How to Deploy Llama 4 to AWS, Azure & Hugging Face

This guide provides step-by-step instructions for deploying Meta's Llama 4 models (Scout and Maverick) on three major platforms: AWS, Azure, and Hugging Face. These models offer advanced capabilities including multimodal processing, massive context windows, and state-of-the-art performance.

Prerequisites & Hardware Requirements  for Llama 4 Deployment

• Access to Llama 4 models through Meta's license agreement
• Hugging Face account with READ access token
• AWS, Azure, or Hugging Face Pro account as needed for your deployment target
• Basic understanding of containerization and cloud services

AWS (via TensorFuse)

• Scout: 8x H100 GPUs for 1M token context
• Maverick: 8x H100 GPUs for 430K token context
• Alternative: 8x A100 GPUs (reduced context window)

Azure

(This aligns with general Azure ML guidance for large language models, but no Llama 4-specific documentation was found to confirm exact requirements.)

• Recommended: ND A100 v4-series (8 NVIDIA A100 GPUs)
• Minimum: Standard_ND40rs_v2 or higher

Hugging Face

• Recommended: A10G-Large Space hardware
• Alternative: A100-Large (premium hardware option)
• Free tier hardware is insufficient for full models

1. Deploying Llama 4 to AWS using TensorFuse

1.1 Set Up AWS and TensorFuse

Install TensorFuse CLI:

pip install tensorfuse

Configure AWS credentials:

aws configure

Initialize TensorFuse with your AWS account:

tensorkube init

1.2 Create Required Secrets

Store your Hugging Face token:

tensorkube secret create hugging-face-secret YOUR_HF_TOKEN --env default HUGGING_FACE_HUB_TOKEN=

Create API authentication token:

tensorkube secret create vllm-token vllm-key --env default VLLM_API_KEY=

1.3 Create Dockerfile for Llama 4

For Scout model:

FROM vllm/vllm-openai:v0.8.3
ENV HF_HUB_ENABLE_HF_TRANSFER=1
EXPOSE 80
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server", \\\\
            "--model", "meta-llama/Llama-4-Scout-17B-16E-Instruct", \\\\
            "--dtype", "bfloat16", \\\\
            "--trust-remote-code", \\\\
            "--tensor-parallel-size", "8", \\\\
            "--max-model-len", "1000000", \\\\
            "--port", "80", \\\\
            "--override-generation-config", "{\\\\"attn_temperature_tuning\\\\": true}", \\\\
            "--limit-mm-per-prompt", "image=10", \\\\
            "--kv-cache-dtype", "fp8", \\\\
            "--api-key", "${VLLM_API_KEY}"]

For Maverick model:

FROM vllm/vllm-openai:v0.8.3
ENV HF_HUB_ENABLE_HF_TRANSFER=1
EXPOSE 80
ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server", \\\\
            "--model", "meta-llama/Llama-4-Maverick-17B-128E-Instruct", \\\\
            "--dtype", "bfloat16", \\\\
            "--trust-remote-code", \\\\
            "--tensor-parallel-size", "8", \\\\
            "--max-model-len", "430000", \\\\
            "--port", "80", \\\\
            "--override-generation-config", "{\\\\"attn_temperature_tuning\\\\": true}", \\\\
            "--limit-mm-per-prompt", "image=10", \\\\
            "--kv-cache-dtype", "fp8", \\\\
            "--api-key", "${VLLM_API_KEY}"]

1.4 Create Deployment Configuration

Create deployment.yaml:

gpus: 8
gpu_type: h100
secret:
  - huggingfacesecret
  - vllmtoken
min-scale: 1
readiness:
  httpGet:
    path: /health
    port: 80

1.5 Deploy to AWS

Deploy your service:

tensorkube deploy --config-file ./deployment.yaml

1.6 Access Your Deployed Service

List deployments to get your endpoint URL:

tensorkube deployment list

Test your deployment:

curl --request POST \\\\
  --url YOUR_APP_URL/v1/completions \\\\
  --header 'Content-Type: application/json' \\\\
  --header 'Authorization: Bearer vllm-key' \\\\
  --data '{
    "model": "meta-llama/Llama-4-Scout-17B-16E-Instruct",
    "prompt": "Earth to Llama 4. What can you do?",
    "max_tokens": 1000
  }'

2. Deploying Llama 4 to Azure

2.1 Set Up Azure ML Workspace

Install Azure CLI and ML extensions:

pip install azure-cli azure-ml
az login

Create Azure ML workspace:

az ml workspace create --name llama4-workspace --resource-group your-resource-group

2.2 Create Compute Cluster

az ml compute create --name llama4-cluster --type amlcompute --min-instances 0 \\\\
  --max-instances 1 --size Standard_ND40rs_v2 --vnet-name your-vnet-name \\\\
  --subnet your-subnet --resource-group your-resource-group --workspace-name llama4-workspace

2.3 Register Llama 4 Model in Azure ML

Create model.yml:

$schema: <https://azuremlschemas.azureedge.net/latest/model.schema.json>
name: llama-4-scout
version: 1
path: .
properties:
  model_name: "meta-llama/Llama-4-Scout-17B-16E-Instruct"

Register the model:

az ml model create --file model.yml --resource-group your-resource-group --workspace-name llama4-workspace

2.4 Create Deployment Configuration

Create deployment.yml:

$schema: <https://azuremlschemas.azureedge.net/latest/managedOnlineDeployment.schema.json>
name: llama4-deployment
endpoint_name: llama4-endpoint
model: azureml:llama-4-scout@latest
instance_type: Standard_ND40rs_v2
instance_count: 1
environment_variables:
  HUGGING_FACE_HUB_TOKEN: ${{secrets.HF_TOKEN}}
  VLLM_API_KEY: ${{secrets.VLLM_KEY}}
environment:
  image: vllm/vllm-openai:v0.8.3
  conda_file: conda.yml

Create conda.yml:

channels:
  - conda-forge
dependencies:
  - python=3.10
  - pip
  - pip:
    - vllm==0.8.3
    - transformers
    - accelerate

2.5 Create Endpoint and Deploy

az ml online-endpoint create --name llama4-endpoint \\\\
  --resource-group your-resource-group --workspace-name llama4-workspace

az ml online-deployment create --file deployment.yml \\\\
  --resource-group your-resource-group --workspace-name llama4-workspace

2.6 Test the Deployment

az ml online-endpoint invoke --name llama4-endpoint --request-file request.json \\\\
  --resource-group your-resource-group --workspace-name llama4-workspace

Where request.json contains:

{
  "model": "meta-llama/Llama-4-Scout-17B-16E-Instruct",
  "prompt": "Earth to Llama 4. What can you do?",
  "max_tokens": 1000
}

3. Deploying Llama 4 to Hugging Face

3.1 Set Up Hugging Face Account

1. Create a Hugging Face account at https://huggingface.co/
2. Accept the license agreement for Llama 4 models at https://huggingface.co/meta-llama

3.2 Deploy Using Hugging Face Spaces

Navigate to https://huggingface.co/spaces and click "Create new Space"

Configure your Space:

• Name: llama4-deployment
• License: Select appropriate license
• SDK: Choose Gradio
• Space Hardware: A10G-Large (for best performance)
• Visibility: Private or Public based on your needs

Clone the Space repository:

git clone <https://huggingface.co/spaces/YOUR_USERNAME/llama4-deployment>
cd llama4-deployment

3.3 Create Application Files

Create app.py:

import gradio as gr
import torch
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
import os

# Add your HF token to environment or Secrets
os.environ["HUGGING_FACE_HUB_TOKEN"] = "YOUR_HF_TOKEN"

# Load model and tokenizer with appropriate configuration
model_id = "meta-llama/Llama-4-Scout-17B-16E-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

# Create pipeline
pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_length=2048
)

def generate_text(prompt, max_length=1000, temperature=0.7):
    # Format prompt according to Llama 4 format
    formatted_prompt = f"<|begin_of_text|><|user|>\\\\n{prompt}<|end_of_text|>\\\\n<|assistant|>"

    outputs = pipe(
        formatted_prompt,
        max_length=len(tokenizer.encode(formatted_prompt)) + max_length,
        temperature=temperature,
        do_sample=True,
    )

    return outputs[0]['generated_text'].replace(formatted_prompt, "")

# Create Gradio interface
demo = gr.Interface(
    fn=generate_text,
    inputs=[
        gr.Textbox(lines=4, placeholder="Enter your prompt here...", label="Prompt"),
        gr.Slider(minimum=100, maximum=2000, value=1000, step=100, label="Max Length"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature")
    ],
    outputs="text",
    title="Llama 4 Demo",
    description="Generate text using Meta's Llama 4 model",
)

demo.launch()

Create requirements.txt:

accelerate>=0.20.3
bitsandbytes>=0.41.1
gradio>=3.50.0
torch>=2.0.1
transformers>=4.34.0

3.4 Deploy to Hugging Face

Push to your Hugging Face Space:

git add app.py requirements.txt
git commit -m "Add Llama 4 deployment"
git push

3.5 Monitor Deployment

1. Visit your Space URL: https://huggingface.co/spaces/YOUR_USERNAME/llama4-deployment
2. The first build will take time as it needs to download and set up the model
3. Once deployed, you'll see a Gradio interface where you can interact with the model

4. Testing and Interacting with Your Deployments

4.1 Using Python Client for API Access (AWS & Azure)

import openai

# For AWS
client = openai.OpenAI(
    base_url="YOUR_AWS_URL/v1",  # From tensorkube deployment list
    api_key="vllm-key"  # Your configured API key
)

# For Azure
client = openai.AzureOpenAI(
    azure_endpoint="YOUR_AZURE_ENDPOINT",
    api_key="YOUR_API_KEY",
    api_version="2023-05-15"
)

# Make a text completion request
response = client.completions.create(
    model="meta-llama/Llama-4-Scout-17B-16E-Instruct",
    prompt="Write a short poem about artificial intelligence.",
    max_tokens=200
)

print(response.choices[0].text)

# For multimodal capabilities (if supported)
import base64

# Load image as base64
with open("image.jpg", "rb") as image_file:
    image_base64 = base64.b64encode(image_file.read()).decode('utf-8')

# Create chat completion with the image
response = client.chat.completions.create(
    model="meta-llama/Llama-4-Scout-17B-16E-Instruct",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Describe this image:"},
                {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_base64}"}}
            ]
        }
    ],
    max_tokens=300
)

print(response.choices[0].message.content)

Conclusion

You now have step-by-step instructions for deploying Llama 4 models on AWS, Azure, and Hugging Face. Each platform offers different advantages:

• AWS with TensorFuse: Full control, high scalability, best performance
• Azure: Integration with Microsoft ecosystem, managed ML services
• Hugging Face: Simplest setup, great for prototyping and demos

Choose the platform that best fits your specific requirements for cost, scale, performance, and ease of management.