Arsh9210/NVIDIA-Nemotron-3-Ultra-550B-A55B-GenRM

NVIDIA-Nemotron-3-Ultra-550B-A55B-GenRM

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Model Summary

Quick Start

For more details on how to deploy and use the model - see the Quick Start Guide below!

Model Overview

Model Developer: NVIDIA Corporation

Model Dates: December 2025 - April 2026

Data Freshness:

• —The post-training data has a cutoff date of May 2026.
• —The pre-training data has a cutoff date of September 2025.

What is Nemotron?

NVIDIA Nemotron™ is a family of open models with open weights, training data, and recipes, delivering leading efficiency and accuracy for building specialized AI agents.

Description

NVIDIA-Nemotron-3-Ultra-550B-A55B-GenRM is a Generative Reward Model (GenRM) that leverages NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16 as the foundation and is fine-tuned to evaluate the quality of assistant's responses.

Given a conversation history, a new user request, and two candidate assistant responses, it produces an individual helpfulness score for each response and a ranking score. The model also accepts user-specified principles, when given the principles, it judges the responses based on the principles.

This GenRM is used in the Reinforcement Learning from Human Feedback training of NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16.

For training details, see the Nemotron 3 Ultra technical report.

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

License/Terms of Use

Use of this model is governed by the OpenMDW License Agreement, version 1.1 (OpenMDW-1.1).

Deployment Geography: Global

Use Case

This GenRM is used in the Reinforcement Learning from Human Feedback training of NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16.

Release Date

Hugging Face - 06/04/2026 via Hugging Face

Reference(s)

• —NVIDIA Nemotron 3 model family on Hugging Face
• —NVIDIA Nemotron 3 Ultra Technical Report

Model Architecture

• —Architecture Type: Mamba2-Transformer Hybrid Latent Mixture of Experts (LatentMoE) with Multi-Token Prediction (MTP)
• —Network Architecture: Nemotron Hybrid LatentMoE
• —Number of model parameters: 550B Total / 55B Active

Model Design

We developed this model using an early version of NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16 as its foundation. This model contains 550 billion parameters.

Input

• —Input Type(s): Text
• —Input Format(s): String
• —Input Parameters: One-Dimensional (1D): Sequences
• —Other Properties Related to Input: Maximum context length up to 1M tokens. Supported languages include: English, French, Spanish, Italian, German, Japanese, Korean, Hindi, Korean, Brazilian Portuguese, and Chinese

Output

• —Output Type(s): Text
• —Output Format: String
• —Output Parameters: One-Dimensional (1D): Sequences
• —Other Properties Related to Output: Maximum context length up to 1M tokens

Our AI models are designed and 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

• —Runtime Engine(s): NeMo 26.04.01
• —Supported Hardware Microarchitecture Compatibility: NVIDIA Ampere - A100; NVIDIA Blackwell; NVIDIA Hopper - H100-80GB
• —Operating System(s): Linux

The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.

Model Version(s)

• —v1.0 - GA

Quick Start Guide

The model shares the same architecture as NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16.

Deployment instructions can be found on the NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16 model card.

Now you can query the model, here is an example:

from openai import OpenAI
client = OpenAI(base_url="http://127.0.0.1:8000/v1", api_key="dummy")
msg = [
  {"role": "user", "content": "What is 1+1?"}, 
  {"role": "assistant", "content": "1+1=2"}, 
  {"role": "user", "content": "What about 1+2?"},
  {"role": "response_1", "content": "1+2=4"},
  {"role": "response_2", "content": "1+2=3"}
]
completion = client.chat.completions.create(
    model="nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-GenRM",
    messages=msg,
    temperature=1.0,
    top_p=0.95,
    max_tokens=24576,
    stream=False
)
output = completion.choices[0].message.content
print(output)

You can also use it with pre-defined principles by formatting message as:

msg = [
  {"role": "user", "content": "How's the weather in LA?"}, 
  {"role": "response_1", "content": "I don't have access to real-time data, so I can't give you the current weather in Los Angeles."},
  {"role": "response_2", "content": "Most days sit in the 65 °F–80 °F (18 °C–27 °C) range, with cooler evenings, especially near the coast."},
  {"role": "principle", "content": "You will be given one or more evaluation criteria (rubrics).\nEvaluate both responses on EACH criterion individually first, then synthesize an overall judgment.\nCriteria:\n\n1. Response should state that it doesn't have access to real-time data."}
]

Note that the conversation history should be presented in "user" and "assistant" roles, where the last turn is user turn. The responses to be judged should be in "response_1" and "response_2" roles, and pre-defined principles should be in "principle" role. When no principle is given, it defaults to a general helpfulness principle.

Interpretation of Scores

For individual helpfulness score, it ranges from 1 to 5, where higher means better.

For ranking score, it ranges from 1 to 6, where:

• —1 = Response 1 is much better than Response 2
• —2 = Response 1 is better than Response 2
• —3 = Response 1 is slightly better than Response 2
• —4 = Response 2 is slightly better than Response 1
• —5 = Response 2 is better than Response 1
• —6 = Response 2 is much better than Response 1

Training and Evaluation Datasets

Training

Data Modality: Text The total size: 53.8 TiB (14.8 trillion tokens) Total number of datasets: 226 Dataset partition: Training [100%], testing [0%], validation [0%] Time period for training data collection: 2013 to 2026 Time period for testing data collection: 2013 to 2026 Time period for validation data collection: 2013 to 2026 Data Collection Method by dataset: Hybrid: Automated, Human, Synthetic Labeling Method by dataset: Hybrid: Automated, Human, Synthetic

More details on the datasets and synthetic data generation methods can be found in the technical report [**NVIDIA Nemotron 3 Ultra**](https://research.nvidia.com/labs/nemotron/files/NVIDIA-Nemotron-3-Ultra-Technical-Report.pdf).

<details> <summary>For Detailed Dataset Information: Click here!</summary>

Base Pre-Training Corpus (Nemotron 3 Foundation)

The foundation of the model is trained on the Nemotron-3-Ultra corpus, comprising the following datasets from the Nemotron Pre-Training Datasets collection:

Public Datasets

Crawled and Scraped from Online Sources by NVIDIA

The English Common Crawl data was downloaded from the Common Crawl Foundation (see their FAQ for details on their crawling) and includes the snapshots CC-MAIN-2013-20 through CC-MAIN-2025-13. The data was subsequently deduplicated and filtered in various ways described in the Nemotron-CC paper. Additionally, we extracted data for fifteen languages from the following three Common Crawl snapshots: CC-MAIN-2024-51, CC-MAIN-2025-08, CC-MAIN-2025-18. The fifteen languages included were Arabic, Chinese, Danish, Dutch, French, German, Italian, Japanese, Korean, Polish, Portuguese, Russian, Spanish, Swedish, and Thai. As we did not have reliable multilingual model-based quality classifiers available, we applied just heuristic filtering instead—similar to what we did for lower quality English data in the Nemotron-CC pipeline, but selectively removing some filters for some languages that did not work well. Deduplication was done in the same way as for Nemotron-CC.

The GitHub Crawl was collected using the GitHub REST API and the Amazon S3 API. Each crawl was operated in accordance with the rate limits set by its respective source, either GitHub or S3. We collect raw source code and subsequently remove any having a license which does not exist in our permissive-license set (for additional details, refer to the technical report).

Private Non-publicly Accessible Datasets of Third Parties

Private Non-publicly Accessible Datasets by NVIDIA

NVIDIA-Sourced Synthetic Datasets (Pre-Training)

<br>

NVIDIA-Sourced Synthetic Datasets (Post-Training)

NVIDIA-Sourced Synthetic Datasets (Reward Modeling)

The preference data blend is an updated version of the previously released nvidia/Nemotron-RLHF-GenRM-v1 dataset. We plan to release this updated data under nvidia/Nemotron-RLHF-GenRM-v2.

Language Distribution in Post-Training

For our post-training recipe, we focused on the following languages in addition to English: French, Spanish, Italian, German, Japanese, Korean, Hindi, Brazilian Portuguese, and Chinese.

Those languages were represented in the form of multilingual reasoning and translation tasks.

The following table depicts our sample distribution.

Evaluation Datasets:

Data Collection Method by dataset <br>

• —Hybrid: Automated, Human, Synthetic

Labeling Method by dataset <br>

• —Hybrid: Automated, Human, Synthetic

Properties: This corpus comprises a mix of high-quality standard benchmarks and test suites relevant to reward modeling.

Testing Datasets:

Data Collection Method by dataset <br>

• —Hybrid: Automated, Human, Synthetic

Labeling Method by dataset <br>

• —Hybrid: Automated, Human, Synthetic

Properties: This corpus comprises a mix of high-quality standard benchmarks and test suites relevant to reward modeling.

</details>

Inference

• —Acceleration Engine: PyTorch
• —Test Hardware:
• —NVIDIA Hopper
• —H100
• —H200
• —NVIDIA Grace Blackwell
• —GB200
• —GB300
• —NVIDIA Blackwell
• —B200
• —B300

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. 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.

We advise against circumvention of any provided safety guardrails contained in the Model without a substantially similar guardrail appropriate for your use case. For more details: Safety and Explainability Subcards.

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

Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.

Citation

@misc{nvidia_nemotron_3_ultra_2026,
  title  = {Nemotron 3 Ultra: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning},
  author = {{NVIDIA}},
  year   = {2026},
  url    = {https://research.nvidia.com/labs/nemotron/files/NVIDIA-Nemotron-3-Ultra-Technical-Report.pdf},
  note   = {White Paper}
}