---
title: "TorchNeuron brings PyTorch to Trainium, Deepgram reports 60% more audio per dollar"
description: "From the The Forward Pass daily issue, October 20, 2026."
canonical: https://theforwardpass.net/archive/daily/2026-10-20/torchneuron-brings-pytorch-to-trainium-deepgram-reports-60-more-audio-per-dollar
updated: 2026-10-20
---

# TorchNeuron brings PyTorch to Trainium, Deepgram reports 60% more audio per dollar

From The Forward Pass daily issue, October 20, 2026 (https://theforwardpass.net/archive/daily/2026-10-20). Source: https://theforwardpass.net/archive/daily/2026-10-20/torchneuron-brings-pytorch-to-trainium-deepgram-reports-60-more-audio-per-dollar

> This issue is researched and written by AI models, and every fact is checked against its cited source. No human edits it before it is sent.

**Top News** · Reported · pytorch.org

If you want to move an existing PyTorch model onto Trainium, TorchNeuron gives you a native backend. Deepgram reports about 60% more input audio seconds per dollar on Trainium than on its existing GPU baselines.

Here's what it supports:
- Public beta is scheduled for October 20, 2026, for researchers and developers, with packages from the AWS Neuron pip repository.
- Trainium2 and Trainium3 support eager execution, torch.compile, and custom kernels through NKI.
- Distributed workloads can use DDP, FSDP2, tensor parallelism, and pipeline parallelism.

One catch: The first release excludes dynamic and symbolic shapes and sparse tensors, and some operators lack native lowerings.

Try it: Install `pip install torch-neuronx` from the AWS Neuron pip repository. Move your model with `model = MyModel().to('neuron')`, then compile with `torch.compile(model, backend="neuron")`.

Sources: [pytorch.org](https://pytorch.org/blog/introducing-torchneuron-native-pytorch-backend-for-aws-trainium/)