NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16

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.

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, 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.

Training Methodology

Stage 1: Pre-Training

• NVIDIA-Nemotron-3-Ultra-550B-A55B-Base-BF16 model was pre-trained for approximately 20T tokens using crawled and synthetic code, math, science, and general knowledge data. Training leveraged an NVFP4 recipe for efficiency. All datasets are disclosed in the Training and Evaluation Datasets section of this document. Major portions of the pre-training corpus are released in the Nemotron-Pre-Training-Datasets collection.
• Software used for pre-training: Megatron-LM

Stage 2: Supervised Fine-Tuning

• The model was further fine-tuned on synthetic code, math, science, tool calling, instruction following, structured outputs, and general knowledge data. This stage incorporated data designed to support long-range retrieval and multi-document aggregation. All datasets are disclosed in the Training and Evaluation Datasets section of this document. Major portions of the fine-tuning corpus are released in the Nemotron-Post-Training-v3 collection. Data Designer is one of the libraries used to prepare these corpora.

Stage 3: Reinforcement Learning

• The model underwent multi-environment reinforcement learning using asynchronous GRPO (Group Relative Policy Optimization) across math, code, science, instruction following, multi-step tool use, multi-turn conversations, and structured output environments. It utilized an asynchronous RL architecture that fully decouples training from inference across separate GPU devices, leveraging in-flight weight updates and MTP to accelerate rollout generation. Conversational quality was further refined through RLHF. All datasets are disclosed in the Training and Evaluation Datasets section of this document. The RL environments and datasets are released as part of NeMo Gym.
• Software used for reinforcement learning: NeMo RL, NeMo Gym

Stage 4: Multi-Domain On-Policy Distillation (MOPD)

• The model underwent Multi-Domain On-Policy Distillation (MOPD) to improve reasoning across many task types while staying efficient. This technique uses strong teacher models to guide training on the model's own generated attempts (on-policy rollouts), helping recover accuracy and improve performance across coding, math, instruction following, tool use, and agentic workflows. By distilling teacher signal onto the student's own trajectories rather than offline traces, MOPD better aligns the student's behavior with what it would actually produce at inference time, yielding stronger gains than purely off-policy distillation.

NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16 model is a result of the above work.

The end-to-end training recipe is available in the NVIDIA Nemotron Developer Repository. Evaluation results can be replicated using the NeMo Evaluator SDK. Data Designer is one of the libraries used to prepare the pre and post training datasets. More details on the datasets and synthetic data generation methods can be found in the technical report NVIDIA Nemotron 3 Ultra Technical Report.

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, 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.

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

The model utilizes the LatentMoE architecture, where tokens are projected into a smaller latent dimension for expert routing and computation, improving accuracy per byte. The Ultra model is pre-trained using an NVFP4 recipe — sharing the quantization-aware pre-training approach pioneered in the Nemotron 3 family. The majority of linear layers use NVFP4 for weights, activations, and gradients, while select layers (including latent projections, MTP layers, QKV/attention projections, and embeddings) are maintained in BF16 or MXFP8 for training stability. The model includes Multi-Token Prediction (MTP) layers using a shared-weight design across prediction heads. This improves training signal quality, enables faster inference via native speculative decoding, and supports more stable autoregressive drafting at longer draft lengths compared to independently trained offset heads.

Deployment Geography: Global

Use Case

NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16 is a frontier-scale general purpose reasoning and chat model intended to be used in English, Code, and supported multilingual contexts. This model is optimized for complex agentic workflows, long-context reasoning, and high-stakes analytical workloads. It is intended to be used by developers designing AI Agent systems, chatbots, RAG systems, and other AI-powered applications. This model is also suitable for complex instruction-following tasks and long-context reasoning over very large documents and codebases.

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

Benchmarks

| Benchmark | N-3-Ultra
550B-A55B | MiniMax-2.7
230B-A10B | GLM-5.1
744B-A40B | Kimi-K2.6
1T-A32B | Qwen-3.5
397B-17B | DS-v4-Pro
1.6T-A49B | DS-v4-Flash
284B-A13B | | :-- | :-: | :-: | :-: | :-: | :-: | :-: | :-: | | Agentic | | | | | | | | | Terminal Bench 2.1 | 56.4 | 55.5 | 59.3 | 67.2 | 49.9 | 49.2 | 54.2 | | GDPVal | 46.7 | 47.6 | 54.7 | 50.4 | 34.6 | 54.6 | 50.2 | | SWE-Bench Verified | 70.7 | 75.3 | 76.2 | 75.7 | 73.6 | 74.5 | 73.5 | | SWE-Bench Multilingual | 67.7 | 71.8 | 74.8 | 77.1 | 70.9 | 76.5 | 75.0 | | ProfBench (Search) | 56.0 | 52.0 | 46.0 | 56.0 | 53.0 | 59.9 | 57.0 | | PinchBench | 90.0 | 77.6 | 81.2 | 90.2 | 86.6 | 88.6 | 91.3 | | TauBench V3 | | | | | | | | |   Airline | 81.5 | 75.3 | 85.0 | 85.8 | 76.5 | 80.8 | 80.8 | |   Retail | 86.4 | 84.9 | 84.1 | 82.9 | 88.5 | 88.9 | 89.1 | |   Telecom | 92.9 | 89.6 | 96.9 | 97.8 | 98.0 | 96.3 | 98.3 | |   Banking | 22.6 | 14.6 | 12.8 | 23.1 | 20.9 | 25.9 | 26.7 | |   Average | 70.9 | 66.1 | 69.7 | 72.4 | 71.0 | 73.2 | 73.7 | | BrowseComp | 44.4 | 54.1 | 59.4 | 61.3 | 40.5 | 59.4 | 46.9 | | Vals.ai Financial Agent 1.1 | | | | | | | | |   without web search | 60.1 | 51.3 | 60.2 | 54.0 | 61.3 | 58.9 | 58.4 | |   with web search | 53.7 | 50.5 | 60.7 | 58.8 | 59.0 | 62.3 | 60.1 | | Reasoning and Knowledge | | | | | | | | | IOI 2025 | 570.0 | -- | 456.5 | 585.0 | 441.3 | 580.1 | -- | | LiveCodeBench (v6) | 89.0 | 77.2 | 85.7 | 90.2 | 79.3 | 92.5 | 90.9 | | IMOAnswerBench (no tools) | 88.6 | 68.3 | 86.8 | 91.1 | 83.1 | 93.0 | 91.1 | | IMOAnswerBench (with tools) | 92.3 | 75.1 | 91.1 | 93.71 | 84.51 | 85.4 | 89.6 | | Apex-Shortlist (no tools) | 74.9 | 28.9 | 71.1 | 77.4 | 61.4 | 85.8 | 82.4 | | Apex-Shortlist (with tools) | 84.8 | 51.9 | 79.0 | 73.2 | 60.4 | 86.5 | 82.0 | | GPQA (no tools) | 87.0 | 86.6 | 86.1 | 91.0 | 87.1 | 87.8 | 88.5 | | SciCode (subtask) | 44.6 | 38.3 | 47.7 | 52.0 | 48.0 | 50.5 | 48.2 | | HLE (no tools) | 26.7 | 23.1 | 27.2 | 34.8 | 28.5 | 37.7 | 32.2 | | HLE (with tools) | 37.4 | -- | 50.4 | 54.0 | 48.3 | 48.2 | 45.1 | | CritPt (no tools) | 3.1 | 0.6 | 3.7 | 9.1 | 2.4 | 14.0 | 10.6 | | MMLU-Pro | 86.8 | 81.9 | 85.9 | 88.1 | 88.3 | 87.5 | 86.4 | | OmniScience Accuracy | 24.1 | 20.5 | 31.3 | 35.5 | 35.9 | 46.8 | 39.9 | | OmniScience Non-Hallucination | 78.7 | 74.4 | 66.8 | 67.1 | 7.4 | 5.7 | 2.8 | | Chat & Instruction Following | | | | | | | | | IFBench (prompt loose) | 81.7 | 74.6 | 76.6 | 73.7 | 78.2 | 79.1 | 82.0 | | Multi-Challenge | 63.8 | 42.5 | 63.0 | 63.1 | 63.9 | 64.1 | 63.5 | | Long Context | | | | | | | | | AA-LCR | 65.4 | 69.8 | 66.9 | 70.2 | 68.3 | 67.3 | 62.7 | | RULER (1M) | 94.7 | -- | -- | -- | 90.1 | 94.2 | 87.7 | | Longbench v2 (≤ 1M) | 61.9 | -- | -- | -- | 68.9 | 62.1 | 57.0 | | Multilingual | | | | | | | | | MMLU-ProX (avg en/de/fr/es/it/ja/zh/hi/pt/ko) | 83.0 | 78.4 | 85.8 | 85.0 | 86.4 | 85.6 | 84.3 | | WMT24++ (en→xx) | 83.7 | 82.8 | 84.4 | 84.5 | 86.8 | 85.9 | 85.9 |

All evaluation results were collected via Nemo Evaluator SDK. We used three main evaluation harnesses: Nemo Gym, Nemo Skills, and Harbor with extended sandboxing support via AWS ECS on Nemo Evaluator. In addition, the evaluations also used dedicated open-source packaged containers for ScaleAI Multi Challenge Multi Turn Instruction Following and KernelBench. For reproducibility purposes, more details on the evaluation settings and pinned containers can be found in the Nemo Evaluator SDK examples folder and the reproducibility tutorial for Nemotron 3 Ultra.

The following benchmarks are not onboarded yet in our open source tools and for these we used either their official open source implementation or otherwise an internal scaffolding that we plan to open source in the future: BrowseComp with Search, Tau Bench 3, ProfBench with Search, PinchBench, Vals.ai, LongBench v2.

License/Terms of Use

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

Description

Nemotron-3-Ultra-550B-A55B-BF16 is a frontier-scale large language model (LLM) trained by NVIDIA, designed to deliver strong agentic, reasoning, and conversational capabilities. It is optimized for the most demanding workloads, including complex multi-step agents, long-context analysis, and high-accuracy reasoning over code, math, and science. Like other models in the family, it responds to user queries and tasks by first generating a reasoning trace and then concluding with a final response. The model's reasoning capabilities can be configured through a flag in the chat template.

The model employs a hybrid Latent Mixture-of-Experts (LatentMoE) architecture, utilizing interleaved Mamba-2 and MoE layers, along with select Attention layers. Like the Super model, the Ultra model incorporates Multi-Token Prediction (MTP) layers for faster text generation and improved quality, and it is trained using an NVFP4 pre-training recipe to maximize compute efficiency. The model has 55B active parameters and 550B parameters in total.

The supported languages include: English, French, Spanish, Italian, German, Japanese, Hindi, Korean, Brazilian Portuguese, and Chinese.

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

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.

Model Summary