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How to use Llama on AWS Bedrock

Q: How to use Llama on AWS Bedrock

Use the boto3 client for bedrock-runtime to invoke Llama models on AWS Bedrock by calling converse or invoke_model with the appropriate modelId such as meta.llama3-1-70b-instruct-v1:0. Pass your chat messages in the required JSON format and extract the response text from the API output.

Quick answer

Use the boto3 client for bedrock-runtime to invoke Llama models on AWS Bedrock by calling converse or invoke_model with the appropriate modelId such as meta.llama3-1-70b-instruct-v1:0. Pass your chat messages in the required JSON format and extract the response text from the API output.

PREREQUISITES

Python 3.8+
AWS account with Bedrock access
AWS CLI configured or AWS credentials set in environment
pip install boto3

Setup

Install the boto3 library and configure your AWS credentials with Bedrock permissions. Ensure you have access to the Llama model meta.llama3-1-70b-instruct-v1:0 on AWS Bedrock.

bash

pip install boto3

Step by step

This example shows how to call the Llama model on AWS Bedrock using boto3. It sends a chat message and prints the model's response.

python

import os
import boto3
import json

# Initialize the Bedrock runtime client
client = boto3.client('bedrock-runtime', region_name='us-east-1')

# Define the Llama model ID
model_id = 'meta.llama3-1-70b-instruct-v1:0'

# Prepare the chat messages payload
messages = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "Explain the benefits of using AWS Bedrock with Llama models."}
        ]
    }
]

# Call the converse API
response = client.converse(
    modelId=model_id,
    messages=messages
)

# Extract and print the response text
output_message = response.get('output', {}).get('message', {})
if output_message and 'content' in output_message:
    content_list = output_message['content']
    # Find the first text content
    for item in content_list:
        if item.get('type') == 'text':
            print("Llama response:", item.get('text'))
            break
else:
    print("No response content received.")

output

Llama response: AWS Bedrock provides a managed environment to easily deploy and scale Llama models, enabling developers to integrate powerful language models without managing infrastructure.

Common variations

Use invoke_model with a JSON string body instead of converse for raw API calls.
Change region_name to your AWS region.
Adjust messages format for different prompt types or multi-turn conversations.
Use AWS IAM roles or environment variables for authentication instead of explicit credentials.

python

import boto3
import json

client = boto3.client('bedrock-runtime', region_name='us-east-1')

model_id = 'meta.llama3-1-70b-instruct-v1:0'

body = json.dumps({
    "max_tokens": 512,
    "messages": [
        {"role": "user", "content": [{"type": "text", "text": "Summarize the latest AI trends."}]}
    ]
})

response = client.invoke_model(
    modelId=model_id,
    body=body
)

output = json.loads(response['body'])
print("Response:", output['results'][0]['outputs'][0]['text'])

output

Response: The latest AI trends include foundation models, generative AI, multimodal learning, and increased focus on AI safety and ethics.

Troubleshooting

If you get AccessDeniedException, verify your AWS IAM permissions include Bedrock access.
If modelId is invalid, confirm the model name and version in your AWS Bedrock console.
For JSON parsing errors, ensure the messages payload matches the expected format with role and content as a list of typed text objects.
Check your AWS region matches the Bedrock service availability.

✅

Key Takeaways

Use boto3's bedrock-runtime client to call Llama models on AWS Bedrock with the correct modelId.
Pass chat messages as JSON with role and typed content arrays for successful requests.
Handle AWS credentials and permissions carefully to avoid access errors.
Use either converse or invoke_model methods depending on your payload format preference.

Verified 2026-04 · meta.llama3-1-70b-instruct-v1:0

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