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NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4

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NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4

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Model Overview

Model Developer: NVIDIA Corporation

Model Dates:

September 2025 - December 2025

Data Freshness:

The post-training data has a cutoff date of November 28, 2025.
The pre-training data has a cutoff date of June 25, 2025.

Description

Nemotron-Nano-3-30B-A3B-NVFP4 is a quantized version of Nemotron-Nano-3-30B-A3B and is a large language model (LLM) trained from scratch by NVIDIA, and designed as a unified model for both reasoning and non-reasoning tasks. 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. If the user prefers the model to provide its final answer without intermediate reasoning traces, it can be configured to do so, albeit with a slight decrease in accuracy for harder prompts that require reasoning. Conversely, allowing the model to generate reasoning traces first generally results in higher-quality final solutions to queries and tasks.

The model employs a hybrid Mixture-of-Experts (MoE) architecture, consisting of 23 Mamba-2 and MoE layers, along with 6 Attention layers. Each MoE layer includes 128 experts plus 1 shared expert, with 6 experts activated per token. The model has 3.5B active parameters and 30B parameters in total.

The supported languages include: English, German, Spanish, French, Italian, and Japanese. Improved using Qwen.

This model is ready for commercial use.

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.

To get started, you can use our quickstart guide below.

License/Terms of Use

Governing Terms: Use of this model is governed by the NVIDIA Nemotron Open Model License.

Reasoning Benchmark Evaluations

We evaluated our model on the following benchmarks:

Task	NVIDIA-Nemotron-3-Nano-30B-A3B-BF16	NVIDIA-Nemotron-3-Nano-30B-A3B-FP8	NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4
General Knowledge
MMLU-Pro	78.3	78.1	77.4
Reasoning
AIME25 (no tools)	89.1	87.7	86.7
GPQA (no tools)	73.0	72.5	71.9
LiveCodeBench (v6 2025-08–2025-05)	68.3	67.6	65.4
SciCode (subtask)	33.0	31.9	30.7
HLE (no tools)	10.2	10.3	9.4
Agentic
TauBench V2 (Airline)	48.0	44.8	41.5
TauBench V2 (Retail)	56.9	55.6	54.1
TauBench V2 (Telecom)	42.2	40.8	41.2
TauBench V2 (Average)	49.0	47.0	45.6
Chat & Instruction Following
IFBench (prompt)	71.5	72.2	70.7
Long Context
AA-LCR	35.9	36.1	33.3
Multilingual
MMLU-ProX (avg over langs)	59.50	59.6	57.8

All evaluation results were collected via Nemo Evaluator SDK and Nemo Skills. The open source container on Nemo Skills packaged via NVIDIA’s Nemo Evaluator SDK used for evaluations can be found here. In addition to Nemo Skills, the evaluations also used dedicated packaged containers for Tau-2 Bench, ArenaHard v2, AA_LCR. A reproducibility tutorial along with all configs can be found in Nemo Evaluator SDK examples. The configs are also available in this HF repo here.

Deployment Geography: Global

Use Case

NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4 is a general purpose reasoning and chat model intended to be used in English and coding languages. Other non-English languages (English, Spanish, French, German, Japanese, Italian) are also supported. This model 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 typical instruction-following tasks.

Release Date

January 28, 2026 via Hugging Face

Reference(s)

Model Architecture

Architecture Type: Mamba2-Transformer Hybrid Mixture of Experts (MoE)
Network Architecture: Nemotron Hybrid MoE
Number of model parameters: 30B

Model Design

The model was trained with 25T tokens, with a batch size of 3072, and used the Warmup-Stable-Decay (WSD) learning rate schedule with 8B tokens of learning rate warm up, peak learning rate of 1e-3 and minimum learning rate of 1e-5. There are a total of 52 layers, of which there are 23 of each MoE and Mamba-2 and the remaining 6 layers use grouped query attention (GQA) with 2 groups. Each MoE layer includes 128 routed experts plus 1 shared expert, with 6 experts activated per token.

Training Methodology

Stage 1: Pre-Training

NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16 model was pre-trained using crawled and synthetic code, math, science, and general knowledge data. All datasets are disclosed in the Training, Testing, 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. All datasets are disclosed in the Training, Testing, 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.
Software used for supervised fine-tuning: Megatron-LM

Stage 3: Reinforcement Learning

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. Conversational quality was further refined through RLHF using a generative reward model. All datasets are disclosed in the Training, Testing, 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: Post-Training Quantization

The model was quantized to NVFP4 with KV Cache quantized to FP8 using Post-Training Quantization (PTQ). To preserve accuracy while improving efficiency, we applied a selective quantization strategy, keeping the attention layers and the Mamba layers that feed into those attention layers in BF16. After PTQ, Quantization-Aware Distillation (QAD) was applied for further accuracy recovery.
Software used for quantization: Model Optimizer

NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4 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. More details on the datasets and synthetic data generation methods can be found in the technical report NVIDIA Nemotron 3 Nano.

Input

Input Type(s): Text
Input Format(s): String
Input Parameters: One-Dimensional (1D): Sequences
Maximum input size: 1M tokens
Other Properties Related to Input: Supported languages include: English, Spanish, French, German, Japanese, Italian

Output

Output Type(s): Text
Output Format: String
Output Parameters: One-Dimensional (1D): Sequences
Maximum output size: 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 25.11.01
Supported Hardware Microarchitecture Compatibility: NVIDIA H100-80GB, NVIDIA A100
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.

Quick Start Guide

Use it with Transformers

The snippet below shows how to use this model with Huggingface Transformers (tested on version 4.57.3). We recommend using NeMo Framework 25.11.01 to ensure all required libraries are available.

Please note that the model supports up to a 1M context size, although the default context size in the Hugging Face configuration is 256k due to higher VRAM requirements.

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

messages = [
    {"role": "user", "content": "Write a haiku about GPUs"},
]

tokenized_chat = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    tokenized_chat,
    max_new_tokens=1024,
    temperature=1.0,
    top_p=1.0,
    eos_token_id=tokenizer.eos_token_id
)
print(tokenizer.decode(outputs[0]))

temperature=1.0 and top_p=1.0 are recommended for reasoning tasks, while temperature=0.6 and top_p=0.95 are recommended for tool calling.

If you’d like to use reasoning off, add enable_thinking=False to apply_chat_template(). By default, enable_thinking is set to be True.


tokenized_chat = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    enable_thinking=False,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

# Use Greedy Search for reasoning off
outputs = model.generate(
    tokenized_chat,
    max_new_tokens=32,
    do_sample=False,
    num_beams=1,
    eos_token_id=tokenizer.eos_token_id
)
print(tokenizer.decode(outputs[0]))

Use it with vLLM

For more detailed information on how to use the model with vLLM, please see this cookbook. If you are on Jetson Thor or DGX Spark, please use this vllm container.

pip install -U "vllm>=0.12.0"

Download the custom parser from the Hugging Face repository.

wget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4/resolve/main/nano_v3_reasoning_parser.py

Launch a vLLM server using the custom parser.

VLLM_USE_FLASHINFER_MOE_FP4=1 \
VLLM_FLASHINFER_MOE_BACKEND=throughput \
vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4 \
  --served-model-name model \
  --max-num-seqs 8 \
  --tensor-parallel-size 1 \
  --max-model-len 262144 \
  --port 8000 \
  --trust-remote-code \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_coder \
  --reasoning-parser-plugin nano_v3_reasoning_parser.py \
  --reasoning-parser nano_v3 \
  --kv-cache-dtype fp8

In the example above, we use a context length of 256k. You can increase the context size up to 1M to support longer contexts. To enable this, set the VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 environment variable as shown below:

VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 \
VLLM_USE_FLASHINFER_MOE_FP4=1 \
VLLM_FLASHINFER_MOE_BACKEND=throughput \
vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4 \
  --served-model-name model \
  --max-num-seqs 8 \
  --tensor-parallel-size 1 \
  --max-model-len 262144 \
  --port 8000 \
  --trust-remote-code \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_coder \
  --reasoning-parser-plugin nano_v3_reasoning_parser.py \
  --reasoning-parser nano_v3 \
  --kv-cache-dtype fp8

Here is an example client code for vLLM. By default, the endpoint has reasoning enabled. We recommend setting a high value (e.g., 10,000) for max_tokens.

shell

curl http://localhost:8000/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "model",
        "messages":[{"role": "user", "content": "Write a haiku about GPUs"}],
        "max_tokens": 10000
    }'

If you’d like to use reasoning off with vLLM, you can do the following:
vLLM OpenAI curl request:

shell

curl http://localhost:8000/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "model",
        "messages":[{"role": "user", "content": "Write a haiku about GPUs"}],
        "chat_template_kwargs": {"enable_thinking": false}
    }'

vLLM OpenAI client:

response = client.chat.completions.create(model=model, messages=messages, extra_body={"chat_template_kwargs": {"enable_thinking": False}})

Use it with TRT-LLM

For more detailed information on how to use the model with TRT-LLM, please see this cookbook.

# nano_v3 example yaml is https://github.com/NVIDIA/TensorRT-LLM/blob/main/examples/auto_deploy/nano_v3.yaml
trtllm-serve <model_path> \
--backend _autodeploy \
--trust_remote_code \
--reasoning_parser nano-v3 \
--tool_parser qwen3_coder \
--extra_llm_api_options nano_v3.yaml

Use it with SGLang

For more detailed information on how to use the model with SGLang, please see this cookbook.

python3 -m sglang.launch_server --model-path nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4 \
  --trust-remote-code \
  --tp 1 \
  --attention-backend flashinfer \
  --tool-call-parser qwen3_coder \
  --reasoning-parser nano_v3

Using Budget Control

The thinking budget allows developers to keep accuracy high and meet response‑time targets - which is especially crucial for customer support, autonomous agent steps, and edge devices where every millisecond counts.

With budget control, you can set a limit for internal reasoning:

reasoning_budget: This is a threshold that will attempt to end the reasoning trace at the next newline encountered in the reasoning trace. If no newline is encountered within 500 tokens, it will abruptly end the reasoning trace at reasoning_budget + 500.

NOTE: This client will work with any OpenAI API compatible endpoint.

Client for supporting budget control:

from typing import Any, Dict, List

import openai
from transformers import AutoTokenizer


class ThinkingBudgetClient:
   def __init__(self, base_url: str, api_key: str, tokenizer_name_or_path: str):
       self.base_url = base_url
       self.api_key = api_key
       self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name_or_path)
       self.client = openai.OpenAI(base_url=self.base_url, api_key=self.api_key)


   def chat_completion(
       self,
       model: str,
       messages: List[Dict[str, Any]],
       reasoning_budget: int = 512,
       max_tokens: int = 1024,
       **kwargs,
   ) -> Dict[str, Any]:
       assert (
           max_tokens > reasoning_budget
       ), f"thinking budget must be smaller than maximum new tokens. Given {max_tokens=} and {reasoning_budget=}"


       # 1. first call chat completion to get reasoning content
       response = self.client.chat.completions.create(
           model=model, messages=messages, max_tokens=reasoning_budget, **kwargs
       )
       content = response.choices[0].message.content


       reasoning_content = content
       if not "</think>" in reasoning_content:
           # reasoning content is too long, closed with a period (.)
           reasoning_content = f"{reasoning_content}.\n</think>\n\n"
       reasoning_tokens_len = len(
           self.tokenizer.encode(reasoning_content, add_special_tokens=False)
       )
       remaining_tokens = max_tokens - reasoning_tokens_len
       assert (
           remaining_tokens > 0
       ), f"remaining tokens must be positive. Given {remaining_tokens=}. Increase the max_tokens or lower the reasoning_budget."


       # 2. append reasoning content to messages and call completion
       messages.append({"role": "assistant", "content": reasoning_content})
       prompt = self.tokenizer.apply_chat_template(
           messages,
           tokenize=False,
           continue_final_message=True,
       )
       response = self.client.completions.create(
           model=model, prompt=prompt, max_tokens=remaining_tokens, **kwargs
       )


       response_data = {
           "reasoning_content": reasoning_content.strip().strip("</think>").strip(),
           "content": response.choices[0].text,
           "finish_reason": response.choices[0].finish_reason,
       }
       return response_data

Calling the server with a budget (Restricted to 32 tokens here as an example)

tokenizer_name_or_path = "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4"
client = ThinkingBudgetClient(
   base_url="http://localhost:8000/v1",  # Nemotron 3 Nano deployed in thinking mode
   api_key="EMPTY",
   tokenizer_name_or_path=tokenizer_name_or_path,
)


result = client.chat_completion(
   model="nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4",
   messages=[
       {"role": "system", "content": "You are a helpful assistant. /think"},
       {"role": "user", "content": "What is 2+2?"},
   ],
   reasoning_budget=32,
   max_tokens=512,
   temperature=1.0,
   top_p=1.0,
)
print(result)

You should see output similar to the following:

{'reasoning_content': "Okay, the user asked, What is 2+2? Let me think. Well, 2 plus 2 equals 4. That's a basic.", 'content': '2 + 2 equals **4**.\n', 'finish_reason': 'stop'}

Model Version(s)

v1.0 NVFP4

Training, Testing, and Evaluation Datasets

Data Modality: Text
The total size: 10,648,823,153,919 Tokens
Total number of datasets: 141
Dataset partition: Training [100%], testing [0%], validation [0%]
Time period for training data collection: 2013 to May 1, 2025
Time period for testing data collection: 2013 to May 1, 2025
Time period for validation data collection: 2013 to May 1, 2025
Data Collection Method by dataset: Hybrid: Automated, Human, Synthetic
Labeling Method by dataset: Hybrid: Automated, Human, Synthetic

NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 is pre-trained on a large corpus of high-quality curated and synthetically-generated data. It is trained in the English language, as well as 19 other languages and 43 programming languages. Our sources cover a variety of document types such as: webpages, dialogue, articles, and other written materials. The corpus spans domains including legal, math, science, finance, and more. We also include a small portion of question-answering, and alignment style data to improve model accuracy. The model was trained for approximately 25 trillion tokens.

The post-training corpus for NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 of high-quality curated and synthetically-generated data. Primary languages used for post-training include English, German, Spanish, French, Italian, and Japanese.

These datasets, such as FinePDFs, EssentialWeb, HotpotQA, SQuAD, and HelpSteer3, do not collectively or exhaustively represent all demographic groups (and proportionally therein). For instance, these datasets do not contain explicit mentions of demographic classes such as age, gender, or ethnicity in 64-99% of samples, depending on the source. In the subset where such terms are present, document-based datasets (FinePDFs and EssentialWeb) contain representational skews, such as references to "male" outnumbering those to "female", and mentions of "White" as the most frequent among ethnic identifiers (comprising 43-44% of ethnicity mentions). To mitigate these imbalances, we recommend considering evaluation techniques such as bias audits, fine-tuning with demographically balanced datasets, and mitigation strategies like counterfactual data augmentation to align with the desired model behavior. This evaluation used a 3,000-sample subset per dataset, identified as the optimal threshold for maximizing embedder accuracy.

During post-training, we generate synthetic data by distilling trajectories, solutions, and translations from strong teacher models and agent systems, often grounded in real tasks or documents and aggressively filtered for quality. For math, code, and science, we start from curated problem sets and use open source permissive models such as GPT-OSS-120B to produce step-by-step reasoning traces, candidate solutions, best-of-n selection traces, and verified CUDA kernels. For long-context and science, we build synthetic QA and reasoning data by retrieving passages from long documents, generating MCQ/OpenQA questions and answers, and paraphrasing them via into multiple prompt/response formats to ensure diversity. Across all pipelines we stack automated verification—compilers, numerical checks, language identification—to ensure our data is high quality.

For all domains, we apply a unified data filtering pipeline to ensure that only high-quality, license-compliant, and verifiable samples are used for post-training. We first discard malformed examples using structural checks (e.g., missing tool definitions when tool calls are present). We then aggressively filter reasoning traces exhibiting pathological repetition, such as repeated n-grams within a sliding window or across the entire trajectory, which we found to be a strong indicator of malformed or low-quality reasoning. Finally, based on internal audits of synthetically generated datasets, we observed that some teacher models occasionally produce reasoning traces and final responses that implicitly align with specific political entities or promote nationalistic narratives. To mitigate this, we apply targeted keyword- and regex-based filters and remove all trajectories matching such behavior.

Alongside the model, we release our final pre-training and post-training data, as outlined in this section. For ease of analysis, there is a sample set that is ungated. For all remaining code, math and multilingual data, gating and approval is required, and the dataset is permissively licensed for model training purposes.

More details on the datasets and synthetic data generation methods can be found in the technical report NVIDIA Nemotron 3 Nano. Data Designer is one of the libraries used to prepare the pre and post training datasets.

Dataset	Collection Period
GSM8K	4/23/2025
CC-NEWS	4/23/2025
Common Crawl	4/23/2025
Wikimedia	4/23/2025
Bespoke-Stratos-17k	4/23/2025
tigerbot-kaggle-leetcodesolutions-en-2k	4/23/2025
glaive-function-calling-v2	4/23/2025
APIGen Function-Calling	4/23/2025
LMSYS-Chat-1M	4/23/2025
Open Textbook Library - CC BY-SA & GNU subset and OpenStax - CC BY-SA subset	4/23/2025
Advanced Reasoning Benchmark, tigerbot-kaggle-leetcodesolutions-en-2k, PRM800K, and SciBench	4/23/2025
FineWeb-2	4/23/2025
Court Listener	Legacy Download
peS2o	Legacy Download
OpenWebMath	Legacy Download
BioRxiv	Legacy Download
PMC Open Access Subset	Legacy Download
OpenWebText2	Legacy Download
Stack Exchange Data Dump	Legacy Download
PubMed Abstracts	Legacy Download
NIH ExPorter	Legacy Download
arXiv	Legacy Download
BigScience Workshop Datasets	Legacy Download
Reddit Dataset	Legacy Download
SEC's Electronic Data Gathering, Analysis, and Retrieval (EDGAR)	Legacy Download
Advanced Mathematical Problem Solving	Legacy Download
MathPile	Legacy Download
NuminaMath CoT	Legacy Download
PMC Article	Legacy Download
FLAN	Legacy Download
Advanced Reasoning Benchmark	Legacy Download
SciBench	Legacy Download
WikiTableQuestions	Legacy Download
FinQA	Legacy Download
Riddles	Legacy Download
Problems in Elementary Mathematics for Home Study	Legacy Download
MedMCQA	Legacy Download
Cosmos QA	Legacy Download
MCTest	Legacy Download
AI2's Reasoning Challenge	Legacy Download
OpenBookQA	Legacy Download
MMLU Auxiliary Train	Legacy Download
social-chemestry-101	Legacy Download
Moral Stories	Legacy Download
The Common Pile v0.1	Legacy Download
FineMath	Legacy Download
MegaMath	Legacy Download
MegaMath	Legacy Download
MultiverseMathHard	10/2/2025
SWE-Gym	10/2/2025
WorkBench	10/2/2025
WildChat-1M	10/2/2025
OpenCodeReasoning-2	10/2/2025
HelpSteer3	10/2/2025
opc-sft-stage2	10/2/2025
Big-Math-RL-Verified	10/2/2025
NuminaMath CoT	10/2/2025
MetaMathQA	10/2/2025
simple-arithmetic-problems	10/2/2025
arithmetic	10/2/2025
Skywork-OR1-RL-Data	10/2/2025
News Commentary	10/2/2025
FastChat	10/2/2025
Essential-Web	10/2/2025
finepdfs	10/2/2025
HotpotQA	10/2/2025
SQuAD2.0	10/2/2025
NLTK Words Lists	10/2/2025

Private Non-publicly Accessible Datasets of Third Parties

Dataset
Global Regulation
TAUS Translation Memory
Scale HLE
HackerRank Coding

Private Non-publicly Accessible Datasets by NVIDIA

Dataset
Simple Minesweeper
Simple Sudoku
Multitool Typewriter Hard
Machine Translation of News Commentary and TAUS Translation Memory
Machine Translation of STEM data using Qwen2.5-14B-Instruct

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).

Dataset	Modality	Dataset Size	Collection Period	Collecting Organisation
English Common Crawl	Text	3.36T	4/8/2025	NVIDIA Advanced Deep Learning Research
English Common Crawl 1.1	Text	Not disclosed	10/2/2025	NVIDIA Advanced Deep Learning Research
Multilingual Common Crawl	Text	812.7B	5/1/2025	NVIDIA Advanced Deep Learning Research
GitHub Crawl	Text	747.4B	4/29/2025	NVIDIA Advanced Deep Learning Research

NVIDIA-Sourced Synthetic Datasets

Dataset	Modality	Dataset Size	Seed Dataset	Model(s) used for generation
Synthetic Art of Problem Solving from DeepSeek-R1	Text	40B	Art of Problem Solving; American Mathematics Competitions 8; American Mathematics Competitions 10;	DeepSeek-R1
Synthetic Moral Stories and Social Chemistry from Mixtral-8x22B-v0.1	Text	327M	social-chemestry-101; Moral Stories	Mixtral-8x22B-v0.1
Synthetic Social Sciences seeded with OpenStax from DeepSeek-V3, Mixtral-8x22B-v0.1, and Qwen2.5-72B	Text	83.6M	OpenStax - CC BY-SA subset	DeepSeek-V3; Mixtral-8x22B-v0.1; Qwen2.5-72B
Synthetic Health Sciences seeded with OpenStax from DeepSeek-V3, Mixtral-8x22B-v0.1, and Qwen2.5-72B	Text	9.7M	OpenStax - CC BY-SA subset	DeepSeek-V3; Mixtral-8x22B-v0.1; Qwen2.5-72B
Synthetic STEM seeded with OpenStax, Open Textbook Library, and GSM8K from DeepSeek-R1, DeepSeek-V3, DeepSeek-V3-0324, and Qwen2.5-72B	Text	175M	OpenStax - CC BY-SA subset; GSM8K; Open Textbook Library - CC BY-SA & GNU subset	DeepSeek-R1, DeepSeek-V3; DeepSeek-V3-0324; Qwen2.5-72B
Nemotron-PrismMath	Text	4.6B	Big-Math-RL-Verified; OpenR1-Math-220k	Qwen2.5-0.5B-instruct, Qwen2.5-72B-Instruct; DeepSeek-R1-Distill-Qwen-32B
Synthetic Question Answering Data from Papers and Permissible Books from Qwen2.5-72B-Instruct	Text	350M	arXiv; National Institutes of Health ExPorter; BioRxiv; PMC Article; USPTO Backgrounds; peS2o; Global Regulation; CORE; PG-19; DOAB CC BY & CC BY-SA subset; NDLTD	Qwen2.5-72B-Instruct
Refreshed Nemotron-MIND from phi-4	Text	73B	Common Crawl	phi-4
Nemotron-CC-Math-4plus	Text	52.3B	Common Crawl	phi-4
Nemotron-CC-Math-3	Text	80.9B	Common Crawl	phi-4
Synthetic AGIEval seeded with AQUA-RAT, LogiQA, and AR-LSAT from DeepSeek-V3 and DeepSeek-V3-0324	Text	4.0B	AQUA-RAT; LogiQA; AR-LSAT	DeepSeek-V3; DeepSeek-V3-0324
Synthetic AGIEval seeded with AQUA-RAT, LogiQA, and AR-LSAT from Qwen3-30B-A3B	Text	4.2B	AQUA-RAT; LogiQA; AR-LSAT	Qwen3-30B-A3B
Synthetic Art of Problem Solving from Qwen2.5-32B-Instruct, Qwen2.5-Math-72B, Qwen2.5-Math-7B, and Qwen2.5-72B-Instruct	Text		Art of Problem Solving; American Mathematics Competitions 8; American Mathematics Competitions 10; GSM8K; PRM800K	Qwen2.5-32B-Instruct; Qwen2.5-Math-72B; Qwen2.5-Math-7B; Qwen2.5-72B-Instruct
Synthetic MMLU Auxiliary Train from DeepSeek-R1	Text	0.5B	MMLU Auxiliary Train	DeepSeek-R1
Synthetic Long Context Continued Post-Training Data from Papers and Permissible Books from Qwen2.5-72B-Instruct	Text		arXiv; National Institutes of Health ExPorter; BioRxiv; PMC Article; USPTO Backgrounds; peS2o; Global Regulation; CORE; PG-19; DOAB CC BY & CC BY-SA subset; NDLTD	Qwen2.5-72B-Instruct
Synthetic Common Crawl from Qwen3-30B-A3B and Mistral-Nemo-12B-Instruct	Text	415.8B	Common Crawl	Qwen3-30B-A3B; Mistral-NeMo-12B-Instruct
Synthetic Multilingual Data from Common Crawl from Qwen3-30B-A3B	Text		Common Crawl	Qwen3-30B-A3B
Synthetic Multilingual Data from Wikimedia from Qwen3-30B-A3B	Text		Wikimedia	Qwen3-30B-A3B
Synthetic Math Data from Wikimedia from Nemotron-4-340B-Instruct	Text		-	Nemotron-4-340B-Instruct
Synthetic Common Crawl Code from phi-4	Text	427.9B	Common Crawl	phi-4
Synthetic Scientific Coding from Qwen3-235B-A22B	Text	1.2B	Wikimedia	Qwen3-235B-A22B
Tool Calling Data	Text	26.2B		Qwen3-235B-A22B-2507; gpt-oss-120b
Synthetic Essential-Web from QwQ-32B	Text	28.1B	Essential-Web	QwQ-32B
Translated Synthetic Crawl	Text	389.9B	Common Crawl	Qwen3-30B-A3B
Translated Synthetic Wikipedia	Text	7.9B	Wikimedia	Qwen3-30B-A3B
Synthetic Art of Problem Solving from gpt-oss-120b and Qwen2.5-32B-Instruct	Text	Undisclosed	Art of Problem Solving; American Mathematics Competitions 8; American Mathematics Competitions 10	gpt-oss-120b; Qwen2.5-32B-Instruct
Synthetic Stack Exchange from gpt-oss-120b and Qwen2.5-32B-Instruct	Text	Undisclosed	Stack Exchange	gpt-oss-120b; Qwen2.5-32B-Instruct
Synthetic OpenCodeReasoning from DeepSeek-R1-0528	Text	Undisclosed	OpenCodeReasoning	DeepSeek-R1-0528
Synthetic HackerRank Coding from DeepSeek-R1-0528	Text	Undisclosed	HackerRank Coding Dataset	DeepSeek-R1-0528
Synthetic SWE-Gym from Qwen3-Coder-480B-A35B-Instruct	Text	Undisclosed	SWE-Gym	Qwen3-Coder-480B-A35B-Instruct
Synthetic Art of Problem Solving and Stack Exchange from gpt-oss-120b, Qwen2.5-32B-Instruct, and Goedel-Prover-V2-32B	Text	Undisclosed	Art of Problem Solving; American Mathematics Competitions 8; American Mathematics Competitions 10; Stack Exchange	gpt-oss-120b; Qwen2.5-32B-Instruct; Goedel-Prover-V2-32B
Synthetic Multilingual Science and Code data from DeepSeek-R1, DeepSeek-R1-0528, Qwen2.5-32B-Instruct, and Qwen3-235B-A22B, translated with Qwen2.5-32B-Instruct and Qwen2.5-14B-Instruct	Text	Undisclosed	Stack Exchange; SCP-116K; LIMO; TACO; Code Contest; Codeforces	DeepSeek-R1; DeepSeek-R1-0528; Qwen2.5-32B-Instruct; Qwen3-235B-A22B;
Synthetic Safety from DeepSeek-R1-0528, gpt-oss-120b and Mixtral-8x7B-v0.1	Text	Undisclosed	Nemotron Content Safety Dataset V2; Gretel Synthetic Safety Alignment Dataset; RedTeam-2K; Malicious Tasks; Nemotron-Personas-USA	DeepSeek-R1-0528; gpt-oss-120b; Mixtral-8x7B-v0.1
Synthetic STEM from Qwen3-235B-A22B-Instruct-2507 and gpt-oss-120b	Text	Undisclosed	arXiv; National Institutes of Health ExPorter; BioRxiv; PMC Article; USPTO Backgrounds; peS2o; Global Regulation; CORE; PG-19; DOAB CC BY & CC BY-SA subset; NDLTD	Qwen3-235B-A22B-Instruct-2507; gpt-oss-120b
Synthetic KernelBook from DeepSeek-R1-0528	Text	Undisclosed	KernelBook	DeepSeek-R1-0528
Synthetic Tool Calling from Qwen3-235B-A22B-Thinking-2507 and Qwen3-Next-80B-A3B-Thinking	Text	Undisclosed	ToolBench; glaive-function-calling-v2; APIGen Function-Calling; Nemotron-Personas-USA	Qwen3-235B-A22B-Thinking-2507; Qwen3-Next-80B-A3B-Thinking
Synthetic Chat from gpt-oss-120b, Mixtral-8x22B-Instruct-v0.1, Qwen3-235B-A22B-Instruct-2507 , and Qwen3-235B-A22B-Thinking-2507	Text	Undisclosed	C4; LMSYS-Chat-1M; ShareGPT; GSM8K; PRM800K; FinQA; WikiTableQuestions; Riddles; glaive-function-calling-v2; SciBench; tigerbot-kaggle-leetcodesolutions-en-2k; OpenBookQA; Advanced Reasoning Benchmark; Software Heritage; Khan Academy Math Keywords; WildChat-1M; Nemotron-Personas-USA	gpt-oss-120b; Mixtral-8x22B-Instruct-v0.1; Qwen3-235B-A22B-Instruct-2507; Qwen3-235B-A22B-Thinking-2507
Synthetic Long Context from Qwen3-235B-A22B-Instruct-2507	Text	Undisclosed	CORE; PG-19; DOAB CC BY & CC BY-SA subset; NDLTD	Qwen3-235B-A22B-Instruct-2507
Synthetic Tool Use Interactive Agent from gpt-oss-120b, DeepSeek-R1-0528, Qwen3-32B, and Qwen3-235B-A22B-Thinking-2507	Text	Undisclosed	NVIDIA Internal	gpt-oss-120b; DeepSeek-R1-0528; Qwen3-32B; and Qwen3-235B-A22B-Thinking-2507
Synthetic STEM from Qwen3-235B-A22B-Thinking-2507	Text	Undisclosed	ICHO-IPH0; Physics Big; Scale HLE; OpenMathReasoning; OpenCodeReasoning	Qwen3-235B-A22B-Thinking-2507
Synthetic DocFinQA and SWE-smith from Qwen3-Coder-480B-A35B-Instruct and Kimi-K2-Thinking	Text	Undisclosed	DocFinQA; SWE-smith	Qwen3-Coder-480B-A35B-Instruct; Kimi-K2-Thinking
Synthetic Math from gpt-oss-120b and Qwen2.5-32B-Instruct	Text	Undisclosed	-	gpt-oss-120b; Qwen2.5-32B-Instruct
Synthetic Essential-Web from gpt-oss-120b	Text	Undisclosed	Essential-Web	gpt-oss-120b
Synthetic Scale HLE from gpt-oss-120b	Text	Undisclosed	Scale HLE	gpt-oss-120b
Synthetic CDQuestions from gpt-oss-120b	Text	Undisclosed	CDQuestions	gpt-oss-120b
Synthetic Stack Exchange from gpt-oss-120b	Text	Undisclosed	Stack Exchange	gpt-oss-120b
Synthetic GPQA from gpt-oss-120b and Qwen2.5-32B-Instruct	Text	Undisclosed	Stack Exchange	gpt-oss-120b; Qwen2.5-32B-Instruct
Synthetic Vedantu from gpt-oss-120b	Text	Undisclosed	Vedantu	gpt-oss-120b
Synthetic SWE-Gym and R2E-Gym-Subset from Qwen3-Coder-480B-A35B-Instruct	Text	Undisclosed	SWE-Gym; R2E-Gym-Subset	Qwen3-Coder-480B-A35B-Instruct
Synthetic SWE-Gym from Qwen3-Coder-480B-A35B-Instruct	Text	Undisclosed	SWE-Gym	Qwen3-Coder-480B-A35B-Instruct
Synthetic SWE-Gym and R2E-Gym-Subset from DeepSeek-R1-0528	Text	Undisclosed	SWE-Gym; R2E-Gym-Subset	DeepSeek-R1-0528
Synthetic HelpSteer, LMSYS-Chat-1M, and Nemotron-Personas-USA from gpt-oss-120b, Qwen3-235B-A22B-Instruct-2507, and Qwen3-235B-A22B-Thinking-2507	Text	Undisclosed	HelpSteer2; HelpSteer3; LMSYS-Chat-1M; Nemotron-Personas-USA	gpt-oss-120b; Qwen3-235B-A22B-Instruct-2507; Qwen3-235B-A22B-Thinking-2507
Synthetic Structured Outputs from Qwen3-30B-A3B-Instruct-2507, Qwen3-30B-A3B-Thinking-2507, Qwen3-235B-A22B-Instruct-2507, and Qwen3-235B-A22B-Thinking-2507	Text	Undisclosed	-	Qwen3-30B-A3B-Instruct-2507; Qwen3-30B-A3B-Thinking-2507; Qwen3-235B-A22B-Instruct-2507; Qwen3-235B-A22B-Thinking-2507
Synthetic Search STEM MCQ from Qwen3-235B-A22B and DeepSeek-R1-0528	Text	Undisclosed	-	Qwen3-235B-A22B; DeepSeek-R1-0528
Synthetic Search STEM OPENQ from DeepSeek-R1-0528	Text	Undisclosed	-	DeepSeek-R1-0528
Synthetic OpenSTEM from Qwen2.5-32B-Instruct and DeepSeek-R1-0528	Text	Undisclosed	-	Qwen2.5-32B-Instruct; DeepSeek-R1-0528
Synthetic MCQ from Qwen2.5-32B-Instruct and DeepSeek-R1-0528	Text	Undisclosed	-	Qwen2.5-32B-Instruct; DeepSeek-R1-0528
Synthetic MCQ10 from DeepSeek-R1-0528	Text	Undisclosed	-	DeepSeek-R1-0528
Synthetic MCQ4 from Qwen3-235B-A22B, DeepSeek-R1-0528, and Qwen3-235B-A22B-Instruct-2507	Text	Undisclosed	-	Qwen3-235B-A22B; DeepSeek-R1-0528; Qwen3-235B-A22B-Instruct-2507
Synthetic OpenMathReasoning from gpt-oss-120b and Qwen2.5-32B-Instruct	Text	Undisclosed	OpenMathReasoning	gpt-oss-120b; Qwen2.5-32B-Instruct
Synthetic Offline Search MCQA HLE from DeepSeek-R1-0528	Text	Undisclosed	-	DeepSeek-R1-0528
Synthetic Offline Search MCQA GPQA from Qwen3-235B-A22B and DeepSeek-R1-0528	Text	Undisclosed	-	Qwen3-235B-A22B; DeepSeek-R1-0528
Synthetic Human Preference from QwQ-32B, Qwen3-30B-A3B, Qwen3-235B-A22B, Qwen3-235B-A22B-Instruct-2507, Mistral-Small-3.1-24B-Instruct-2503, Mistral-Small-3.2-24B-Instruct-2506, MiniMax-M1-80k, MiniMax-M1-40k, Kimi-K2-Instruct, DeepSeek-V3-0324, DeepSeek-R1-0528	Text	Undisclosed	-	QwQ-32B; Qwen3-30B-A3B; Qwen3-235B-A22B; Qwen3-235B-A22B-Instruct-2507; Mistral-Small-3.1-24B-Instruct-2503; Mistral-Small-3.2-24B-Instruct-2506; MiniMax-M1-80k; MiniMax-M1-40k; Kimi-K2-Instruct; DeepSeek-V3-0324; DeepSeek-R1-0528
Synthetic WildChat-1M and arena-human-preference-140k from DeepSeek-R1, gemma-2-2b-it, gemma-3-27b-it, gpt-oss-20b, gpt-oss-120b, Mistral-7B-Instruct-v0.3, Mixtral-8x22B-Instruct-v0.1, Nemotron-4-340B-Instruct, NVIDIA-Nemotron-Nano-9B-v2, Phi-4-mini-instruct, Phi-3-small-8k-instruct, Phi-3-medium-4k-instruct, Qwen3-235B-A22B, QwQ-32B	Text	Undisclosed	WildChat-1M; arena-human-preference-140k	DeepSeek-R1; gemma-2-2b-it; gemma-3-27b-it; gpt-oss-20b; gpt-oss-120b; Mistral-7B-Instruct-v0.3; Mixtral-8x22B-Instruct-v0.1; Nemotron-4-340B-Instruct; NVIDIA-Nemotron-Nano-9B-v2; Phi-4-mini-instruct; Phi-3-small-8k-instruct; Phi-3-medium-4k-instruct; Qwen3-235B-A22B; QwQ-32B
Synthetic Safety from DeepSeek-R1-0528, gpt-oss-120b, DeepSeek-R1-Distill-Qwen-7B, and Mixtral-8x7B-v0.1	Text	Undisclosed	Nemotron Content Safety Dataset V2; Gretel Synthetic Safety Alignment Dataset; RedTeam-2K; Malicious Tasks;	DeepSeek-R1-0528; gpt-oss-120b; DeepSeek-R1-Distill-Qwen-7B; Qwen3-30B-A3B-Thinking-2507; Qwen3-235B-A22B-Instruct-2507; Mixtral-8x7B-v0.1
Synthetic Code from Qwen3-32B	Text	Undisclosed	English Common Crawl; English Common Crawl 1.1	Qwen3-32B
Synthetic OpenCodeReasoning from DeepSeek-R1	Text	Undisclosed	OpenCodeReasoning	DeepSeek-R1
Synthetic LIMO from DeepSeek-R1-0528	Text	Undisclosed	LIMO	DeepSeek-R1-0528
Synthetic SCP from DeepSeek-R1-0528	Text	Undisclosed	SCP-116K	DeepSeek-R1-0528
Synthetic Stack Exchange from DeepSeek-R1-0528	Text	Undisclosed	Stack Exchange	DeepSeek-R1-0528
Synthetic Common Crawl from Qwen3-30B-A3B	Text	Undisclosed	Common Crawl	Qwen3-30B-A3B
Synthetic Wikipedia from Qwen3-30B-A3B	Text	Undisclosed	Wikimedia	Qwen3-30B-A3B
Synthetic Essential-Web from Qwen3-30B-A3B and Qwen3-235B-A22B-Thinking-2507	Text	Undisclosed	Essential-Web	Qwen3-30B-A3B; Qwen3-235B-A22B-Thinking-2507
Synthetic Textbook Math from Qwen3-30B-A3B, Qwen3-235B-A22B, phi-4	Text	Undisclosed	Common Crawl; FineMath	Qwen3-30B-A3B; Qwen3-235B-A22B; phi-4
Synthetic Math and Code from DeepSeek-R1 and DeepSeek-R1-0528	Text	Undisclosed	Magicoder-Evol-Instruct-110K; opc-sft-stage2; TACO; OpenCodeReasoning; OpenMathReasoning; NuminaMath CoT	DeepSeek-R1; DeepSeek-R1-0528
Synthetic Nemotron-Personas-USA from gpt-oss-120b and Qwen3-8B	Text	Undisclosed	Nemotron-Personas-USA	gpt-oss-120b; Qwen3-8B

Training Dataset

Dataset	# of Tokens in Nemotron Nano 2	# of Tokens in Nemotron 3 Nano
English Common Crawl	3,360,110,334,818	3,456,523,212,210
English Synthetic CC	1,949,464,641,123	4,340,740,677,920
Crawl++	360,389,153,262	360,389,153,262
Math	124,606,230,663	154,217,502,165
Synthetic Math	73,007,767,155	73,007,767,155
Code	747,409,228,724	1,043,856,922,136
Synthetic Code	175,067,553,293	453,117,917,176
Common Crawl Code	0	263,072,374,097
English Wiki	17,349,266,926	17,349,266,926
Synthetic Wiki	0	7,850,648,552
Books	0	0
Papers	191,586,493,365	191,586,493,365
PDF-to-text	141,096,578,533	141,096,578,533
Code SFT	60,025,726,817	102,863,752,325
STEM SFT	272,680,426,295	359,826,214,274
General SFT	6,057,478,645	6,057,478,645
Tool-Calling SFT	0	26,244,716,867
Multilingual	2,172,261,909,350	1,743,892,490,859
Synthetic multilingual	997,710,364,950	595,140,661,135
Total	10,648,823,153,919	13,336,833,827,602

We use a considerable amount of synthetic data. Out of 10.6 trillion tokens, 3,534,013,958,278 tokens are synthetically generated.

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. Additionally, we used data from Wikipedia and FineWeb-2 (Penedo et al., 2025) for these fifteen languages as well as four additional languages: Czech, Finnish, Hebrew, and Hindi.

Language	Total Tokens
Arabic	118,056,362,726
Danish	117,747,321,618
German	146,613,691,781
Spanish	469,156,575,409
French	139,982,002,289
Italian	298,858,370,174
Japanese	682,755,693,336
Korean	127,099,747,538
Dutch	89,041,592,681
Polish	105,356,493,147
Portuguese	243,249,275,089
Russian	185,314,014,057
Swedish	74,954,953,299
Thai	160,778,944,467
Chinese	211,007,236,689

We collect a total of 922,476,782,017 tokens of code in 43 different languages.

Language	Tokens
Assembly	750,628,764
C	42,657,300,868
C#	56,153,329,307
C++	67,773,701,658
CommonLisp	263,234,672
CSS	38,848,760,035
Cuda	400,222,993
Dart	3,816,960,470
Dockerfile	474,958,084
Fortran	1,105,049,387
Go	8,332,419,480
Haskell	1,294,613,669
HTML	69,082,117,487
Java	131,440,465,822
JavaScript	75,573,420,861
JSON	15,366,881,241
Julia	621,046,949
JupyterNotebook	2,241,893,197
Lua	4,146,420,802
Makefile	12,640,010,879
Markdown	64,796,743,311
Mathematica	320,504,225
OmniversePython	26,946,093
Pascal	1,625,013,876
Perl	1,575,314,434
PHP	61,575,339,005
Python	126,916,727,384
R	19,811,381,935
reStructuredText	1,779,876,391
Ruby	6,446,962,615
Rust	4,438,640,533
Scala	3,343,959,154
Shell	18,758,779,250
SQL	23,205,633,085
Swift	5,976,714,881
SystemVerilog	233,056,185
TeX	7,347,157,527
TypeScript	15,657,838,582
Verilog	811,884,369
VHDL	648,401,444
VisualBasic.NET	1,005,680,881
XML	12,616,779,741
YAML	10,574,010,491

Language Distribution in Post-Training

For our post-training recipe, we focused on 5 main languages in addition to English: Spanish, French, Japanese, Italian, German.
Those languages were represented in the form of multilingual reasoning and translation task.

The following table depicts our sample distribution for the 6 languages and 5 translation pairs.

Language	Size
English	16.2 M
Italian	0.252M
German	0.252M
Spanish	0.252M
French	0.252M
Japanese	0.252M
English <-> Italian	108k
English <-> German	108k
English <-> Spanish	108k
English <-> French	108k
English <-> Japanese	108k

Evaluation Dataset

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

Inference

Engines: HF, vLLM, TRT-LLM, SGLang, Llama.cpp
Test Hardware: NVIDIA B200 192GB, RTX PRO 6000 96GB, Jetson Thor, DGX Spark

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 Trustworthy AI 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 security vulnerabilities or NVIDIA AI Concerns here.

Citation

@misc{nvidia_nemotron_nano_v3_2025,
  title  = {{Nemotron 3 Nano}: Open, Efficient Mixture-of-Experts Hybrid {Mamba}-{Transformer} Model for {Agentic} Reasoning},
  author = {{NVIDIA}},
  year   = {2025},
  url    = {https://arxiv.org/abs/2512.20848},
  note   = {Technical report}
}

Loron200/NVIDIA-Nemotron-3-Nano-30B-A3B-NVFP4