# Hugging Face — Does Your Model Run on AWS Trainium? Look It Up

- Company: Hugging Face (huggingface.co)
- Announced: 2026-10-23
- Category: developer-tool-release
- Coverage: not counted
- Announcement: yes
- Group: announcements
- Source: https://huggingface.co/blog/torch-neuronx-model-support
- Record: https://forck.live/items/27130-does-your-model-run-on-aws-trainium-look-it-up
- Subject: Platform
- Models affected: Qwen/Qwen3-0.6B

Hugging Face has published a Space that records which popular Transformers models on the Hub run unmodified on AWS Trainium, after the TorchNeuron backend made that possible. Each row reports the result, the number of Trainium cores the model is sharded over, the wall time including compilation, and peak device memory. Tests load the model on a trn2 instance over the smallest number of cores that hold its weights, generate 32 tokens and check them, comparing against a CPU run on a single core and using a small judge model for fluent English when sharded. Only models fitting on 4 Trainium2 devices, at most 16 cores, are tested, and a PASS says nothing about throughput or longer generations. The Space lists the Neuron stack, packages and run.py that produced each result, and users can skip graph compilation by syncing cached graphs from the public NEFF cache bucket. Coverage is limited to text-generation and image-text-to-text models for now, with plans to add diffusers, sentence transformers and other tasks; all results come from the public aws-neuron/torch-neuronx-model-support dataset.

## Evidence

Verbatim from https://huggingface.co/blog/torch-neuronx-model-support:

> The torch-neuronx model support Space answers that question for the most popular models on the Hub. It tells you whether a model runs unmodified on Trainium, and gives you the script to run it yourself.

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