Example Curl Request

#!/bin/bash

curl -X 'POST' \
'<ENDPOINT_URL>/v1/chat/completions' \
-H 'Accept: application/json' \
-H 'Content-Type: application/json' \
-H "Authorization: Bearer <API_KEY>" \
-d '{
"messages": [
  {
      "role": "user",
      "content": "Write a limerick about the wonders of GPU computing."
  }
],
"max_tokens": 256
}'

Use Case

Release Date

Reference(s)

• NVIDIA Nemotron 3 model family on Hugging Face
• NVIDIA Nemotron 2 model family on Hugging Face
• NVIDIA Nemotron 3 White Paper

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: 120B Total / 12B Active

Model Design

Training Methodology

• NVIDIA-Nemotron-3-Super-120B-A12B-Base model was pre-trained for over 25T tokens using crawled and synthetic code, math, science, and general knowledge data. Training leveraged NVFP4 quantization 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

• 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.

• The model underwent multi-environment reinforcement learning using synchronous 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 decouples training from inference and leverages 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

Software Integration

• Runtime Engine(s): NeMo 25.11.01
• Supported Hardware Microarchitecture Compatibility: NVIDIA Ampere; NVIDIA Blackwell; NVIDIA Hopper
• 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.

Training

Base Pre-Training Corpus (Nemotron 3 Foundation)

Public Datasets

Crawled and Scraped from Online Sources by NVIDIA

Private Non-publicly Accessible Datasets of Third Parties

Private Non-publicly Accessible Datasets by NVIDIA

NVIDIA-Sourced Synthetic Datasets

Language Distribution in Post-Training

Ethical Considerations

Safety

Privacy

Explainability

Bias

Citation

@misc{nvidia_nemotron_3_2025,
  title  = {NVIDIA Nemotron 3: Efficient and Open Intelligence},
  author = {{NVIDIA}},
  year   = {2025},
  url    = {https://arxiv.org/abs/2512.20856},
  note   = {White Paper}
}

Evaluation Dataset

• Data Collection Method by dataset: Hybrid: Human, Synthetic
• Labeling Method by dataset: Hybrid: Automated, Human, Synthetic

Inference

• Acceleration Engine: PyTorch
• Test Hardware:
• NVIDIA Hopper
  • 1-8x H100
  • 1-8x H200
• NVIDIA Grace Blackwell
  • GB200