Build Your First App with vLLM in 2025: 5-Step Guide

Ever felt that rush of excitement building a new AI feature, only to be slowed down by sluggish model inference? You're not alone. As Large Language Models (LLMs) become more powerful, serving them efficiently has become the biggest bottleneck. But what if you could achieve near-optimal throughput with just a few lines of code? Welcome to the world of vLLM.

In this guide, we'll cut through the complexity and show you how to build your very first, blazing-fast LLM-powered application using vLLM and FastAPI in just five simple steps. Let's get building!

What is vLLM and Why Should You Care?

vLLM is a high-throughput and memory-efficient serving engine for LLMs. Developed by researchers at UC Berkeley, it's designed to solve the performance puzzle of LLM inference. Its secret sauce? PagedAttention.

Think of traditional memory management for LLMs like booking an entire hotel floor for a single guest—it's wasteful. PagedAttention, inspired by virtual memory and paging in operating systems, allocates memory in much smaller, non-contiguous blocks or "pages." This allows vLLM to:

• Drastically reduce memory waste: It can pack more requests onto a single GPU.
• Increase throughput: By managing memory so efficiently, it can process many more requests per second compared to standard frameworks like Hugging Face's `transformers`.
• Simplify batching: It intelligently batches incoming requests without complex padding.

For developers, this means faster response times, lower hosting costs, and the ability to serve more users with the same hardware. It's a game-changer.

Before We Begin: Prerequisites

Before we dive in, make sure your system is ready. You'll need:

• Python 3.8+
• An NVIDIA GPU with a compute capability of 7.0+ (e.g., V100, T4, A10G, A100, H100).
• CUDA 11.8 or newer installed. vLLM relies heavily on the GPU, so this is non-negotiable.
• pip for package management.

Step 1: Setting Up Your Environment

First things first, let's create a clean project directory and a virtual environment. This keeps our dependencies tidy.

mkdir my-vllm-app
cd my-vllm-app
python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

Now, installing vLLM is as simple as a single pip command. The library is built to be incredibly easy to integrate.

pip install vllm

And that's it! The core engine is now installed in your environment.

Step 2: Choosing Your Large Language Model (LLM)

vLLM integrates seamlessly with the Hugging Face Hub, giving you access to thousands of pre-trained models. For your first app, it's best to start with a well-supported and reasonably sized model.

Some great choices for 2025 include:

• meta-llama/Meta-Llama-3-8B-Instruct: A powerful and popular instruction-tuned model.
• mistralai/Mistral-7B-Instruct-v0.2: Known for its excellent performance-to-size ratio.
• google/gemma-7b-it: Google's latest family of open models.

For this guide, we'll use mistralai/Mistral-7B-Instruct-v0.2 because it's powerful yet manageable for a wide range of modern GPUs.

The Core Logic - Your First vLLM Script

Let's write a simple Python script to see vLLM in action. This script will load the model, define a few prompts, and generate responses.

Create a file named test_vllm.py:

from vllm import LLM, SamplingParams

# 1. Define a list of prompts to send to the model
prompts = [
    "Hello, my name is",
    "The president of the United States is",
    "The capital of France is",
    "The future of AI is",
]

# 2. Create SamplingParams to control the generation process
# Here, we'll use temperature for creativity and max_tokens to limit output length.
sampling_params = SamplingParams(temperature=0.7, max_tokens=50)

# 3. Initialize the LLM with the model you chose
# vLLM will automatically download the model from Hugging Face if it's not cached
print("Loading the LLM...")
llm = LLM(model="mistralai/Mistral-7B-Instruct-v0.2")
print("LLM loaded successfully!")

# 4. Generate the outputs
# vLLM automatically handles batching these prompts for maximum efficiency
outputs = llm.generate(prompts, sampling_params)

# 5. Print the results
for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"--- \nPrompt: {prompt!r}")
    print(f"Generated: {generated_text!r}\n---")

Run this script from your terminal:

python test_vllm.py

You'll see vLLM download the model (if it's your first time using it) and then quickly print the generated text for all four prompts. Notice how you just passed a list of prompts, and vLLM handled the complex task of batching them behind the scenes. That's the power of an optimized serving engine!

A Quick Detour: vLLM vs. The Competition

Before we build the API, it's helpful to see where vLLM stands. Why not just use a standard `transformers` pipeline?

For a production application where performance and cost are critical, vLLM is often the top choice due to its superior throughput.

Step 4: Wrapping it in an API with FastAPI

A script is great, but a real application needs an interface. We'll use FastAPI, a modern, high-performance web framework for Python, to create a simple API endpoint that accepts a prompt and returns a completion.

First, install FastAPI and the Uvicorn server:

pip install fastapi "uvicorn[standard]" pydantic

Now, create a file named `main.py`. We'll load the LLM once when the application starts to avoid reloading it on every request.

from fastapi import FastAPI
from pydantic import BaseModel
from vllm import LLM, SamplingParams

# --- Application Setup ---
app = FastAPI(
    title="My First vLLM App",
    description="A simple API to interact with a powerful LLM via vLLM.",
    version="1.0.0",
)

# --- Model Loading ---
# Load the LLM and sampling parameters globally on startup
# This ensures the model is ready to go as soon as the app starts.
print("Loading the LLM...")
llm = LLM(model="mistralai/Mistral-7B-Instruct-v0.2", trust_remote_code=True)
sampling_params = SamplingParams(temperature=0.7, top_p=0.95, max_tokens=256)
print("LLM loaded successfully!")


# --- API Data Models ---
class GenerationRequest(BaseModel):
    prompt: str

class GenerationResponse(BaseModel):
    text: str


# --- API Endpoints ---
@app.post("/generate", response_model=GenerationResponse)
def generate_text(request: GenerationRequest):
    """Accepts a prompt and returns the model's generated text."""
    
    # Use the globally loaded LLM to generate text
    # We wrap the single prompt in a list as vLLM expects a batch
    outputs = llm.generate([request.prompt], sampling_params)
    
    # Extract the generated text from the first (and only) output
    generated_text = outputs[0].outputs[0].text
    
    return GenerationResponse(text=generated_text)

@app.get("/")
def read_root():
    return {"message": "vLLM API is running. Go to /docs for more info."}

In this code, we define a `/generate` endpoint that accepts a JSON object with a `prompt` field. It feeds this prompt to our globally loaded `llm` instance and returns the result.

Step 5: Running and Testing Your App

It's time to bring your app to life! Run the following command in your terminal:

uvicorn main:app --host 0.0.0.0 --port 8000

Your API is now running! You can open your browser to http://localhost:8000/docs to see the interactive Swagger UI documentation that FastAPI automatically generates.

To test it from the command line, open a new terminal and use `curl`:

curl -X POST "http://localhost:8000/generate" \
-H "Content-Type: application/json" \
-d '{"prompt": "Explain the concept of PagedAttention in one sentence."}'

You should receive a JSON response like this (text will vary):

{
  "text": " PagedAttention is a memory management technique for large language models that allocates GPU memory in non-contiguous, fixed-size blocks called pages, similar to how virtual memory works in an operating system, to reduce memory fragmentation and increase throughput."
}

Congratulations! You've just built and deployed a high-performance LLM application. You're now serving one of the world's most advanced open-source models with industry-leading speed.

Key Takeaways & Next Steps

You've covered a lot of ground in a short time. Let's recap what makes this process so powerful:

• Performance is Key: vLLM's PagedAttention architecture is the magic behind its incredible speed and efficiency.
• Simplicity Wins: You went from zero to a deployed API with just two Python files and a few `pip install` commands.
• FastAPI is the Perfect Partner: Combining vLLM's inference speed with FastAPI's web performance creates a robust, production-ready stack.

So, what's next?

1. Explore Sampling Parameters: Dive into other options in `SamplingParams`, like `top_p`, `top_k`, and `presence_penalty`, to control the generation output more finely.
2. Containerize Your App: Use Docker to package your application for easy deployment on any cloud provider.
3. Try Different Models: Experiment with other models from the Hugging Face Hub to see how they perform for your specific use case.

You now have the foundational knowledge to build and deploy sophisticated AI applications. The world of high-performance LLM serving is at your fingertips. Happy coding!