alt.freeweights.nemotron / NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4

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NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4

Date: 2026-11-15Commit: e8e360dSigned: 2DC28CC7B26A283B ✓

Image text to text model with 65.8B parameters. Safetensors weights, 75.1 GB in all. Published by unsloth under OpenMDW-1.1.

65.8B params

75.1GBNVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4safetensorscustom terms

FactsParams65.8BSize75.1 GBFormatssafetensorsTaskimage text to textLicenseOpenMDW-1.1From sourcecustom termsread terms

# custom terms: read the license before you download
$ fw get unsloth/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4
# manifest sha256 97aaeb4eac6b… · 75.1 GB · 109 files
$ fw verify ./NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4 unsloth/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4

From sourceHosted (signed)unsloth/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4Last check: 2026-11-15 00:26 UTC

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vLLM exo Text Generation WebUI

SGLang MLC LLM KTransformers LMCache Xinference LMDeploy mistral.rs Mooncake FlashInfer

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AIBrix fastllm ExLlamaV3

Transformers LocalAI tinygrad Triton Inference Server BentoML CTranslate2 ExLlamaV2 LitServe

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mergekit TensorRT Model Optimizer

LLM Compressor Neural Compressor AutoRound AQLM GPTQModel ONNX Core ML Tools ExecuTorch

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Opik DeepEval Ragas lm-evaluation-harness OpenCompass SWE-bench

simple-evals TruLens HELM

LightEval

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promptfoo garak Guardrails NeMo Guardrails Giskard DeepTeam AI Fairness 360 Fairlearn Detoxify MCP Scanner AgentDojo ModelScan

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Overview

Size: 75.1 GB
Weights: 75.0 GB (safetensors)
Parameters: 65.8B
Formats: safetensors
Task: image text to text
License: OpenMDW-1.1 (custom)
Files: 109, each with a sha256
Publisher: unsloth
Added: 2026-11-15
Section: alt.freeweights.nemotron

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Base model: nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16

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Model card

From the publisher at commit e8e360d. Shown as plain text with links; images open at the publisher.

Read our How to Run Nemotron 3.5 Super VL Guide!#

See Unsloth Dynamic 3.0 GGUFs for our quantization benchmarks.

https://github.com/unslothai/unsloth/ https://discord.gg/unsloth https://unsloth.ai/docs/models/nemotron-3.5-super-vl

NVIDIA-Nemotron-3.5-Super-VL-120B-A12B#

Pre-Training Datasets Homepage Discord

License

https://unsloth.ai/docs/models/nemotron-3.5-super-vl

Description#

NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16 is the full-precision release of NVIDIA Nemotron 3.5 Super VL, a vision-language model for reasoning, coding, tool use, document understanding, and video understanding. The BF16 weights support customization, domain adaptation, and full-precision evaluation. For optimized deployment, see the NVFP4 release.

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.

Model Summary#


Total Parameters	120B (12B active)
Architecture	Mamba-2–Transformer hybrid Mixture of Experts with a C-RADIO vision encoder
Precision	BF16 (full-precision reference weights)
Context Length	Up to 1M tokens; deployment settings are listed below
Inputs / Outputs	Text, images, and video / text
Deployment	4× H100 or 2× GB200
Speculative Decoding	Native Multi-Token Prediction (MTP)
Reasoning Mode	Configurable through the chat template (`enable_thinking=True/False`)
Recommended Sampling	Text: temperature 1.0, top_p 0.95; vision: temperature 0.6, top_p 0.95
Best For	Customization, domain adaptation, and full-precision evaluation
License	OpenMDW-1.1

Model Overview#

Model Developer: NVIDIA Corporation

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#

We evaluated our model on the following benchmarks:

Benchmark	Nemotron 3.5 SuperGA BF16	Qwen3.5-122B-A10B	Qwen3.8-Flash-Next	DeepSeek-V4-Flash0731	Nemotron 3 Ultra	Inkling-Small	GLM-5.3-Flash
GPQA-Diamond	89.52	86.7	92.7	89.6	87.0	88.6	91.2
HLE, text-only	31.79	24.2	39.6	35.8	29.2	33.3	36.47
MMLU-ProX	76.91	83.2	83.0	85.5	83.1	84.0	83.0
AA-Omniscience accuracy	26.20	26.2	27.9	45.0	25.3	35.4	34.2
AA-Omniscience hallucination rate	30.0	84.5	41.3	95.9	22.1	59.6	30.3
AA-Omniscience index	4.03	-36.1	-1.9	-7.8	8.8	-3.1	14.3
CritPt	10.86	1.1	—	12.6	3.1	9.1	—
APEX shortlist	88.36	54.7	91.8	88.7	75.0	71.5	77.1
LiveCodeBench v6	88.22	76.7	94.1	94.6	89.2	88.3	80.6
SciCode, subtask	47.13	39.8	46.6	49.3	42.0	48.3	50.05
AA-LCR v1.1	68.62	75.1	78.4	79.7	75.2	77.3	79.5
GraphWalks	89.71	86.1	76.1	65.4	63.3	55.7	88.4
IFBench	70.86	67.6	75.6	76.2	82.9	79.3	57.7
LMArena v3 proxy	52.67	47.0	35.2	49.8	34.3	53.6	61.0
WMT24++	84.80	86.3	86.2	85.5	82.7	84.6	86.1
tau2-bench retail	69.24	55.4	71.6	73.8	73.7	71.8	78.2
tau2-bench airline	76.50	63.5	80.8	83.5	79.8	62.7	81.8
tau2-bench telecom	73.19	87.2	90.4	98.2	83.0	90.1	98.5
tau3-bench banking	18.88	14.2	44.5	38.6	18.4	—	34.8
GDPval-AA v2	1211	861	1561	1382	1148	1313	1563
BrowseComp	78	58.1	89.0	84.2	67.85	74.1	86.6
SWE-bench Verified	71.10	71.9	78.7	75.4	73.9	75.27	79.10
SWE-bench Multilingual	67.72	62.0	77.10	72.0	68.8	71.4	78.30
SWE-bench Pro	51.78	44.9	60.37	56.91	49.66	56.54	59.70
Terminal-Bench 2.1	66.70	48.70	85.80	76.80	54.80	53.40	82.70
BabyVision	29.60	35.7	46.8	N/A	N/A	24.7	39.1
MMLongBench-Doc	62.82	63.7	67.9	N/A	N/A	49.06	65.8
V*	85.60	91.1	93.0	N/A	N/A	70.5	88.1
OCR-Reasoning	65.08	64.3	67.1	N/A	N/A	54.7	64.6
MathVision	78.16	83.3	87.5	N/A	N/A	78.8	82.5
HLE, full set	29.20	24.4	38.8	N/A	N/A	31.6	35.1
OCRBench v2, EN	68.12	68.3	67.9	N/A	N/A	54.2	61.8
OCRBench v2, ZH	59.15	67.3	68.3	N/A	N/A	52.4	56.8
CharXiv reasoning	80.82	83.5	85.8	N/A	N/A	77.8	81.9
Video-MME v2	32.85	32.0	45.1	N/A	N/A	—	14.8
OSWorld-Verified	51.14	51.5	55.8	N/A	N/A	—	14.78
BrowseComp [offline]	78	58	89	83	44 (reported)	74	86.6
WebVoyager [offline]	77.72	54.89	90.39	not applicable	not applicable	5.25	72.64
AA-Briefcase-Lite [offline]	1137 (-) \	1252 (-) \	40	551 \	591 \	22	1636 \	1324 \	76	1469 \	1277 \	80	665 (836) \	866 (874) \	47	765 \	684 \	17	1785 (1658) \	1428 (1408) \	76
AutomationBench-AA [offline]	32.44	27.25	72.06	63.86	23.28	32.83	70.17
GDP.pdf [offline]	6.6 (AA 6.6)	8.8 (AA 3.4)	17.4 (AA 15.6)	10.8 (AA 11.0)	8.4 (AA 5.0)	8.6 (AA 11.2)	14.8 (AA 15.4)
Terminal Bench 4 [offline]	1	0	20.71	3.54	0	0.5	15.15
DeepSWE [offline]	24.65	0.4	55.57	42.3	3	18	57.77 / 63.4 (reported)

We evaluated Nemotron 3.5 Super on a comprehensive suite of benchmarks spanning agentic capabilities, reasoning and knowledge, multimodal, conversational ability and instruction following, long-context understanding, and multilingual performance. All evaluation results for Nemotron 3.5 Super and baselines were collected via NeMo Gym. For reproducibility purposes, more details on the evaluation settings can be found in the NeMo Gym recipes folder and the corresponding reproducibility tutorial. The published recipes specify the benchmark-specific containers, prompts, inference parameters, parser configurations, and scoring settings used to produce the results.

There were a few benchmarks that were not yet onboarded in our open source tools, and for these we used either their official open source implementation or an internal scaffolding that we plan to open source in the future: BabyVision, CharXiv-RQ, MathVision, MMLongBench-Doc, OCR-Reasoning, OCRBench-v2, V*, Video-MME-v2.

Agentic Coding Benchmarks#

Additional harness-level coding-agent results for SWE-Bench Pro and Terminal-Bench 2.1 are shown below.

Image: image

Deployment Geography: Global#

Use Case#

This model is intended for developers who want to customize a post-trained vision-language model, adapt it to domain-specific data, develop quantized variants, or run full-precision inference and evaluation.

Release Date#

Hugging Face - 10/15/2026 via Hugging Face

Reference(s)#

NVIDIA Nemotron 3.5 Super VL and Lightning 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.

Stage 4: Supervised Fine-Tuning

A two-stage supervised fine-tuning pipeline jointly trains the language and vision components on vision, reasoning, code, tools, chat, safety, and long-context data. Training uses packed sequences of up to 524,288 tokens.

Stage 5: Reinforcement Learning and Distillation

Reinforcement learning with verifiable rewards, reinforcement learning from human feedback, and specialized teacher training improve reasoning, agentic, chat, and vision capabilities. Multi-teacher on-policy distillation consolidates these capabilities into a single model.

Stage 6: MTP Boosting

The MTP head is further trained against the frozen post-trained backbone to improve speculative-decoding acceptance.

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): vLLM; SGLang
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.

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.

The post-training corpus includes reasoning, coding, tool use, instruction following, structured outputs, chat, safety, and long-context tasks, together with image and video understanding. Synthetic trajectories are filtered for structural correctness, repetition, and task validity.

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
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
Mercor SWE-AgentsV1	Undisclosed
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
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)
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

NVIDIA-Sourced Synthetic Datasets (Pre-Training)#

Dataset	Modality	Dataset Size	Seed Dataset	Model(s) used for generation
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
Indic SDG for Nemotron	Text	711,276,565 indexed source-sequence entries; final training-blend count not confirmed	nvidia/Nemotron-CC-v2	google/gemma-4-31B-it; google/gemma-4-31B-it-assistant

NVIDIA-Sourced Synthetic Datasets (Post-Training)#

Dataset	Modality	Dataset Size	Seed Dataset	Model(s) used for generation
Synthetic Single-Step Tool Use — RL	Text	Approximately 1,433,203 samples	Handwritten prompts; no external seed data	DeepSeek-V3.2; DeepSeek-R1-0528; Qwen3-235B-A22B-Thinking-2507; Qwen3-32B
Synthetic Sparse Tables with Mixed Prompts	Text and images	100,000 records	Generated in-house; no external seed data	None; generated without an SDG model
Synthetic Single-Step Tool Use — RL	Text	Approximately 1,433,203 samples	Handwritten prompts; no external seed data	DeepSeek-V3.2; DeepSeek-R1-0528; Qwen3-235B-A22B-Thinking-2507; Qwen3-32B
Synthetic Sparse Tables with Mixed Prompts	Text and images	100,000 records	Generated in-house; no external seed data	None; generated without an SDG model
Synthetic Competitive MATH Proofs from DeepSeek-V4-Pro	Text	Undisclosed	[AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]	[deepseek-ai/DeepSeek-V4-Pro]
Synthetic Hermes Agent Reasoning Traces	Text	Undisclosed	[lambda/hermes-agent-reasoning-traces]	[hermes-agent-generator]
Synthetic Competitive Coding from DeepSeek-V4-Pro	Text	Undisclosed	[NVCompetitiveCodingV1]	[deepseek-ai/DeepSeek-V4-Pro]
Synthetic Competitive Science Reasoning from DeepSeek-V4-Pro	Text	Undisclosed	[AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [EssentialAI/essential-web-v1.0]; [cdquestions.com]; [Pile-FreeLaw]; [Vedantu]; [askfilo]; [doubtnut]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)]; [AAPT]; [ChemData 700K]; [oMeBench]; [Flavor Analysis and Recognition Transformer]; [ChemCoTBench]; [Llama Nemotron Dataset]	[deepseek-ai/DeepSeek-V4-Pro]
Synthetic Competitive MATH CoT and TIR from Nemotron 5.5	Text	Undisclosed	[Pile-FreeLaw]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]	[Nemotron 5.5]
Vendor Terminal Bench-like Tasks from Mercor	Text	Undisclosed	[Terminal bench like tasks curated by the vendor]	[Undisclosed - purchased dataset]
Turing Math Data Pack	Text	Undisclosed	[Turing Math Data Pack dataset]	[Undisclosed - purchased dataset]
Synthetic Holdout, Skywork, DAPO, and Turing Math from GPT-5.5	Text	Undisclosed	[DocQA-RL-1.6K]; [DAPO-Math-17k]	[GPT-5.5]
Synthetic Long Context RL from QwenLong L1 and DocQA-RL-1.6K	Text	Undisclosed	[DocQA-RL-1.6K]	Undisclosed
Synthetic Competitive Coding Gym Tasks	Text	Undisclosed	[NVCompetitiveCodingV1.1]	Undisclosed
Synthetic Finance SEC Search Agent from GPT-OSS-120B and Qwen3	Text	Undisclosed	[SEC filings from sec.gov]	[GPT-OSS-120B]; [Qwen3-235B-A22B-Instruct]; [Qwen3-4B-Instruct]
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	[Nemotron-RL-agent-structured-outputs-v1]	[Qwen3-30B-A3B-Instruct-2507]; [Qwen3-235B-A22B-Instruct-2507]
Synthetic Long Context Equivalence Rule from Qwen3-235B-A22B-Thinking-2507 and DeepSeek-R1	Text	Undisclosed	[Long-context SFT data]	[Qwen/Qwen3-235B-A22B-Thinking-2507]; [Deepseek-ai/DeepSeek-R1]
Synthetic Science RL Data Blend from Qwen2.5-32B	Text	Undisclosed	[doubtnut]; [Pile-FreeLaw]; [Llama Nemotron Dataset]; [askfilo]; [EssentialAI/essential-web-v1.0]; [Vedantu]; [auxiliary_train]; [cdquestions.com]; [AMC 8 Problems and Solutions, AMC 10 Problems and Solution, and AIME Problems and Solutions]; [AAPT]; [ICHO-IPH0 Dataset]; [LIMO dataset (Less is More for Reasoning)]	[Qwen2.5-32B]
Synthetic Abstention Data from Nemotron Super v3	Text	Undisclosed	[Go abstention Dataset]	[nvidia/nvidia/nemotron-3-super-v3]
Synthetic Chemistry Data from Nemotron Super v3	Text	Undisclosed	[ChemData 700K]	[nvidia/nvidia/nemotron-3-super-v3]
Synthetic Tool Call Schema for RL	Text	469,983	[UltraTool]; [ToolEyes]; [AutoTools]; [API-Bank]; [Nemotron-Personas-USA]; [Salesforce xLAM function-calling]; [Glaive function-calling-v2]; [Agent-Ark/Toucan-1.5M]	[DeepSeek-V3.2]; [GLM-4.6]; [gpt-oss-120b]; [Kimi-K2-Instruct]
Synthetic Freeform Text Formatting from GPT-OSS-120B	Text	Undisclosed	[In-house data]	[GPT OSS 120B - Apache 2.0]
Synthetic Citation Formatting from GPT-OSS-120B	Text	Undisclosed	[In-house data]	[GPT OSS 120B - Apache 2.0]
Droid Harness Pivot Vendor Data	Text	Undisclosed	[Droid Harness Pivot vendor data]	Undisclosed
Synthetic HotpotQA Training Data from Qwen3-235B	Text	Undisclosed	[HotpotQA]	[Qwen3-235B]
Synthetic Natural Language Math Proofs from Nemotron 5.5	Text	Undisclosed	[AMC8, AMC10, and AIME problem sets hosted on Art of Problem Solving]; [Pile-StackExchange]	[Nemotron 5.5]
Synthetic Stack Overflow OpenQ	Text	Undisclosed	[Pile-FreeLaw]	Undisclosed
Chemistry Ether0 Vendor Data	Text	Undisclosed	[Chemistry ether0 vendor data]	Undisclosed
Synthetic Litmus-Bench Chemistry from ChEMBL	Text	Undisclosed	[ChEMBL]; [Nemo Gym RL dataset generated from ChEMBL with RDKit]	Undisclosed
Synthetic ZINC Chemistry from Nemotron Super v3	Text	Undisclosed	[ZINC]	[Nemotron Super v3]
ARC-AGI Gym Environment	Text	Undisclosed	[ARC-AGI-2]	[ARC-AGI-2]
Synthetic Agentic Search Tool-Use from DeepSeek-V3.2	Text	Undisclosed	[Mercor Data]	[DeepSeek-V3.2]
Synthetic Text-To-SQL	Text	96,564	[In-house Text-to-SQL data]	[gpt-oss-120b]
Dialog Memory Vendor Data	Text	Undisclosed	[Patronus external vendor agreement]	Undisclosed
Synthetic Indirect Prompt Injection from Nemotron Super v3 and Qwen3-Next-80B-A3B-Instruct	Text	Undisclosed	[In-house indirect prompt injection data]	[nvidia/nemotron-3-super-v3, qwen/qwen3-next-80b-a3b-instruct]

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File	Size	sha256
.gitattributes	1.64 KB	fe81d528e7ee055bbcf6a310afe0f2808423a4a16d45225f29c285926abef08d
README.md	82.6 KB	70ce3b51c596e77a946c5f1278269a2b5ab65869b8c0c80f2112c8813e78afbe
__init__.py	0 B	e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855
chat_template.jinja	12.9 KB	1369e90b19976f99ce2070eb21ade8fcb4af640f008e14e82af5c5e89a9cc816
chat_template_backup.jinja	12.5 KB	6521afc75afb0661e3558e7e6dee5f2d15de8be1c4dece8171935e778ea283f4
checksums.blake3	10.3 KB	1579e56d4f067f7f183a72af2120b68373ff8faadc5b71ea74b0992456b8932b
config.json	8.67 MB	35189a8038d1b1f934e58cba50eb7e97b42a4da569424e35b9fd1048058bc7ea
configuration_nemotron_h_omni.py	7.14 KB	83f0c11fd9fb4da4a3de434759e1a18958b69f9fce672bafed4346a616ec20d4
configuration_radio.py	4.22 KB	b0f1ea1d8136272635bc8d6eeba0564542a4e48474ae98b2d8ed74b0fc5d20ce
generation_config.json	209 B	c67c90fcadf0e7ef78b663eb159664b36a35ffb9dd2523096e226e5c3d1b0e5f
hf_quant_config.json	5.63 MB	b797654d89e25e9bd13617cb1d216ff5fad78cb6a8b99ba0cd0fbcf20050d75a
image_processing_nemotron_h_omni.py	10.2 KB	0344e8432097e0718672d6529c68fd6095c36bea176500f131d203d1c7d497e0
model-00001-of-00088.safetensors	2.87 GB	6f41915eae39faf9eea1b8edbbc8a4ffcab6c7b8a3288e872c65457c86fa1438
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model-00073-of-00088.safetensors	1.63 GB	49bc3077777db67d047dd7be6aefa9151ef216a5b22183d463c0dbb0629b8325
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model-00075-of-00088.safetensors	1.63 GB	b8ccaaa2566487d7b4a9b62f47a8a7d182394831871a7d39d4d8c38b4dd529f6
model-00076-of-00088.safetensors	110 MB	d81a94179ba3882c619d30bccdb7ab0f1e7bbc9005989d4c2afdfa3956bdd9ee
model-00077-of-00088.safetensors	1.63 GB	5520b6e5538b842e1bd732e4f7f18da43f2fc6ea7a52db4e9dc635f25765c2f9
model-00078-of-00088.safetensors	110 MB	be0ad7debfcc928632c0ea2a1960f81fc6d5b10bd667e5aef64eed0a64f6a42b
model-00079-of-00088.safetensors	35.7 MB	aa4b44ce3a0fb19d01cf3a42b1b3ca7440c3c753d776516b5dd7118351b7bc47
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model-00083-of-00088.safetensors	110 MB	5a13e46428f89d489080cf7128160c7334c82bf77818d05acdbb9d154497bdd8
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model-00085-of-00088.safetensors	110 MB	dba638bd7910d5c84574c070c74b487a45a0b4d13379828532b9571db8613fff
model-00086-of-00088.safetensors	1.63 GB	992ccd2c56c9a1ee79f5c4907bee0aa12517204bbd2e65a3d86469fa1dee7b47
model-00087-of-00088.safetensors	110 MB	26361d65e034375d00b1ac09e4cc436132dbc2895f315c0a679218b0b18eea52
model-00088-of-00088.safetensors	3.94 GB	6e576283e02f46a5a547e08a892515cd4e77a0fa4e6955325cfbbfab24e37479
model.safetensors.index.json	19.5 MB	c368acdbcfe882236af1ec79df4a1fb6127e6ed9f742d59c01c21fe96ddc2741
modeling_nemotron_h_omni.py	22.6 KB	dfe88cc516f27b1dc8508ec7e448d81143aaf13d01400f5f93525b7f1271d929
modeling_radio.py	22.8 KB	ceec4c1ecd2a2649a79e74b16a2ea4af60e6cd11fbecebbd681c33fb1fccb943
preprocessor_config.json	693 B	0b80e5c895052ff2d8e528eca719a6b6e39b7b1b60afb0b94553ffae948e22bc
processing_nemotron_h_omni.py	16.4 KB	406bc7689161d19e2a01e34f0ac8a7cc1bbc927f664d03bae3d23d00ea27f11a
processor_config.json	1.06 KB	ce813d598192df1b09813631b5f9c78f31c1e2e8d291c7c660c8dfa92a9597a7
tokenizer.json	17.1 MB	d0432ad8b1c2984a2e2dca82e1933e3fd5628df6e95c51fba4c3ee9950dce299
tokenizer_config.json	619 B	89fa70d31734c0c75ffdcee4114449f8cf8abd64bc233d420b58e25763f91aba
ultra_v3_reasoning_parser.py	2.49 KB	14e03de50a523c1b6b4542c22d38ec11cadc515a95752e5a4c8246967f620857

VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.

Upstream commit	Date	Contents
e8e360d920 pinned now	2026-11-15 00:26 UTC	109 files · 75.1 GB

Use it

# custom terms: read the license before you download
$ fw get unsloth/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4
# manifest sha256 97aaeb4eac6b… · 75.1 GB · 109 files
$ fw verify ./NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4 unsloth/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4

Terms that come with these files. OpenMDW 1.1: keep the agreement and the copyright notices with the files when you pass them on.

Custom license (custom). Read the license text first. Open this panel to show the commands once you accept its terms.

fw.pyChecks the signature and every sha256, tries each source in order, resumes.

$ python3 fw.py get https://freeweights.allternit.com/manifests/unsloth/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4.json --dir ./NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4
$ python3 fw.py verify ./NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4 ./unsloth__NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4.json

hfThe Hugging Face CLI with one setting. Same repo id.

$ HF_ENDPOINT=https://freeweights.allternit.com hf download unsloth/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4 --revision e8e360d920b9efb4ca5ecedc938fb2d1e61822f5 --local-dir ./NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4

transformersLoad the weights in Python.

import os
os.environ["HF_ENDPOINT"] = "https://freeweights.allternit.com"
from transformers import AutoModelForImageTextToText, AutoProcessor
processor = AutoProcessor.from_pretrained("unsloth/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4", revision="e8e360d920b9efb4ca5ecedc938fb2d1e61822f5")
model = AutoModelForImageTextToText.from_pretrained("unsloth/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4", revision="e8e360d920b9efb4ca5ecedc938fb2d1e61822f5", device_map="auto")

vLLMServe an OpenAI-compatible API.

$ HF_ENDPOINT=https://freeweights.allternit.com vllm serve unsloth/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4 --revision e8e360d920b9efb4ca5ecedc938fb2d1e61822f5

Run at home

License

Name: OpenMDW-1.1
Id: OpenMDW-1.1
Class: Custom terms
Redistribution: click-through
Listing: Hosted (signed)
Terms: OpenMDW 1.1: keep the agreement and the copyright notices with the files when you pass them on.
Decided: License as declared by the publisher. No person read it. 2026-11-15
Text in: README.md
Text sha256: 70ce3b51c596e77a946c5f1278269a2b5ab65869b8c0c80f2112c8813e78afbe
Terms gate: Yes. The commands stay folded until you open them after reading the license.

Read the license text

Licenses are checked against the license table. A custom license passes only after a person reads it.

Provenance

From:

unsloth <huggingface.co/unsloth>Newsgroups: alt.freeweights.nemotronSubject: NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4Date: 2026-11-15Commit: e8e360dSigned: 2DC28CC7B26A283B ✓

Upstream: unsloth/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4
Pinned commit: e8e360d920b9efb4ca5ecedc938fb2d1e61822f5
Manifest: NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4.json · .minisig
Manifest sha256: 97aaeb4eac6b229323de3334b9efe3281835ae22fb5492fa4aad073d697bed97
Signature: minisign, Ed25519, key 2DC28CC7B26A283B
Created: 2026-11-15 00:26 UTC
Generator: freeweights-manifest/0.1.0
Takedowns: None.

How to verify

$ curl -O https://freeweights.allternit.com/manifests/unsloth/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4.json
$ curl -O https://freeweights.allternit.com/manifests/unsloth/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4.json.minisig
$ minisign -Vm NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4.json -P RWQ7KGqyx4zCLXtUFOqvfv8L3g+8fpKJ1FsuxPyOYMF+4KufbUxKq1Oq
$ shasum -a 256 <file>   # compare with the Files tab

A manifest whose signature fails must be rejected. A file whose size or sha256 does not match must be discarded. Verify a download

Model tree

Parent

nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16 (not in this catalog) · this model is a quantization of it

No model in the catalog names this one as its parent.

Get it

Read license, then download

$ HF_ENDPOINT=https://freeweights.allternit.com hf download unsloth/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4

Open in Freeweights Studio

Pull, verify and run it in the Allternit app on Desktop, web or phone.

Signed manifest

minisign, key 2DC28CC7B26A283B. NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4.json. Provenance. Takedown history: none.

Source

huggingface.co/unsloth/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4

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NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4

Works with

Runs with

Fine-tune with

Convert with

Evaluate with

Scan with

Works with

Overview

Model card

Read our How to Run Nemotron 3.5 Super VL Guide!#

NVIDIA-Nemotron-3.5-Super-VL-120B-A12B#

Description#

Model Summary#

Model Overview#

What is Nemotron?#

License/Terms of Use#

Benchmarks#

Agentic Coding Benchmarks#

Deployment Geography: Global#

Use Case#

Release Date#

Reference(s)#

Model Architecture#

Model Design#

Training Methodology#

Input#

Output#

Software Integration#

Training, Testing, and Evaluation Datasets#

Training#

Base Pre-Training Corpus (Nemotron 3 Foundation)#

Public Datasets#

Crawled and Scraped from Online Sources by NVIDIA#

Private Non-publicly Accessible Datasets of Third Parties#

Private Non-publicly Accessible Datasets by NVIDIA#

NVIDIA-Sourced Synthetic Datasets (Pre-Training)#

NVIDIA-Sourced Synthetic Datasets (Post-Training)#

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