Model reference · open weights
nougat-small is an open-weight language model from Meta. nougat-small (FP32) weighs 495 MB; the smallest configuration that runs it is RTX 3060 12 GB.
Summary of the facebook/nougat-small model card, 2026-10-01
What it is
| Released by | Meta |
|---|---|
| Released | 2023-09-21 |
| Parameters | 247M |
| VRAM | 495 MB for the weights |
What it runs on
How much memory each request adds isn't estimated yet for this architecture. The weights need at least the cards below, plus room for the context.
| Card | Weights alone |
|---|---|
| RTX 3060 12 GB | fits |
| RTX 4060 Ti 16 GB | fits |
| RTX 3090 24 GB | fits |
| RTX 4090 24 GB | fits |
| RTX 5090 32 GB | fits |
| L40S 48 GB | fits |
| A100 80 GB | fits |
| H100 80 GB | fits |
| RTX PRO 6000 Blackwell 96 GB | fits |
| DGX Spark (GB10) 128 GB unified | fits |
| H200 141 GB | fits |
| B200 180 GB | fits |
From the model card
Nougat model trained on PDF-to-markdown. It was introduced in the paper Nougat: Neural Optical Understanding for Academic Documents by Blecher et al. and first released in this repository.
Disclaimer: The team releasing Nougat did not write a model card for this model so this model card has been written by the Hugging Face team.
Note: this model corresponds to the "0.1.0-small" version of the original repository.
Nougat is a Donut model trained to transcribe scientific PDFs into an easy-to-use markdown format. The model consists of a Swin Transformer as vision encoder, and an mBART model as text decoder.
The model is trained to autoregressively predict the markdown given only the pixels of the PDF image as input.
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You can use the raw model for transcribing a PDF into Markdown. See the model hub to look for other fine-tuned versions that may interest you.
We refer to the docs.
@misc{blecher2023nougat,
title={Nougat: Neural Optical Understanding for Academic Documents},
author={Lukas Blecher and Guillem Cucurull and Thomas Scialom and Robert Stojnic},
year={2023},
eprint={2308.13418},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.