Using Llama 4 Maverick 17B Instruct FP8 on DeepInfra
Implementation guide · Llama 4 · AI at Meta
ServerlessOpen Weights
DeepInfra exposes Llama 4 Maverick 17B Instruct FP8 through model ID meta-llama/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 DeepInfra SDK or REST API to call
meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8— see the documentation for request format. - 3
Code Examples
Install
pip install openaiAPI key
DEEPINFRA_API_KEYModel ID
meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8DeepInfra uses "organization/model-name" format, e.g. "meta-llama/Meta-Llama-3-8B-Instruct" or "mistralai/Mistral-7B-Instruct-v0.3". See the DeepInfra model catalog for exact IDs.
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["DEEPINFRA_API_KEY"],
base_url="https://api.deepinfra.com/v1/openai"
)
response = client.chat.completions.create(
model="meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Pricing on DeepInfra
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.15 |
| Output tokens | $0.60 |
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