nemotron-parse Overview

nemotron-parse is a general purpose text-extraction model, specifically designed to handle documents. Given an image, nemotron-parse is able to extract formatted-text, with bounding-boxes and the corresponding semantic class. This has downstream benefits for several tasks such as increasing the availability of training-data for Large Language Models (LLMs), improving the accuracy of retriever systems, and enhancing document understanding pipelines.

This model is ready for commercial use.

License

GOVERNING TERMS: This trial service is governed by the NVIDIA API Trial Terms of Service. Use of this model is governed by the NVIDIA Community Model License.Use of the tokenizer included in this model is governed by the CC-BY-4.0 license.

Deployment Geography:

Global

Use Case:

nemotron-parse will be capable of comprehensive text understanding and document structure understanding. It will be used in retriever and curator solutions. Its text extraction datasets and capabilities will help with LLM and VLM training, as well as improve run-time inference accuracy of VLMs. The nemotron-parse model will perform text extraction from PDF and PPT documents. The nemotron-parse can classify the objects (title, section, caption, index, footnote, lists, tables, bibliography, image) in a given document, and provide bounding boxes with coordinates.

Release Date:

October 28, 2025

References

• https://huggingface.co/docs/transformers/en/model_doc/mbart

Model Architecture

Architecture Type :

Transformer-based vision-encoder-decoder model

Network Architecture

• Vision Encoder: ViT-H model (https://huggingface.co/nvidia/C-RADIO)
  
• Adapter Layer: 1D convolutions & norms to compress dimensionality and sequence length of the latent space (1280 tokens to 320 tokens)
  
• Decoder: mBart [1] 10 blocks
  
• Tokenizer: Use of the tokenizer included in this model is governed by the CC-BY-4.0 license
  
• Number of Parameters: <1B
  

Computational Load (For NVIDIA Models Only)

Cumulative Compute: 1.4e+21
Estimated Energy and Emissions for Model Training: Energy Consumption: 6200 kWh
Carbon Emissions: 2.55

Input

• Input Type: Image, Text
  
• Input Type(s): Red, Green, Blue (RGB) + Prompt (String)
• Input Parameters: 2D, 1D

• Other Properties Related to Input:
  • Max Input Resolution (Width, Height): 1648, 2048
  • Min Input Resolution (Width, Height): 1024, 1280
• Channel Count: 3

Output

• Output Type: Text
  
• Output Format: String
• Output Parameters: 1D

• Other Properties Related to Output:
  • nemotron-parse output format is a string which encodes text content (formatted or not) as well as bounding boxes and class attributes.
    

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

Software Integration:

Runtime Engine(s): TensorRT-LLM

Supported Hardware Microarchitecture Compatibility:
NVIDIA Hopper/NVIDIA Ampere/NVIDIA Turing

Supported Operating System(s): Linux

The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.

Model Version:

nemotron-parse: As part of this first release, we share the set of weights named overjoyed-adder.

Training, Testing, and Evaluation Datasets:

Training Dataset

nemotron-parse is first pre-trained on our internal datasets: human, synthetic and automated. Data Modality: *Text *Image
Data Collection Method by Dataset: Hybrid: Human, Synthetic, Automated Labeling Method by Dataset: Hybrid: Human, Synthetic, Automated

Testing and Evaluation Dataset:

nemotron-parse is evaluated on multiple datasets for robustness, including public and internal dataset. Data Collection Method by Dataset: Hybrid: Human, Synthetic, Automated Labeling Method by Dataset: Hybrid: Human, Synthetic, Automated

Inference

Runtime Engine(s): TensorRT-LLM

Test Hardware: NVIDIA H100

Ethical Considerations

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