Using Llama 4 Maverick 17B Instruct FP8 on AWS Bedrock
Implementation guide · Llama 4 · AI at Meta
ServerlessOpen Weights
AWS Bedrock exposes Llama 4 Maverick 17B Instruct FP8 through model ID llama-4-maverick-17b-128e-instruct-fp8. Use the setup steps, sourced pricing, capabilities, and official provider links below to validate this route before deployment.
Last refreshed 2026-07-09. Next refresh: weekly.
Quick Start
- 1
- 2Use the AWS Bedrock SDK or REST API to call
llama-4-maverick-17b-128e-instruct-fp8— see the documentation for request format. - 3
Code Examples
Install
pip install boto3API key
AWS_ACCESS_KEY_IDModel ID
llama-4-maverick-17b-128e-instruct-fp8Use Amazon Bedrock model IDs, e.g. "anthropic.claude-3-opus-20240229-v1:0" for on-demand, or cross-region inference profile IDs like "us.anthropic.claude-opus-4-7-20251101-v1:0". These differ from the public model slug.
import boto3
# Reads AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_DEFAULT_REGION from env
client = boto3.client("bedrock-runtime", region_name="us-east-1")
response = client.converse(
modelId="llama-4-maverick-17b-128e-instruct-fp8",
messages=[{
"role": "user",
"content": [{"text": "Hello"}]
}]
)
print(response["output"]["message"]["content"][0]["text"])Pricing on AWS Bedrock
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.24 |
| Output tokens | $0.97 |
Capabilities
VisionMultimodalStructured Outputs
About Llama 4 Maverick 17B Instruct FP8
Meta's Llama 4 Maverick 17B with 128 experts, FP8-optimized for cost-efficient inference. Supports native Model Router integration on Microsoft Foundry.
Model Specs
Released2025-04-05
Parameters400B (17B active)
Context1m
ArchitectureMixture of Experts
Knowledge cutoff2024-08