PyTorch covers these tasks through companion libraries such as TorchVision and Hugging Face Transformers, which offer a much larger catalogue of pretrained models. Image generation is available for PyTorch through Hugging Face Diffusers; OpenNN does not include it.
GPU
| Capability | OpenNN | PyTorch |
|---|---|---|
| GPU training and inference | Yes | Yes |
| Mixed precision | Yes | Yes |
| CUDA graphs | Yes | Yes |
| Distributed training | No | Yes |
Both libraries train and run networks on NVIDIA GPUs, with mixed precision and CUDA graphs. PyTorch also supports distributed training across several GPUs and computers. Multi-GPU training and inference are on the OpenNN roadmap.
See also code size and training performance.