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What is vLLM? How to Speed Up LLM Serving by 24x

vLLM is an open-source library designed to speed up the process of serving Large Language Models (LLMs) by up to 24 times compared to standard methods. It achieves this primarily through PagedAttention, a technique that manages memory more effectively so your hardware can handle multiple user requests at once. By using vLLM, you can run high-performance models like Llama 4 on your own server with significantly lower latency (the time it takes for a model to respond).

Why is vLLM faster than other tools?

The secret to vLLM's speed is how it handles the KV Cache (Key-Value Cache—a temporary storage area for data the model needs to remember while generating a sentence). In traditional setups, this memory is often wasted because the system reserves large blocks of space that stay empty. vLLM treats this memory like a computer's RAM (Random Access Memory), breaking it into small pieces and filling them only as needed.

This approach allows vLLM to fit more data into your GPU (Graphics Processing Unit—the specialized chip that runs AI models). Because the memory is packed tightly, the system can process many prompts simultaneously without slowing down. We have found that this specific memory management is what makes vLLM the industry standard for self-hosting AI.

What do you need to get started?

Before you run your first model, you need a specific environment. vLLM requires a Linux-based operating system and a GPU with sufficient VRAM (Video RAM—the memory on your graphics card).

What You'll Need:

  • Linux OS: Ubuntu 22.04 or later is recommended.
  • Python 3.14+: The current stable version of the Python programming language.
  • CUDA 12.4+: A platform that allows your GPU to perform general-purpose computing.
  • NVIDIA GPU: An Ampere architecture card (like an RTX 3090 or A100) or newer is ideal.

How do you install vLLM?

Installation is straightforward using the Python package manager. It is best practice to use a virtual environment (a private workspace for your project so you don't mess up your computer's main settings).

Step 1: Create a virtual environment Open your terminal and type the following:

# Create a new environment named 'vllm_env'
python3 -m venv vllm_env

# Activate the environment
source vllm_env/bin/activate

What you should see: Your terminal prompt should now show (vllm_env) at the beginning.

Step 2: Update your package installer

# Ensure pip is up to date
pip install --upgrade pip

Step 3: Install the vLLM library

# This will download vLLM and its required parts
pip install vllm

What you should see: A long list of progress bars as the library and its dependencies (helper programs) are installed.

How do you run your first model?

Once installed, you can start an "OpenAI-compatible" server. This means you can use the same code that works for GPT-5 to talk to your own local model. For this example, we will use a small-parameter version of Llama 4.

Step 1: Start the server Run this command in your terminal:

# --model specifies which AI to download
# --dtype half reduces memory usage without losing quality
python -m vllm.entrypoints.openai.api_server --model meta-llama/Llama-4-8B --dtype half

What you should see: The terminal will show "Uvicorn running on http://0.0.0.0:8000". This means your server is live.

Step 2: Send a request Open a second terminal window and use curl (a tool for sending data over the internet) to ask the model a question:

curl http://localhost:8000/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "meta-llama/Llama-4-8B",
        "messages": [{"role": "user", "content": "What is the capital of France?"}]
    }'

What you should see: A JSON (JavaScript Object Notation—a standard data format) response containing the text "The capital of France is Paris."

What are the common gotchas?

Running local AI can be tricky at first. It is normal to run into "Out of Memory" (OOM) errors if your GPU isn't large enough for the model you chose.

  • GPU Memory Errors: If you see an OOM error, try a smaller model or use the --max-model-len flag to limit how much text the model processes at once.
  • Hugging Face Access: Many models like Llama 4 require you to agree to a license on Hugging Face (a website where AI models are stored). You will need to run huggingface-cli login in your terminal and provide an API key.
  • Slow First Run: The very first time you run a model, vLLM has to download several gigabytes of data. Don't worry if it seems stuck; it is just fetching the model files from the internet.

How does PagedAttention work?

To understand why this library is special, imagine a restaurant where every table must be reserved for 4 people, even if only 1 person shows up. That is how traditional AI serving works—it wastes a lot of "seating" space.

PagedAttention works like a modern host who can seat people at any individual chair available. It breaks the "conversation" into small pages. If a sentence only needs 10 tokens (units of text), it only takes 10 tokens worth of memory. This efficiency is why vLLM can handle many more users on the same hardware compared to older tools.

Next Steps

Now that you have a basic server running, you can explore more advanced features. You might want to try "Quantization" (a way to compress models so they fit on cheaper hardware) or look into "LoRA adapters" (custom plugins that give your AI specific personalities).

To dive deeper into the technical settings and more advanced deployment options, check out the official vLLM documentation.

Read the Vllm Documentation