Gradio vs Streamlit for AI Demos on GPU

You need a web interface for your GPU-hosted model and the two dominant options are Gradio and Streamlit. Both let you build interactive AI demos in Python without frontend expertise, but they approach the problem differently. This guide compares both frameworks on a dedicated GPU server so you can pick the right tool for your deployment.

Framework Overview

Gradio was built specifically for machine learning demos. Streamlit targets broader data applications. That origin shapes every design decision in both frameworks.

Gradio: Quick Model Interface

Gradio excels at wrapping a model function in a web UI with minimal code. You define inputs, outputs, and the function that connects them. The framework handles the rest.

import gradio as gr
from transformers import pipeline

generator = pipeline("text-generation", model="meta-llama/Llama-3-8B-Instruct", device=0)

def generate(prompt, max_tokens):
    result = generator(prompt, max_new_tokens=int(max_tokens))
    return result[0]["generated_text"]

demo = gr.Interface(
    fn=generate,
    inputs=[gr.Textbox(label="Prompt"), gr.Slider(50, 500, value=200, label="Max Tokens")],
    outputs=gr.Textbox(label="Output"),
    title="LLaMA 3 Demo"
)
demo.launch(server_name="0.0.0.0", server_port=7860)

That is a complete, working demo. For production deployment patterns with vLLM as the backend, see our Gradio deployment guide.

Streamlit: Data-Centric AI Apps

Streamlit treats your Python script as the app. It reruns the script on each interaction, managing state through session variables. This model works well for dashboards that combine model inference with data visualisation.

import streamlit as st
from transformers import pipeline

@st.cache_resource
def load_model():
    return pipeline("text-generation", model="meta-llama/Llama-3-8B-Instruct", device=0)

generator = load_model()

st.title("LLaMA 3 Demo")
prompt = st.text_area("Prompt")
max_tokens = st.slider("Max Tokens", 50, 500, 200)

if st.button("Generate"):
    with st.spinner("Running inference..."):
        result = generator(prompt, max_new_tokens=max_tokens)
        st.write(result[0]["generated_text"])

The @st.cache_resource decorator ensures the model loads once and persists across reruns. Without it, Streamlit would reload the model on every interaction — a costly mistake on GPU servers.

Streaming and Real-Time Inference

Token-by-token streaming is essential for LLM demos. Gradio handles streaming with Python generators natively. Streamlit added streaming support later, and it works well for text but is less flexible for custom output types.

For high-throughput streaming against a vLLM production endpoint, Gradio’s event-driven architecture processes concurrent requests more efficiently. Streamlit’s rerun model means each user session is single-threaded by default.

If your demo serves multiple concurrent users, Gradio’s queue system handles backpressure gracefully. Streamlit requires additional infrastructure like Redis queues to manage concurrent inference requests at scale.

Deployment on GPU Servers

Both frameworks run as standard Python processes. On a dedicated GPU server, deployment is straightforward for either choice:

# Gradio
pip install gradio transformers torch
python app.py

# Streamlit
pip install streamlit transformers torch
streamlit run app.py --server.port 8501 --server.address 0.0.0.0

Place either behind Nginx as a reverse proxy for TLS termination. For building a complete inference API alongside your demo, see the FastAPI inference server guide. Both frameworks integrate with self-hosted LLMs through HTTP endpoints or direct Python imports.

Which to Choose

Choose Gradio when your primary goal is a model demo. It is faster to set up, has better ML-specific components (image classifiers, audio players, chatbot UIs), and handles concurrent users with its built-in queue. Hugging Face Spaces uses Gradio as its default, so your demo is portable. Check our tutorials section for more deployment patterns.

Choose Streamlit when your application combines model inference with data exploration, charting, or dashboarding. Its layout system, native plotting support, and session state management make it the stronger choice for internal tools and analytics-heavy AI applications. See the Streamlit deployment guide for GPU-specific configuration.

Both work well with vLLM and Ollama backends. The framework choice is about the interface you need, not the model you run.