Read the full article on DataCamp: LLaMA 4 With vLLM – A Guide With Demo Project

Learn how to deploy and use Meta’s LLaMA 4 Scout with vLLM on RunPod for both text completion and multimodal inference.

Overview

Meta’s latest models, LLaMA 4 Scout and Maverick, offer long-context, multimodal understanding, and efficient inference. Paired with vLLM, a high-throughput inference engine, you can deploy these models using OpenAI-compatible APIs on GPUs like H100.

In this tutorial, you’ll learn to:

  • 🚀 Deploy LLaMA 4 on RunPod
  • 💬 Run a local chat interface with multi-turn support
  • 🖼 Perform multimodal inference (text + image) using vLLM

Why Use LLaMA 4 on vLLM?

vLLM is a high-performance LLM engine featuring:

  • ✅ Efficient PagedAttention memory
  • 🖼 Multimodal + long-context (up to 10M tokens)
  • 🔄 OpenAI-compatible API
  • 🧠 Supports multi-GPU scaling (tensor + memory parallelism)

Hosting LLaMA 4 Scout on RunPod

Step 1: Set Up RunPod

  • Log in at RunPod.io
  • Add $25+ to your balance

Step 2: Deploy a Multi-GPU Pod

  • Select 4x H100 NVL GPUs (>=24 GB VRAM)
  • Choose PyTorch 2.4.0
  • Set Container + Volume Disk to 1000 GB
  • (Optional) Add Hugging Face token for auto-download

Step 3: Connect to the Pod

Use either:

  • JupyterLab Terminal
  • SSH or HTTP

Step 4: Install vLLM and Libraries

pip install -U vllm
pip install transformers accelerate pillow

Step 5: Launch LLaMA 4 on vLLM

VLLM_DISABLE_COMPILE_CACHE=1 vllm serve meta-llama/Llama-4-Scout-17B-16E-Instruct \
--tensor-parallel-size 4 \
--max-model-len 100000 \
--override-generation-config='{"attn_temperature_tuning": true}'

✅ Your API is now running on port 8000!

Text Completion With LLaMA 4 Scout

Step 1: Install and Import SDK

pip install openai colorama

from openai import OpenAI
from colorama import Fore, Style, init

Step 2: Initialize OpenAI-Compatible Client

init(autoreset=True)
client = OpenAI(
    api_key="EMPTY",
    base_url="http://localhost:8000/v1"
)

Step 3: Start Chat Interface

messages = [{"role": "system", "content": "You are a helpful assistant."}]
while True:
    user_input = input("User: ")
    if user_input.lower() in ["exit", "quit"]: break
    messages.append({"role": "user", "content": user_input})
    chat_response = client.chat.completions.create(
        model="meta-llama/Llama-4-Scout-17B-16E-Instruct",
        messages=messages
    )
    assistant = chat_response.choices[0].message.content
    print("Assistant:", assistant)
    messages.append({"role": "assistant", "content": assistant})

Multimodal Inference With LLaMA 4 Scout

Step 1: Install and Import

pip install openai

from openai import OpenAI

Step 2: Connect Client

client = OpenAI(
    api_key="EMPTY",
    base_url="http://localhost:8000/v1"
)

Step 3: Submit Multimodal Prompt

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image_url", "image_url": {"url": image_url1}},
            {"type": "text", "text": "Can you describe what's in this image?"}
        ]
    }
]
chat_response = client.chat.completions.create(
    model="meta-llama/Llama-4-Scout-17B-16E-Instruct",
    messages=messages,
)
print("Response:", chat_response.choices[0].message.content)

📸 This will process image + text input and return a grounded visual answer!

Conclusion

In this guide, we:

  • 🚀 Deployed LLaMA 4 Scout on RunPod with vLLM
  • 💬 Built a terminal-based chatbot
  • 🖼 Ran a multimodal Q&A demo
  • 🧪 Explored OpenAI-compatible endpoints

LLaMA 4 with vLLM is ideal for:

  • Long-context reasoning
  • Multimodal assistants
  • Cost-effective GPU deployment

Learn More