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Available now on ModelsLab · Language Model

Llama 4 Maverick Instruct (17Bx128E)Multimodal MoE Power

Run Maverick Efficiently

MoE Architecture

17B Active 400B Total

Activates 17B parameters from 400B total across 128 experts for text and image tasks.

Native Multimodal

Text Image Fusion

Processes multilingual text and images with early fusion for reasoning and vision.

Single H100 Fit

FP8 Quantized Weights

FP8 weights load on one H100 GPU while preserving quality for fast inference.

Examples

See what Llama 4 Maverick Instruct (17Bx128E) can create

Copy any prompt below and try it yourself in the playground.

Chart Analysis

Analyze this sales chart image. Extract key trends, compare quarters, and suggest optimizations. Output in JSON with metrics.

Code Debug

Review this Python function for errors. The code processes image data from a multimodal dataset. Fix bugs and optimize for MoE efficiency.

Doc Reasoning

Read this technical document image on MoE architectures. Summarize Llama 4 Maverick specs, including parameter counts and context length.

Multilingual Query

Translate and reason about this French diagram on AI inference. Explain H100 deployment in English, list pros and cons.

For Developers

A few lines of code.
Instruct via API. One call.

ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.

  • Serverless: scales to zero, scales to millions
  • Pay per token, no minimums
  • Python and JavaScript SDKs, plus REST API
import requests
response = requests.post(
"https://modelslab.com/api/v7/llm/chat/completions",
json={
"key": "YOUR_API_KEY",
"prompt": "",
"model_id": ""
}
)
print(response.json())

FAQ

Common questions about Llama 4 Maverick Instruct (17Bx128E)

Read the docs

Ready to create?

Start generating with Llama 4 Maverick Instruct (17Bx128E) on ModelsLab.