Model reference · open weights
udop-large is an open-weight language model from microsoft. udop-large (FP32) weighs 1.5 GB; the smallest configuration that runs it is RTX 3060 12 GB.
For self-hosting, compare this build's requirements below with the GPU you plan to rent.
Summary of the microsoft/udop-large model card, 2026-10-01
What it is
| Released by | microsoft |
|---|---|
| Released | 2024-02-26 |
| Parameters | 742M |
| VRAM | 1.5 GB for the weights |
What it runs on
| Card | Requests at once | Context max | Memory | |
|---|---|---|---|---|
| 8K each | 32K each | |||
| RTX 3060 12 GB | 11 | 2 | 95K | 11.6 GB |
| RTX 4060 Ti 16 GB | 16 | 4 | 128K | 15.4 GB |
| RTX 3090 24 GB | 26 | 6 | 128K | 23.4 GB |
| RTX 4090 24 GB | 26 | 6 | 128K | 23.4 GB |
| RTX 5090 32 GB | 36 | 9 | 128K | 31.0 GB |
| L40S 48 GB | 52 | 13 | 128K | 44.0 GB |
| A100 80 GB | 94 | 23 | 128K | 78.2 GB |
| H100 80 GB | 91 | 22 | 128K | 78.1 GB |
| RTX PRO 6000 Blackwell 96 GB | 110 | 27 | 128K | 93.8 GB |
| DGX Spark (GB10) 128 GB unified | 127 | 31 | 128K | 107 GB |
| H200 141 GB | 165 | 41 | 128K | 138 GB |
| B200 180 GB | 212 | 53 | 128K | 176 GB |
| Requests at once | 8K tokens each | 32K tokens each |
|---|---|---|
| 1 | 2.8 GB | 5.2 GB |
| 5 | 6.0 GB | 18.1 GB |
| 8 | 8.4 GB | 27.8 GB |
| 16 | 14.9 GB | 53.5 GB |
| 32 | 27.8 GB | 105 GB |
| 64 | 53.5 GB | 208 GB |
One card, with vLLM's small-card settings.
From the model card
The UDOP model was proposed in Unifying Vision, Text, and Layout for Universal Document Processing by Zineng Tang, Ziyi Yang, Guoxin Wang, Yuwei Fang, Yang Liu, Chenguang Zhu, Michael Zeng, Cha Zhang, Mohit Bansal.
UDOP adopts an encoder-decoder Transformer architecture based on T5 for document AI tasks like document image classification, document parsing and document visual question answering.
You can use the model for document image classification, document parsing and document visual question answering (DocVQA).
Here's how to use the model on a document image:
from transformers import AutoProcessor, UdopForConditionalGeneration
from datasets import load_dataset
# load model and processor
# in this case, we already have performed OCR ourselves
# so we initialize the processor with `apply_ocr=False`
processor = AutoProcessor.from_pretrained("microsoft/udop-large", apply_ocr=False)
model = UdopForConditionalGeneration.from_pretrained("microsoft/udop-large")
# load an example image, along with the words and coordinates
# which were extracted using an OCR engine
dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
example = dataset[0]
image = example["image"]
words = example["tokens"]
boxes = example["bboxes"]
question = "Question answering. What is the date on the form?"
# prepare everything for the model
encoding = processor(image, question, words, boxes=boxes, return_tensors="pt")
# autoregressive generation
predicted_ids = model.generate(**encoding)
print(processor.batch_decode(predicted_ids, skip_special_tokens=True)[0])
9/30/92
Refer to the demo notebooks for fine-tuning/inference.
@misc{tang2023unifying,
title={Unifying Vision, Text, and Layout for Universal Document Processing},
author={Zineng Tang and Ziyi Yang and Guoxin Wang and Yuwei Fang and Yang Liu and Chenguang Zhu and Michael Zeng and Cha Zhang and Mohit Bansal},
year={2023},
eprint={2212.02623},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
https://huggingface.co/microsoft/udop-large/blob/main/data_summary_card.md
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.