How to Use Llama 4 Maverick — Locally, via API, or on Cloud GPUs

Llama 4 Maverick is Meta’s latest open-source large language model, released on April 5, 2025. Built with a 128-Mixture-of-Experts (MoE) architecture and trained on 22 trillion tokens of multimodal data, it’s designed for performance, flexibility, and global scale. With a context length up to 10 million tokens, support for text and image inputs, and superior performance in multilingual and reasoning benchmarks.

What is Llama 4 Maverick?

Llama 4 Maverick Overview

Llama 4 Maverick Benchmark

Llama 4 Maverick outperforms Gemini 2.0 Flash, DeepSeek v3.1, and GPT-4o on several major benchmarks, especially in image reasoning, image understanding, and multilingual tasks.

How to Access Llama 4 Maverick Locally?

Llama 4 Maverick Hardware Requirements

Install Llama 4 Maverick Locally 

Step 1: Prepare the Environment

• Install Python (preferably version 3.9 or higher).
• Use a virtual environment for dependency management:textpython -m venv llama_env source llama_env/bin/activate

Step 2: Install Required Python Libraries

Run the following commands to install dependencies:

bash<code>pip install -U transformers==4.51.0
pip install torch
pip install huggingface-hub
pip install hf_xet

These libraries are essential for loading and running the model.

Step 3: Download Model

1. Visit the Hugging Face Hub page for Llama 4 Maverick.
2. Use the following Python code to download the model:

from transformers import AutoProcessor, Llama4ForConditionalGeneration

model_id = "meta-llama/Llama-4-Maverick-17B-128E-Instruct"
processor = AutoProcessor.from_pretrained(model_id)
model = Llama4ForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.bfloat16)

This will download the model and prepare it for inference.

How to Access Llama 4 Maverick via Novita API?

Step 1: Log In and Access the Model Library

Log in to your account and click on the Model Library button.

Step 2: Choose Your Model

Browse through the available options and select the model that suits your needs.

Step 3: Start Your Free Trial

Begin your free trial to explore the capabilities of the selected model.

Step 4: Get Your API Key

To authenticate with the API, we will provide you with a new API key. Entering the “Settings“ page, you can copy the API key as indicated in the image.

Step 5: Install the API

Install API using the package manager specific to your programming language.

After installation, import the necessary libraries into your development environment. Initialize the API with your API key to start interacting with Novita AI LLM. This is an example of using chat completions API for python users.

from openai import OpenAI
  
client = OpenAI(
    base_url="https://api.novita.ai/v3/openai",
    api_key="<YOUR Novita AI API Key>",
)

model = "meta-llama/llama-4-maverick-17b-128e-instruct-fp8"
stream = True # or False
max_tokens = 2048
system_content = """Be a helpful assistant"""
temperature = 1
top_p = 1
min_p = 0
top_k = 50
presence_penalty = 0
frequency_penalty = 0
repetition_penalty = 1
response_format = { "type": "text" }

chat_completion_res = client.chat.completions.create(
    model=model,
    messages=[
        {
            "role": "system",
            "content": system_content,
        },
        {
            "role": "user",
            "content": "Hi there!",
        }
    ],
    stream=stream,
    max_tokens=max_tokens,
    temperature=temperature,
    top_p=top_p,
    presence_penalty=presence_penalty,
    frequency_penalty=frequency_penalty,
    response_format=response_format,
    extra_body={
      "top_k": top_k,
      "repetition_penalty": repetition_penalty,
      "min_p": min_p
    }
  )

if stream:
    for chunk in chat_completion_res:
        print(chunk.choices[0].delta.content or "", end="")
else:
    print(chat_completion_res.choices[0].message.content)
  
  
  

Using Llama 4 Scout via Cloud GPU

Step1：Register an account

If you’re new to Novita AI, begin by creating an account on our website. Once you’re registered, head to the “GPUs” tab to explore available resources and start your journey.

Step2：Exploring Templates and GPU Servers

Start by selecting a template that matches your project needs, such as PyTorch, TensorFlow, or CUDA. Choose the version that fits your requirements, like PyTorch 2.2.1 or CUDA 11.8.0. Then, select the A100 GPU server configuration, which offers powerful performance to handle demanding workloads with ample VRAM, RAM, and disk capacity.

Step3：Tailor Your Deployment

After selecting a template and GPU, customize your deployment settings by adjusting parameters like the operating system version (e.g., CUDA 11.8). You can also tweak other configurations to tailor the environment to your project’s specific requirements.

Step4：Launch an instance

Once you’ve finalized the template and deployment settings, click “Launch Instance” to set up your GPU instance. This will start the environment setup, enabling you to begin using the GPU resources for your AI tasks.

If you’re looking for a powerful, affordable, and developer-friendly LLM, Llama 4 Maverick is your best bet. It outperforms leading models like GPT-4o and Gemini 2.0 Flash in key areas—image reasoning, multilingual support, long-context understanding, and inference cost. Whether running locally or through Novita AI’s high-performance API, getting started is fast and easy. Try it today and see the difference.

Frequently Asked Questions

Novita AI is an AI cloud platform that offers developers an easy way to deploy AI models using our simple API, while also providing an affordable and reliable GPU cloud for building and scaling.

Recommend Reading

• How to Access Llama 3.3 70b Locally or via API: A Complete Guide?
• Qwen 2.5 72b vs Llama 3.3 70b: Which Model Suits Your Needs?
• Llama 3.3 70B Function Calling: Seamless Integration for Better Performance