NVIDIA Nemotron v3
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NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16
Model Overview
Model Developer: NVIDIA Corporation
Model Dates:
September 2025 - December 2025
Data Freshness:
The pre-training data has a cutoff date of June 25, 2025.
Description
NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16 is a base large language model (LLM) trained from scratch by NVIDIA, with the next token prediction loss. It provides a good starting point for instruction fine-tuning.
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.
Feature Voting
We want to hear from you! Share your ideas, vote on what matters, and help shape the future of Nemotron.
License/Terms of Use
GOVERNING TERMS: Use of this model is governed by the NVIDIA Open Model License Agreement.
Base Benchmark Evaluations
We evaluated our model on the following benchmarks:
| Task | Qwen3 30B-A3B-Base | NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16 |
|---|---|---|
| General Knowledge | ||
| MMLU (5-shot, acc) | 81.07 | 78.56 |
| MMLU-Pro (5-shot, CoT EM) | 61.71 | 65.05 |
| AGIEval-En (3/5-shot, CoT acc) | 63.12 | 68.32 |
| Code | ||
| HumanEval (0-shot) | 70.73 | 78.05 |
| MBPP-Sanitized (3-shot) | 73.15 | 75.49 |
| Math | ||
| GSM8K (8-shot, acc) | 89.01 | 92.34 |
| MATH (4-shot, acc) | 61.14 | 82.88 |
| MATH-500 (4-shot, avg@32) | 55.08 | 78.63 |
| Commonsense Understanding | ||
| ARC-Challenge (25-shot, acc_norm) | 94.45 | 91.89 |
| HellaSwag (10-shot, acc_norm) | 83.14 | 85.56 |
| OpenBookQA (0-shot, acc_norm) | 44.80 | 46.20 |
| PIQA (0-shot, acc_norm) | 81.01 | 84.33 |
| WinoGrande (5-shot, acc) | 78.22 | 79.64 |
| Reading Comprehension | ||
| RACE (0-shot, acc) | 90.05 | 88.04 |
| Multilingual | ||
| MMLU Global Lite (5-shot, avg acc) | 76.84 | 74.47 |
| MGSM (8-shot, avg acc) | 82.53 | 83.00 |
| Long Context | ||
| RULER (64K, 0-shot, acc) | 63.55 | 87.50 |
| RULER (128K, 0-shot, acc) | 60.69 | 82.92 |
| RULER (256K, 0-shot, acc) | Not Supported | 75.44 |
| RULER (512K, 0-shot, acc) | Not Supported | 70.56 |
All evaluation results were collected via Nemo Evaluator SDK and LM Evaluation Harness. The open source container on LM Evaluation Harness packaged via NVIDIA's Nemo Evaluator SDK used for evaluations can be found here. A reproducibility tutorial along with all configs can be found in Nemo Evaluator SDK examples.
Deployment Geography: Global
Use Case
This model is intended for developers and researchers building instruction-following LLMs.
Supported languages include: English, Spanish, French, German, Japanese, Italian, Chinese, Arabic, Hebrew, Hindi, Korean, Czech, Danish, Dutch, Finnish, Polish, Portuguese, Thai, Swedish, and Russian.
Release Date:
December 15, 2025 via Hugging Face
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 Mixture of Experts (MoE)
Network Architecture: Nemotron Hybrid MoE
Number of model parameters: 30B
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
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: 128K tokens
Other Properties Related to Input: Supported languages include: English, Spanish, French, German, Japanese, Italian, Chinese, Arabic, Hebrew, Hindi, Korean, Czech, Danish, Dutch, Finnish, Polish, Portuguese, Thai, Swedish, and Russian.
Output
Output Type(s): Text
Output Format: String
Output Parameters: One-Dimensional (1D): Sequences
Maximum output size: 128K 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.
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.
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-Base-BF16"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto"
)
prompt = "The capital of France is"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=32,
do_sample=False,
eos_token_id=tokenizer.eos_token_id
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Model Version(s)
- v1.0
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
NVIDIA-Nemotron-3-Nano-30B-A3B-Base-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.
Alongside the model, we release our final pre-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.
Public dataset
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 |
|---|
| 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 |
| Synthetic Rephrased Math Data from Common Crawl from phi-4 | Text | 73B | Common Crawl | phi-4 |
| Synthetic Math Data from Common Crawl 4plus | Text | 52.3B | Common Crawl | phi-4 |
| Synthetic Math Data from Common Crawl 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 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 Search STEM OPENQ from DeepSeek-R1-0528 | Text | Undisclosed | - | DeepSeek-R1-0528 |
| Synthetic MCQ from Qwen2.5-32B-Instruct and DeepSeek-R1-0528 | Text | Undisclosed | - | Qwen2.5-32B-Instruct; DeepSeek-R1-0528 |
| 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 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 |
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.
| 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 |
Evaluation Dataset
- Data Collection Method by dataset: Hybrid: Human, Synthetic
- Labeling Method by dataset: Hybrid: Automated, Human, Synthetic
Inference
Engines: HF, vLLM, TRT-LLM
Test Hardware: NVIDIA A100 80GB, H100 80GB
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 & Security.
For more detailed information on ethical considerations for this model, please see the Model Card++ Bias, Explainability, 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://research.nvidia.com/labs/nemotron/files/NVIDIA-Nemotron-3-Nano-Technical-Report.pdf},
note = {Technical report}
}
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