TorchNeuron: running HF models on AWS Trainium is easier than ever

TorchNeuron provides a new native backend for PyTorch on AWS Trainium. Transformers, Diffusers and Kernels natively support it, making it easier than ever to run models on Trainium and to share them on the HF hub.

TorchNeuron

TorchNeuron is a PyTorch plugin that brings a new backend for Trainium enabling support for eager mode, torch.compile and native distributed APIs like FSDP and TP. Read the Pytorch blog to learn more about TorchNeuron.

Starting Oct. 20, 2026, TorchNeuron will be available as a public beta. Researchers and developers will be able to install it with pip install torch-neuronx.

New backend, native interface in HF libraries

To date, the Hugging Face experience on AWS Trainium and Inferentia — AI chips designed respectively for cost-effective training and inference at scale — has been delivered through optimum-neuron. This library relies on the torch-neuronx package, which is based on PyTorch XLA. It was the natural choice at the time, but it meant thinking like a compiler: lazy tensors instead of eager execution, static shapes only, a recompilation for every graph break. Optimum Neuron existed to hide that complexity, and maintained custom code (modeling classes, trainers, etc) to do so.

TorchNeuron changes this. It integrates directly into PyTorch and exposes neuron as a native device, with eager execution, PyTorch's own distributed primitives (DTensor, FSDP2) and torch.compile.

For the Hugging Face libraries, Trainium becomes a device like any other: Transformers, Diffusers, Accelerate, PEFT and TRL run on it directly, with the same classes and scripts you would use on any other accelerator. From now on, we recommend that Neuron users use these libraries directly instead of going through Optimum Neuron.

Transformers

Inference

Training

Here is a supervised fine-tuning script built on TRL's SFTTrainer. If you have fine-tuned a model with TRL on GPUs before, it should look familiar.