NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-Base-BF16
Looking for a post-trained model? See the BF16 release for customization or the NVFP4 release for optimized deployment.
Model Overview
Model Developer: NVIDIA Corporation
Description
NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-Base-BF16 is the base vision-language checkpoint of the Nemotron 3.5 Super family. It provides a starting point for continued training, domain adaptation, and developing custom post-trained vision-language models.
The language backbone is initialized from Nemotron 3 Super Base and further trained through distillation from Nemotron 3 Ultra Base. The model underwent continued pre-training on indic languages, long-context training, and software-engineering mid-training. A vision encoder and connector extend the language backbone to visual inputs.
The model combines interleaved Mamba-2, Mixture-of-Experts (MoE), and attention layers with a C-RADIO vision encoder and a vision-language connector. It has 120B total parameters and 12B active parameters and supports context lengths up to 1M tokens. Multi-Token Prediction (MTP) enables native speculative decoding.
This model is ready for commercial or non-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.
License/Terms of Use
Governing Download Terms: Use of this model is governed by the OpenMDW License Agreement, version 1.1.
Benchmarks
The following results evaluate the Nemotron 3.5 Super text-only base backbone, before vision-language training.
| Benchmark | Metric | Score (%) |
|---|---|---|
| MMLU | Exact match | 84.14 |
| MMLU-Pro | Exact match, TigerLab extraction | 76.25 |
| AGIEval-En | CoT exact match | 75.65 |
| SuperGPQA | CoT | 47.65 |
| GPQA Diamond | 5-shot, CoT exact match | 66.16 |
| GSM8K | 8-shot, CoT exact match | 91.74 |
| Minerva MATH | Exact match | 87.66 |
| MATH-500 | 4-shot, sampled pass@1, n=32 | 81.56 |
| HumanEval | EvalPlus sanitized, sampled pass@1, n=32 | 81.31 |
| MBPP-Sanitized | 3-shot, sampled pass@1, n=32 | 79.06 |
| LiveCodeBench CodeGen | Pass@1 | 47.61 |
| BigCodeBench Complete | Greedy | 48.42 |
| BigCodeBench Instruct | Greedy | 49.39 |
| CommonsenseQA | 7-shot, accuracy | 85.26 |
| ARC-Challenge | 25-shot, normalized accuracy | 95.99 |
| HellaSwag | 10-shot, normalized accuracy | 88.96 |
| OpenBookQA | 0-shot, normalized accuracy | 48.80 |
| PIQA | 0-shot, normalized accuracy | 85.09 |
| TruthfulQA MC2 | 0-shot, accuracy | 71.18 |
| WinoGrande | 5-shot, accuracy | 79.08 |
| SocialIQA | 0-shot, accuracy | 49.03 |
| RACE | 0-shot, accuracy | 91.39 |
| Global MMLU | Average across 8 languages | 86.56 |
| MGSM | 8-shot, native CoT, average across 6 languages | 87.53 |
Deployment Geography: Global
Use Case
This model is intended for developers and researchers building custom vision-language models through continued pre-training, supervised fine-tuning, reinforcement learning, and distillation.
Release Date
Hugging Face - 10/15/2026 via Hugging Face
Reference(s)
- NVIDIA Nemotron 3 model family on Hugging Face
- NVIDIA Nemotron 3.5 Super VL and Lightning Technical Report
- NVIDIA Nemotron 3 Ultra Technical Report
Model Architecture
- Architecture Type: Mamba-2–Transformer hybrid Mixture of Experts (MoE) with Multi-Token Prediction (MTP)
- Network Architecture: Nemotron Hybrid MoE with 512 routed experts per MoE layer, a C-RADIO vision encoder, and a vision-language connector
- Number of Model Parameters: 120B total / 12B active
Model Design
The model combines sparse expert computation, attention, and Mamba-2 layers to support long-context processing. The vision encoder and connector turn visual inputs into representations processed by the language backbone. A shared MTP head predicts future tokens recursively for speculative decoding.
The model supports up to 1M tokens of context. The prompt, visual inputs, conversation history, tool results, and generated response share this context window. Individual deployment recipes may use a smaller context window to accommodate the available GPU memory and concurrency.
Training Methodology
Stage 1: Foundation and Multi-Token Prediction
The language backbone is initialized from Nemotron 3 Super Base. A shared MTP head is trained for 100B tokens while the backbone remains frozen, followed by 1T tokens of distillation from Nemotron 3 Ultra Base.
Stage 2: Continued Pre-Training and Mid-Training
The model undergoes 100B tokens of Indic continued pre-training, a 35B-token extension to 1M context, 67B tokens of software-engineering mid-training at 256K context, and a final 9B-token long-context recovery stage. Software-engineering data includes repositories, issue–pull-request pairs, commit diffs, build and CI logs, and agent trajectories.
Stage 3: Vision-Language Training
A C-RADIO vision encoder and connector extend the language backbone to visual inputs, including images, document pages, and video frames.
Input
- Input Type(s): Text, images, and video
- Input Format(s): Text strings, images, and sampled video frames
- Video: mp4, up to 2 minutes.
- Image: Red, Green, Blue (RGB) (jpeg, png)
- Input Parameters:
- Video: Three-Dimensional (3D)
- Image: Two-Dimensional (2D)
- Text: One-Dimensional (1D)
- Other Properties Related to Input: Maximum context length up to 1M tokens. Documents can be supplied as page images with accompanying text.
Output
- Output Type(s): Text
- Output Format: String
- Output Parameters: One-dimensional 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): PyTorch; Megatron-LM; NeMo
- Hardware:
- 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.
Model Version(s)
- v1.0 - GA
Training, Testing, and Evaluation Datasets
Training
Data Modality: Text, images, and video
Foundation Training Data Size: More than 20 trillion tokens
Image Training Data Size: 1 Million to 1 Billion Images
Text Training Data Size: More than 10 Trillion Tokens
Video Training Data Size: 10,000 to 1 Million Hours
Foundation Data Collection Period: 2013 to December 2025
Data Collection Method: Hybrid: automated, manually-collected, and synthetic
Labeling Method: Hybrid: automated, manually-labeled, and synthetic
The foundation corpus contains curated and synthetic text covering English, 19 other spoken languages, and 43 programming languages. Sources include webpages, dialogue, articles, code, and other written materials across math, science, legal, finance, and general knowledge domains.
For more information about the datasets used to train this model, please see the Public Summary of Training Content
Detailed dataset information
Base Pre-Training Corpus (Nemotron 3 Foundation)
The foundation of the model is trained on the Nemotron 3 corpus, comprising the following datasets from the Nemotron Pretraining Datasets collection:
| Dataset Collection | Token Counts | Description |
|---|---|---|
| Nemotron-CC-v2 & v2.1 | 9.1T | A massive collection of English web data filtered from Common Crawl, including 2.5T+ tokens of new organic, translated, and synthetically rephrased content. |
| Nemotron-CC-Code-v1 | 427.9B | High-quality code tokens extracted from Common Crawl using the Lynx + LLM pipeline to preserve structure and equations. |
| Nemotron-Pretraining-Code-v1 & v2 & v3 | 1.7T | Curated GitHub code references with multi-stage filtering, deduplication, and large-scale synthetic code data. |
| Nemotron-CC-Math-v1 | 133.3B | High-quality math pre-training dataset preserving LaTeX formatting and mathematical structures. |
| Nemotron-Pretraining-Specialized-v1 & v1.1 & v1.2 & Nemotron-Pretraining-SFT-v1 | 660.0B | Synthetic datasets targeting specialized domains such as STEM reasoning and scientific coding. |
| Nemotron-Pretraining-Legal-v1 | 4.3B | Synthetic datasets targeting the legal domain. |
Public 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 |
| 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 |
| 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 |
| MetaMathQA | 10/2/2025 |
| simple-arithmetic-problems | 10/2/2025 |
| arithmetic | 10/2/2025 |
| Skywork-OR1-RL-Data | 10/2/2025 |
| FastChat | 10/2/2025 |
| News Commentary | 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 |
| SVG-Stack, Training Subset | Undisclosed |
| OS-Atlas, FineWeb-Derived Web-GUI Grounding Subset | Undisclosed |
| NemoOCR Simplified-Chinese Bounding-Box Labels, Safety-Filtered | 2022-2026 |
| NemoOCR Traditional-Chinese Bounding-Box Labels, Safety-Filtered | 2022-2026 |
| CoSyn-400K Document Subset, Safety-Filtered | 2022-2026 |
| NVClip (NV-CC-Img-Text-Dataset) | 2022-2026 |
| ScaleCUA, Processed Training Subset | 2022-2026 |
| RoboInter-VQA | 2022-2026 |
| Open Images V6, Object-Detection Training Records | 2022-2026 |
| YT1B PLM Video QA, Custom No-Thinking and Safety-Filtered | 2022-2026 |
| YT1B PLM Dense Captions, No-Thinking and Safety-Filtered | 2022-2026 |
| RoboVQA | 2022-2026 |
| YT1B PLM Thinking Traces, Safety-Filtered | 2022-2026 |
| Boyi Short-Video QA | 2022-2026 |
| Boyi Short-Video QA, Custom No-Thinking and Safety-Filtered | 2022-2026 |
| K710 Video Classification, Full Training Snapshot | 2022-2026 |
| Molmo2-VideoCapQA | 2022-2026 |
| Molmo2-VideoCapQA, Custom No-Thinking and Safety-Filtered | 2022-2026 |
| Molmo2-VideoPoint, Custom No-Thinking and Safety-Filtered | 2022-2026 |
| Molmo2-VideoPoint | 2022-2026 |
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 used in the Nemotron-CC data pipeline. 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.
| 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 |
| GitHub Crawl 1.1 | Text | 172.7B | 9/30/2025 | NVIDIA Advanced Deep Learning Research |
| Sangraha | Text | 109,263,115 indexed source sequences: 77,231,480 verified and 32,031,635 unverified | Undisclosed | NVIDIA Advanced Deep Learning Research |
| LITA Chunked Video Labels, Custom No-Thinking Dense Captions | Text (video labels) | 151,349 records | Undisclosed | NVIDIA Advanced Deep Learning Research |
| LITA Chunked Video Labels, Cleaned v2 | Text (video labels) | 150,434 records | Undisclosed | NVIDIA Advanced Deep Learning Research |
| Single-Page Document Labels, Qwen3.5 v2 | Text and images | 50,930 records | Undisclosed | NVIDIA |
| Processed YouTube Videos, Cosmos Dense Captions | Text and video | 274,472 records | Undisclosed | NVIDIA |
Private Non-publicly Accessible Datasets of Third Parties
| Dataset | Model(s) used |
|---|---|
| Global Regulation | Unknown |
| TAUS Translation Memory | Unknown |
| Scale HLE | Unknown |
| HackerRank Coding | Unknown |
| RL data for Search | Gemini 3; GPT-5 |
| KeenSight Batch 1, Relabeled Subset | Qwen3.5-122B-A10B |
| KeenSight Batch 1, Remaining Subset | Qwen3.5-122B-A10B |
| Camio Multilingual OCR and Document Layout | Undisclosed |
| Chinese Visual Question Answering, Relabeled Subset | Qwen3.5-122B-A10B |
| Camio Chinese OCR, Cleaned Variant | Qwen3.5-122B-A10B |
| Camio English OCR, Cleaned Variant | Qwen3.5-122B-A10B |
| KeenSight Batch 2, Remaining Subset | Qwen3.5-122B-A10B |
| KeenSight Batch 1, Safety-Filtered Trace Variant | Qwen3.5-122B-A10B |
| Chinese Visual Question Answering, Safety-Filtered Trace Variant | Qwen3.5-122B-A10B |
| Camio Chinese Question Answering, Verified-Correct Subset | Qwen3.5 |
| KeenSight Chinese OCR, Filtered Subset | DeepSeek-OCR |
| Camio Synthetic Trading-Graph Question Answering | Undisclosed |
| Camio Synthetic Menu and Receipt Question Answering | Qwen3.5-122B-A10B |
| Camio Chinese OCR, Formatted Variant | Qwen3.5-122B-A10B |
| Camio Synthetic Time-Series Question Answering | Undisclosed |
| Camio English OCR, Formatted Variant | Qwen3.5-122B-A10B |
| KeenSight Batch 2, Relabeled and Filtered Subset | Qwen3.5-122B-A10B; Gemini 3 Flash Preview (filtering) |
Camio Chinese Letter QA, Cleaned entrace_cleaned Variant |
Qwen3-VL-30B-A3B-Thinking-FP8; gpt-oss-120b; Qwen3.5-122B-A10B; GPT-4o mini (filtering and quality checks) |
| Camio Chinese Letter QA, Cleaned Variant | Qwen3-VL-30B-A3B-Thinking-FP8; gpt-oss-120b; Qwen3.5-122B-A10B; GPT-4o mini (filtering and quality checks) |
Camio Chinese Text QA, Cleaned entrace_cleaned Variant |
Qwen3-VL-30B-A3B-Thinking-FP8; gpt-oss-120b; Qwen3.5-122B-A10B; GPT-4o mini (filtering and quality checks) |
Private Non-publicly Accessible Datasets by NVIDIA
| Dataset | Model(s) used |
|---|---|
| Simple Minesweeper | Undisclosed |
| Simple Sudoku | Undisclosed |
| Multitool Typewriter Hard | Undisclosed |
| Machine Translation of News Commentary and TAUS Translation Memory | Undisclosed |
| Machine Translation of STEM - | Qwen2.5-14B-Instruct |
| Competitive Coding RL data from Nemotron Cascade | Undisclosed |
| Long context RL | Undisclosed |
| Single-step SWE RL for patch generation | Undisclosed |
| OpenHands SWE | Undisclosed |
| Lean Formal Proofs — RL | Undisclosed |
| Document-Grounded Finance Reasoning, Patch Tasks — RL | Undisclosed |
| Math CoT — SFT | Kimi K3 |
| Chat — SFT | Undisclosed |
| Chat, Reasoning Off — SFT | Undisclosed |
| Conversational Tool-Use Simulation — RL | Undisclosed |
| Math CoT, Low Variant — SFT | Kimi K3 |
| Document-Grounded Finance Reasoning — SFT | Undisclosed |
| Safety Preference v0.3 — RL | Undisclosed |
| Response-Length Instruction Following, Reasoning Off — SFT | Undisclosed |
| Math Python TIR, Low Variant — SFT | Kimi K3 |
| Math Python TIR — SFT | Kimi K3 |
| General-Purpose Tool Calling — SFT | Qwen3.5-397B-A17B; Gemma-4-31B-it |
| Knowledge Agent — SFT | Undisclosed |
| DataFunnel arXiv Science4 v1.0 — RL | DeepSeek-V4-Flash (problem extraction); GLM-5.2 (refinement); Kimi K3 and K3 V5 (verification and filtering); Super 3.5 r14 (difficulty profiling); Qwen3-235B-A22B-Instruct-2507-FP8 (reward judging) |
| Structured Outputs v3 — RL | gpt-oss-120b |
| Response-Length Instruction Following — SFT | Undisclosed |
| Filtered CUDA Coding — SFT | Undisclosed |
| Reasoning Gym — RL | Undisclosed |
| Competitive Coding Challenges v2 — SFT | GLM-5.2 |
| Multi-Turn Instruction Following, Reasoning Off — SFT | Undisclosed |
| Multi-Turn Instruction Following — SFT | Undisclosed |
| Instruction Following, Filtered RLHF Prompts — SFT | Undisclosed |
| Citation Formatting — RL | gpt-oss-120b |
| Long-Context General Data — SFT | Undisclosed |
| Free-Form Formatting — RL | gpt-oss-120b |
| Structured Outputs v4 — RL | Undisclosed |
| Tool-Call Schema — RL | No model used for RL conversion; source-generation models undisclosed |
| Competitive Coding, OCR Python — SFT | DeepSeek-V4-Pro |
| Lean Formal Proofs — SFT | gpt-oss-120b; Goedel-Prover-V2-32B; Qwen2.5-32B-Instruct |
| Citation Instruction Following — RL | Undisclosed |
| Search Graph Walks — SFT | MiniMax-M2 |
| General-Purpose Tool Calling, Reasoning Off — SFT | Undisclosed |
| Knowledge Agent Tau3, Reasoning Off — SFT | Undisclosed |
| ARC-AGI Puzzle Reasoning — SFT | Kimi-K2.5; Qwen3-235B-A22B-Thinking-2507; DeepSeek-V3.2; Qwen3-Next-80B-A3B-Thinking; GLM-4.5-Air-FP8; gpt-oss-120b; GLM-4.7-FP8; Ring-1T; Ministral-3-14B-Reasoning-2512 |
| Calendar — RL | Undisclosed |
| ACEProof — SFT | Undisclosed |
| Long Translation — SFT | Undisclosed |
| Agentless Software Engineering — SFT | Undisclosed |
| Long-Context PDF Translation v1.0 — SFT | DeepSeek-V4-Flash (translation generation); GPT-5.6 Sol (filtering and judging) |
NVIDIA-Sourced Synthetic Datasets
| Dataset | Modality | Dataset Size | Seed Dataset | Model(s) used for generation |
|---|---|---|---|---|
| Indic SDG for Nemotron | Text | 711,276,565 indexed source sequences: 528,835,469 native-script and 182,441,096 romanized | nvidia/Nemotron-CC-v2 | google/gemma-4-31B-it; google/gemma-4-31B-it-assistant |
| Nemotron-Pretraining-Fact-Seeking | Text | 35.0B | FineWiki | Qwen3-30B-A3B-Instruct-2507 |
| Nemotron-Pretraining-Legal | Text | 4.3B | CommonPile (caselaw_access_project_filtered); California Code of Regulations; Judicial Ethics Opinions; GLOBALCIT; CUAD; Nemotron Personas; ToSDR Terms of Service Corpus; CodeHima/TOS_Dataset; ContractNLI; CaseHOLD; Code of Federal Regulations; Canadian Case Law (subsets that allow commercial use) | Qwen3-235B-A22B-Thinking-2507 |
| Nemotron-Pretraining-Formal-Logic | Text | 128M | Nemotron Personas | Qwen3-235B-A22B-Thinking-2507 |
| Nemotron-Pretraining-Economics | Text | 73.4M | - | Qwen3-235B-A22B-Thinking-2507 |
| Nemotron-Pretraining-Multiple-Choice | Text | 1.6B | MMLU Auxiliary Train | DeepSeek-V3; Qwen3-235B-A22B |
| Nemotron-Pretraining-Code-Concepts | Text | 7.3B | - | gpt-oss-20b; gpt-oss-120b |
| Nemotron-Pretraining-Unconditional-Algorithmic | Text | 196.5M | - | gpt-oss-120b; Qwen3-235B-A22B |
| More Synthetic Tasks from DeepSeek-V3 and Qwen3-235B-A22B | Text | 1.1B | train splits of acp_bench; ai2_arc; babi; gsm8k; hendrycks_math; IFEval; MedText; mediqa_qa; mlqa; MMLU-Pro; mmlu-pro-plus; MMLU-ProX; nq_open; tinyGSM8k; truthful_qa; truthfulqa-multi; MATH-lighteval; mmlu; awesome-chatgpt-prompts; super_glue | DeepSeek v3; Qwen3-235B-A22B |
| Synthetic Tasks from DeepSeek-V3 and Qwen3-235B-A22B | Text | 6.7B | train splits of Into the Unknown; AI2 ARC (AI2 Reasoning Challenge); BLiMP (Benchmark of Linguistic Minimal Pairs); CommonSenseQA; GLUE; HeadQA; Hendrycks Ethics; Memo Trap; modus-tollens; NeQA; pattern-matching-suppression; mastermind_24_mcq_random; mastermind_24_mcq_close; quote-repetition; redefine-math; Repetitive Algebra; sig-figs; MMLU-Pro; MC-TACO; MedConceptsQA; MMLU_dataset; OpenbooksQA; PIQA (Physical Interaction Question Answering); SocialIQA; SuperGLUE; tinyAI2_arc; tinyMMLU; tinyWinogrande; TruthfulQA; WebQuestions; Winogrande; GPQA; MBPP | DeepSeek v3; Qwen3-235B-A22B |
| 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 Qwen3-235B-A22B-Thinking-2507 and Mixtral-8x22B-v0.1 | Text | 15.2M | social-chemestry-101; Moral Stories | Qwen3-235B-A22B-Thinking-2507; Mixtral-8x22B-v0.1 |
| 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 | Undisclosed | 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 | 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 | 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 | Undisclosed | Common Crawl | Qwen3-30B-A3B |
| Synthetic Multilingual Data from Wikimedia from Qwen3-30B-A3B | Text | Undisclosed | Wikimedia | Qwen3-30B-A3B |
| Synthetic Math Data from Wikimedia from Nemotron-4-340B-Instruct | Text | Undisclosed | - | 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 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 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 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 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 Stack Exchange from gpt-oss-120b and Qwen2.5-32B-Instruct | Text | Undisclosed | Stack Exchange | gpt-oss-120b; Qwen2.5-32B-Instruct |
| Synthetic Stack Exchange from gpt-oss-120b | Text | Undisclosed | Stack Exchange | gpt-oss-120b |
| 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 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 Essential-Web from gpt-oss-120b | Text | Undisclosed | Essential-Web | gpt-oss-120b |
| 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 Math from gpt-oss-120b and Qwen2.5-32B-Instruct | Text | Undisclosed | - | gpt-oss-120b; Qwen2.5-32B-Instruct |
| Synthetic OpenMathReasoning from gpt-oss-120b and Qwen2.5-32B-Instruct | Text | Undisclosed | OpenMathReasoning | gpt-oss-120b; Qwen2.5-32B-Instruct |
| Synthetic KernelBook from DeepSeek-R1-0528 | Text | Undisclosed | KernelBook | DeepSeek-R1-0528 |
| 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 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 Search STEM MCQ from Qwen3-235B-A22B and DeepSeek-R1-0528 | Text | Undisclosed | - | Qwen3-235B-A22B; DeepSeek-R1-0528 |
| Synthetic OpenSTEM 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 |
Additional Training Data
| Data | Composition and Purpose |
|---|---|
| Indic continued pre-training | 100B tokens: 10B selected Indic tokens and 90B tokens from the preceding blend. |
| Software-engineering mid-training | 67B tokens: whole repositories (20%), issue–pull-request pairs (41%), commit diffs (3%), build and CI logs (1%), agent traces (10%), pre-training replay (20%), and long-context data (5%). Sources are decontaminated against SWE-bench and Terminal-Bench repositories. |
| Long-context pre-training | A 35B-token extension to 1M context, followed by a 9B-token long-context recovery stage. |
| Vision-language data | Image and video data from the Nemotron 3 Nano Omni training corpus, including visual question answering, charts, tables, OCR, multi-page documents, visual grounding, and video understanding. |
Testing Datasets
Data Collection Method: Hybrid: automated, manually-collected, and synthetic
Labeling Method: Hybrid: automated, manually-labeled, and synthetic
Properties: Standard benchmarks and task-specific test suites covering knowledge, math, code, commonsense reasoning, reading comprehension, multilingual understanding, and long context.
Evaluation Datasets
Data Collection Method: Hybrid: automated, manually-collected, and synthetic
Labeling Method: Hybrid: automated, manually-labeled, and synthetic
Properties: Standard benchmarks and task-specific test suites covering knowledge, math, code, commonsense reasoning, reading comprehension, multilingual understanding, and long context.
Inference
Acceleration Engine: PyTorch
Test Hardware:
- NVIDIA Hopper
- H100
- H200
- NVIDIA Grace Blackwell
- GB200
- GB300
- NVIDIA Blackwell
- B200
- B300
- NVIDIA Hopper
The Base model is intended as a starting point for further training. For chat, reasoning, and tool-calling applications, use the post-trained BF16 or NVFP4 release and its deployment instructions.
Download the Base weights with the Hugging Face CLI:
hf download nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-Base-BF16 \
--local-dir ./models/super-vl-base-bf16
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.
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