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Version Release Date: April 28, 2025
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Model Overview

Description:

The NVIDIA Llama 4 Maverick 17B 128E Instruct FP8 model is the quantized language model of the Meta's Llama 4 Maverick 17B 128E model, which is an auto-regressive language model that uses a mixture-of-experts (MoE) architecture and incorporate early fusion for native multimodality. For more information, please check here. The NVIDIA Llama 4 Maverick 17B 128E Eagle3 BF16 model incorporates Eagle speculative decoding with TensorRT Model Optimizer.

This model is ready for commercial and non-commercial use.

Third-Party Community Consideration]

This speculation heads has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA (Llama-4-Maverick-17B-128E) Model Card.

License/Terms of Use:

GOVERNING TERMS: Use of this model is governed by the NVIDIA Open Model License.
ADDITIONAL INFORMATION: Llama4 Community License Agreement. Built with Llama.

Deployment Geography:

Global, except in European Union

Use Case:

Developers looking to take off the shelf pre-quantized models for deployment in AI Agent systems, chatbots, RAG systems, and other AI-powered applications. .

Release Date:

Huggingface: May 27th, 2025via [https://huggingface.co/nvidia/Llama-4-Maverick-17B-128E-Instruct-FP8]

Model Architecture:

Architecture Type: Transformers
Network Architecture: Llama4

Input:

Input Type(s): Multilingual text and up to 5 images
Input Format(s): String, Images
Input Parameters: 1D, 2D
Other Properties Related to Input: Context length up to 1M

Output:

Output Type(s): Multilingual text and code
Output Format: String
Output Parameters: 1D
Other Properties Related to Output: Context length up to 1M

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA’s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

Software Integration :

Supported Runtime Engine(s):

Tensor(RT)-LLM

Supported Hardware Microarchitecture Compatibility:

NVIDIA Hopper
NVIDIA Ampere
NVIDIA Blackwell

[Preferred/Supported] Operating System(s):

Linux

Model Version(s):

The model is quantized with nvidia-modelopt v0.27.0

Datasets:

Calibration Dataset: cnn_dailymail
** Data collection method: Automated
** Labeling method: The dataset is labeled by having the news articles as input and the corresponding highlight as the gold label summary.
Evaluation Dataset: MMMU Pro, GPQA Diamond, HLE, LiveCodeBench, SciCode, HumanEval, AIME 2024, MATH-500
** Data collection method: Hybrid: Automated, Human
** Labeling method: The data is labeled by human input and/or algorithmic methods.

Inference:

Engine: Tensor(RT)-LLM
Test Hardware: H100

Post Training Quantization

This model was obtained by quantizing the weights and activations of Llama 4 Maverick 17B 128E Instruct to FP8 data type, ready for inference with TensorRT-LLM. Only the weights and activations of the linear operators within transformers blocks are quantized. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.

Usage

Deploy with TensorRT-LLM

To serve the quantized checkpoint with TensorRT-LLM, follow the sample commands below with the TensorRT-LLM GitHub repo:

LLM API sample usage:

from tensorrt_llm import LLM, SamplingParams


def main():

    prompts = [
        "Hello, my name is",
        "The president of the United States is",
        "The capital of France is",
        "The future of AI is",
    ]
    sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

    llm = LLM(model="nvidia/Llama-4-Maverick-17B-128E-Instruct-FP8")

    outputs = llm.generate(prompts, sampling_params)

    # Print the outputs.
    for output in outputs:
        prompt = output.prompt
        generated_text = output.outputs[0].text
        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")


# The entry point of the program need to be protected for spawning processes.
if __name__ == '__main__':
    main()

Evaluation

The accuracy benchmark results are presented in the table below:

Precision	MMMU Pro	GPQA Diamond	HLE Challenge	LiveCodeBench	SciCode	HumanEval	AIME 2024	MATH-500
Llama-4-Maverick-17B-128E-Instruct¹	81	67	5	40	33	88	39	89
Llama-4-Maverick-17B-128E-Instruct-FP8	81.2	68.3	4.3	42.5	37.1	85.4	38.6	87.9

¹ Reference scores for Llama-4-Maverick-17B-128E-Instruct sourced from artificialanalysis.

Ethical Considerations :

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should perform safety testing and tuning tailored to their specific applications of the model. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards.

Please report security vulnerabilities or NVIDIA AI Concerns here.

SUBCARDS:

Explainability

Field:	Response:
Intended Application(s) & Domain(s):	Text generation, reasoning, summarization, and question answering.
Model Type:	Text and Image-to-text transformer
Intended Users:	This model is intended for developers, researchers, and customers building/utilizing LLMs, while balancing accuracy and efficiency.
Output:	Text String(s)
Describe how the model works:	Generates text by predicting the next word or token based on the context provided in the input sequence using multiple self-attention layers
Technical Limitations:	The Llama4 model struggles with generating coherent and contextually appropriate responses when faced with highly ambiguous or abstract prompts. Llama4 also struggles when handling nuanced context, complex logic, precise numerical calculations, or ambiguous language.
Verified to have met prescribed quality standards?	Yes
Performance Metrics:	Accuracy, Throughput, and user-side throughput
Potential Known Risk	This model may produce inaccurate or other objectionable responses to user prompts.
Licensing:	Your usage is governed by the following license Built with Llama.

Bias

Field:	Response:
Participation considerations from adversely impacted groups (protected classes) in model design and testing:	None
Measures taken to mitigate against unwanted bias:	None

Safety & Security

Field:	Response:
Model Application(s):	Chat, Instruction Following, Chatbot Development, Code Generation, Reasoning
Describe life critical application (if present):	None Known
Use Case Restrictions:	Abide by the license Built with Llama.
Model and Dataset Restrictions:	The Principle of least privilege (PoLP) is applied limiting access for dataset generation. Restrictions enforce dataset access during training, and dataset license constraints adhered to. Model checkpoints are made available on Hugging Face, and may become available on cloud providers' model catalog.

Privacy

Field:	Response:
Generatable or Reverse engineerable personal data?	None
Was consent obtained for any personal data used?	None Known
Personal data used to create this model?	None Known
How often is dataset reviewed?	Before Release
Is there provenance for all datasets used in training?	Yes
Does data labeling (annotation, metadata) comply with privacy laws?	Yes
Applicable NVIDIA Privacy Policy	https://www.nvidia.com/en-us/about-nvidia/privacy-policy/