Deploy an ML Model on Hugging Face Spaces with GPU 🚀

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Introduction

In this tutorial, you’ll learn how to deploy a machine learning model on Hugging Face Spaces using a GPU for enhanced performance. This is crucial as of 2026, given the rise in computational demands and the need for sustainable practices in deploying ML models, as highlighted by studies such as “Exploring the Carbon Footprint of Hugging Face’s ML Models” (ArXiv). By leveraging GPUs, you can significantly improve model inference speed without compromising on accuracy or environmental impact.

Prerequisites

Before we begin, ensure that your development environment is set up with the following:

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  • Python 3.10+
  • transformers [8] library version 4.26+ (latest as of January 2026)
  • torch library version 1.12+ (compatible with CUDA for GPU support)
  • Hugging Face Hub CLI version 0.9.0+

Install these packages using pip:

pip install transformers torch==1.12.0+cu113 -f https://download.pytorch [6].org/whl/torch_stable.html --upgrade
pip install huggingface [8]_hub==0.9.0

Step 1: Project Setup

To get started, create a new directory for your project and navigate into it:

mkdir hf_spaces_deploy
cd hf_spaces_deploy

Next, initialize a Python virtual environment to manage dependencies:

python3 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip

Step 2: Core Implementation

Firstly, you’ll need to authenticate your Hugging Face Hub account. Run the following command and follow the prompts to log in:

huggingface_hub login

Now, let’s write a Python script that loads an ML model from Hugging Face Model Hub, configures it for GPU inference if available, and defines how to receive input data.

import torch
from transformers import pipeline

def deploy_model_on_spaces():
    # Load a pre-trained NLP model optimized for inference (e.g., BERT)
    nlp_task = "text-classification"
    
    # Initialize the model with GPU support if available
    device_id = 0 if torch.cuda.is_available() else -1
    classifier = pipeline(task=nlp_task, model="distilbert-base-uncased-finetuned-sst-2-english", device=device_id)
    
    print(f"Model loaded on device: {torch.device('cuda' if device_id == 0 else 'cpu')}")
    
    # Example prediction
    sample_text = "I really enjoyed the movie!"
    result = classifier(sample_text)[0]
    return result

if __name__ == "__main__":
    output = deploy_model_on_spaces()
    print(f"Prediction: {output['label']}, Confidence Score: {output['score']*100:.2f}%")

Step 3: Configuration

Before deploying, configure your Hugging Face Spaces settings:

huggingface_hub space create YOUR_USERNAME/YOUR_PROJECT_NAME --dockerfile "FROM python:3.8-slim\nRUN pip install transformers torch==1.12.0+cu113"

Ensure to update YOUR_USERNAME and YOUR_PROJECT_NAME. This command sets up a Docker container with the necessary libraries.

Step 4: Running the Code

Deploy your application on Hugging Face Spaces using:

huggingface_hub space push --local ./ --remote YOUR_USERNAME/YOUR_PROJECT_NAME

Once pushed, navigate to your project URL (e.g., https://YOUR_USERNAME.hf.space/) and test it by inputting some text. Your model should return predictions with high accuracy due to GPU acceleration.

Step 5: Advanced Tips

  • Environment Optimization: Ensure all dependencies are optimized for performance and security.
  • Model Efficiency: Use quantization or other techniques to reduce the memory footprint without significant loss in quality.
  • Continuous Monitoring: Regularly monitor your deployed model’s performance using logging and monitoring tools like Prometheus.

Results

Upon completion, you should have a live, GPU-accelerated ML model hosted on Hugging Face Spaces. Your model will be accessible via an endpoint URL where users can interact with it directly or integrate it into other applications for text classification tasks.

Going Further

  • Explore Model Variants: Experiment with different models available in the Hugging Face Hub.
  • Security Best Practices: Learn about securing your ML deployments from threats as discussed in “A Large-Scale Exploit Instrumentation Study of AI/ML Supply Chain Attacks”.
  • Documentation and Community Engagement: Participate in discussions on forums like GitHub issues or StackOverflow to help other developers.

Conclusion

In this tutorial, you learned how to deploy an efficient and scalable ML model using Hugging Face Spaces with GPU support. This setup not only enhances performance but also promotes sustainable practices by optimizing computational resources.

📚 References & Sources

Research Papers

  1. arXiv - HuggingFace’s Transformers: State-of-the-art Natural Languag - Arxiv. Accessed 2026-01-18.
  2. arXiv - Exploring the Carbon Footprint of Hugging Face’s ML Models: - Arxiv. Accessed 2026-01-18.

Wikipedia

  1. Wikipedia - PyTorch - Wikipedia. Accessed 2026-01-18.
  2. Wikipedia - Hugging Face - Wikipedia. Accessed 2026-01-18.
  3. Wikipedia - Transformers - Wikipedia. Accessed 2026-01-18.

GitHub Repositories

  1. GitHub - pytorch/pytorch - Github. Accessed 2026-01-18.
  2. GitHub - huggingface/transformers - Github. Accessed 2026-01-18.
  3. GitHub - huggingface/transformers - Github. Accessed 2026-01-18.
  4. GitHub - Shubhamsaboo/awesome-llm-apps - Github. Accessed 2026-01-18.

All sources verified at time of publication. Please check original sources for the most current information.