Model reference · open weights

udop-large

LLMs microsoft Vision + text 1 build Open weights 93k dl/mo

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.

  • udop-large is a 742M parameter image-text-to-text model developed by Microsoft for document AI tasks.
  • It uses an encoder-decoder Transformer architecture based on T5 to perform document image classification, document parsing, and document visual question answering.
  • The model is available under the MIT license.

Summary of the microsoft/udop-large model card, 2026-10-01

What it is

Released bymicrosoft
Released2024-02-26
Parameters742M
VRAM1.5 GB for the weights

What it runs on

Memory and cards for udop-large (FP32)

1.5 GBweights, file size
98 MBcache per 1K tokens
514 MBruntime overhead, at least
CardRequests at onceContext maxMemory
8K each32K each
RTX 3060 12 GB11295K11.6 GB
RTX 4060 Ti 16 GB164128K15.4 GB
RTX 3090 24 GB266128K23.4 GB
RTX 4090 24 GB266128K23.4 GB
RTX 5090 32 GB369128K31.0 GB
L40S 48 GB5213128K44.0 GB
A100 80 GB9423128K78.2 GB
H100 80 GB9122128K78.1 GB
RTX PRO 6000 Blackwell 96 GB11027128K93.8 GB
DGX Spark (GB10) 128 GB unified12731128K107 GB
H200 141 GB16541128K138 GB
B200 180 GB21253128K176 GB
Memory needed at each load
Requests at once8K tokens each32K tokens each
12.8 GB5.2 GB
56.0 GB18.1 GB
88.4 GB27.8 GB
1614.9 GB53.5 GB
3227.8 GB105 GB
6453.5 GB208 GB

One card, with vLLM's small-card settings.

From the model card

What microsoft says about udop-large

Read 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.

Model description

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.

Intended uses & limitations

You can use the model for document image classification, document parsing and document visual question answering (DocVQA).

How to use

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.

BibTeX entry and citation info

@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}
}

Data Summary

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.

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