In a landscape crowded with document AI tools, MonkeyOCR is taking its spot as a solution designed for serious document parsing tasks, especially when accuracy, structure, and speed matter. It is built on a novel Structure-Recognition-Relation (SRR) triplet paradigm, and redefines document understanding by replacing clunky multi-stage pipelines with a simple, unified model that achieves both performance and efficiency. It performs very well across diverse document types, from complex formulas and dense tables to multi-page text-heavy PDFs, in both Chinese and English. With a lightweight 3B parameter count, it outperforms heavyweight competitors like Gemini 2.5 Pro and Qwen2.5 VL-72B on average English document performance, while being significantly faster than models like MinerU and Qwen2.5 VL-7B in real-world multi-page scenarios. MonkeyOCR also shines with a remarkable +15% gain in formula parsing and +8.6% in table accuracy compared to modular systems, making it an essential tool for researchers, businesses, and developers handling structured documents at scale.
If you’re deploying locally for private workloads or in the cloud for scalable document intelligence, MonkeyOCR is easy to set up and blazing fast in execution. This guide will walk you through installing MonkeyOCR both locally and on cloud platforms, so you can start parsing data right away.
Prerequisites
The minimum system requirements for running this model are:
- GPU: 1x A100 SXM or 2x RTXA6000
- Storage: 50 GB (preferable)
- VRAM: at least 56GB
- Anaconda installed
Step-by-step process to install and run MonkeyOCR
For the purpose of this tutorial, we’ll use a GPU-powered Virtual Machine by NodeShift since it provides high compute Virtual Machines at a very affordable cost on a scale that meets GDPR, SOC2, and ISO27001 requirements. Also, it offers an intuitive and user-friendly interface, making it easier for beginners to get started with Cloud deployments. However, feel free to use any cloud provider of your choice and follow the same steps for the rest of the tutorial.
Step 1: Setting up a NodeShift Account
Visit app.nodeshift.com and create an account by filling in basic details, or continue signing up with your Google/GitHub account.
If you already have an account, login straight to your dashboard.
Step 2: Create a GPU Node
After accessing your account, you should see a dashboard (see image), now:
- Navigate to the menu on the left side.
- Click on the GPU Nodes option.
- Click on Start to start creating your very first GPU node.
These GPU nodes are GPU-powered virtual machines by NodeShift. These nodes are highly customizable and let you control different environmental configurations for GPUs ranging from H100s to A100s, CPUs, RAM, and storage, according to your needs.
Step 3: Selecting configuration for GPU (model, region, storage)
- For this tutorial, we’ll be using 1x A100 GPU, however, you can choose any GPU as per the prerequisites.
- Similarly, we’ll opt for 100GB storage by sliding the bar. You can also select the region where you want your GPU to reside from the available ones.
Step 4: Choose GPU Configuration and Authentication method
- After selecting your required configuration options, you’ll see the available GPU nodes in your region and according to (or very close to) your configuration. In our case, we’ll choose a 1x A100 80GB GPU node with 32vCPUs/131GB RAM/100GB SSD.
2. Next, you’ll need to select an authentication method. Two methods are available: Password and SSH Key. We recommend using SSH keys, as they are a more secure option. To create one, head over to our official documentation.
Step 5: Choose an Image
The final step is to choose an image for the VM, which in our case is Nvidia Cuda.
That’s it! You are now ready to deploy the node. Finalize the configuration summary, and if it looks good, click Create to deploy the node.
Step 6: Connect to active Compute Node using SSH
- As soon as you create the node, it will be deployed in a few seconds or a minute. Once deployed, you will see a status Running in green, meaning that our Compute node is ready to use!
- Once your GPU shows this status, navigate to the three dots on the right, click on Connect with SSH, and copy the SSH details that appear.
As you copy the details, follow the below steps to connect to the running GPU VM via SSH:
- Open your terminal, paste the SSH command, and run it.
2. In some cases, your terminal may take your consent before connecting. Enter ‘yes’.
3. A prompt will request a password. Type the SSH password, and you should be connected.
Output:
Next, If you want to check the GPU details, run the following command in the terminal:
!nvidia-smi
Step 7: Set up the project environment with dependencies
- Create a virtual environment using Anaconda.
conda create -n monkey python=3.10 -y && conda activate monkey
Output:
2. Clone the official repository of facebookresearch/vjepa2 and move inside the project directory.
git clone https://github.com/Yuliang-Liu/MonkeyOCR.git && cd MonkeyOCR
Output:
3. Once you’re inside the environment, install necessary dependencies to run the model.
pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu124
pip install .
pip install huggingface_hub gradio==5.23.3 pdf2image==1.17.0 lmdeploy==0.8.0
Output:
4. Also, install libgl1 if not present in the system.
sudo apt update
sudo apt install -y libgl1
5. Login to Hugging Face CLI with HF READ access token.
(Enter your HF READ access token when prompted)
huggingface-cli login
Output:
Step 8: Download and Run the model
- Download the model checkpoints.
python tools/download_model.py
Output:
2. Once the checkpoints are downloaded, we’ll connect our local VSCode editor to the remote server to upload a sample image for testing the OCR in the project root folder.
If you’re using a GPU through a remote server (e.g., NodeShift), you can connect it to your visual studio code editor by following the steps below:
a) Install the “Remote-SSH” Extension by Microsoft on VS Code.
b) Type “Remote-SSH: Connect to Host” on the Command Palette.
c) Click on “Add a new host”.
d) Enter the host details, such as username and SSH password, and you should be connected.
3. Upload the test image on the root of the project folder.
4. Run the model to parse the image.
python parse.py ./monkey-ocr-test.jpg
Output:
Original Test Image:
Once the parsing is complete, you will see the following files in the output/filename folder:
A PDF with Layout detection:
Layout extracted in a JSON file:
Finally, the text content within a Markdown file:
Conclusion
MonkeyOCR delivers a powerful blend of speed, accuracy, and structural understanding through its SRR triplet paradigm, outperforming traditional pipelines and even large multimodal models in parsing diverse document types. If you’re handling complex tables, formulas, or multi-page PDFs, MonkeyOCR offers a unified and efficient approach that scales with your needs. With NodeShift, deploying MonkeyOCR becomes even more seamless, offering a reliable, GPU-accelerated environment that replaces the friction of traditional cloud setups with intuitive developer friendly few-click cloud/on-prem setup and ensures smooth, high-performance document parsing at scale.
