Nemotron-Labs-3-Puzzle-75B-A9B is a deployment-optimized large language model developed by NVIDIA, derived from Nemotron-3-Super-120B-A12B. The model is produced using Iterative Puzzle, a post-training compression framework, with the goal of significantly improving inference efficiency for interactive, reasoning-heavy, and long-context workloads while preserving strong downstream accuracy.

The model employs a hybrid MoE architecture with interleaved Mamba, MoE, and Attention layers. Like Nemotron-3-Super, it supports Multi-Token Prediction (MTP) for faster text generation. Compared to its parent, Puzzle-75B-A9B reduces the model from 120.7B total / 12.8B active parameters to 75.3B total / 9.3B active parameters.

See the tech report for full training and compression details: Nemotron-Labs-3-Puzzle-75B-A9B: Compressing Hybrid MoE LLMs.

Compared to Nemotron-3-Super, Puzzle-75B-A9B:

Achieves approximately 2× higher server throughput on a single 8×B200 node at matched user-throughput constraints,
Increases sustainable 1M-token single-H100 concurrency from 1 request to 8 requests,
Maintains strong accuracy across reasoning, coding, multilingual, long-context, and agentic benchmarks.

The supported languages include: English, French, German, Italian, Japanese, Spanish, and Chinese.

This model is ready for commercial use.

License/Terms of Use

Governing Download Terms: Use of this model is governed by the OpenMDW License Agreement, version 1.1 (OpenMDW-1.1).

This project is currently not accepting contributions.

Benchmarks

Benchmark	Nemotron-Labs-3-Puzzle-75B-A9B-BF16	Nemotron-Labs-3-Puzzle-75B-A9B-FP8	Nemotron-Labs-3-Puzzle-75B-A9B-NVFP4
General Knowledge
MMLU-Pro	82.4	82.0	82.2
Reasoning
AIME25 (no tools)	89.7	89.4	89.9
HMMT Feb25 (no tools)	93.4	92.7	92.9
HMMT Feb25 (with tools)	93.9	93.6	93.1
GPQA (no tools)	78.6	77.8	78.0
GPQA (with tools)	79.5	80.6	78.2
LiveCodeBench (v5 2024-07↔2024-12)	81.1	80.5	79.9
SciCode (subtask)	40.6	39.6	40.3
HLE (no tools)	16.5	16.0	15.7
Agentic
Terminal Bench (hard subset)	24.0	22.9	23.4
TauBench V2
Airline	55.8	54.5	55.7
Retail	63.2	63.4	63.7
Telecom	61.5	61.3	60.3
Average	60.2	59.7	59.9
Chat & Instruction Following
IFBench (prompt)	71.9	71.9	71.3
Scale AI Multi-Challenge	56.6	55.4	55.9
Arena-Hard-V2	68.6	69.8	69.0
Long Context
AA-LCR	56.9	56.6	57.1
RULER @ 256k	95.1	95.3	95.3
RULER @ 512k	94.2	94.5	94.8
RULER @ 1M	92.2	92.4	93.2
Multilingual
MMLU-ProX (avg over langs)	77.5	77.1	76.5
WMT24++ (en→xx)	85.2	85.2	85.1

All evaluation results were collected via Nemo Evaluator SDK and for most benchmarks, the Nemo Skills Harness. For reproducibility purposes, more details on the evaluation settings can be found in the Nemo Evaluator SDK configs folder and the reproducibility tutorial for Nemotron 3 Super. The open source container on Nemo Skills packaged via NVIDIA's Nemo Evaluator SDK used for evaluations can be found here. In addition to Nemo Skills, the evaluations also used dedicated open-source packaged containers for Tau-2 Bench (default prompt), Terminal Bench Hard (48 tasks), ScaleAI Multi Challenge Multi-turn Instruction Following, and Ruler.

The following benchmarks are not onboarded yet in our open source tools and for these we used either their official open source implementation or otherwise an internal scaffolding that we plan to open source in the future: SWE Bench Verified (OpenHands).

Deployment Geography:

Global

Use Case:

NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-FP8 is a general purpose reasoning and chat model intended to be used in English, Code, and supported multilingual contexts. This model is optimized for collaborative agents and high-volume workloads. It is intended to be used by developers designing AI Agent systems, chatbots, RAG systems, and other AI-powered applications. This model is also suitable for complex instruction-following tasks and long-context reasoning.

Release Date:

July 6, 2026 via Hugging Face

References(s):

Model Architecture:

Architecture Type: Mamba2-Transformer Hybrid Latent Mixture of Experts (LatentMoE) with Multi-Token Prediction (MTP)
Network Architecture: Modified Nemotron-3-Super-120B-A12B-FP8 architecture with smaller Mamba SSM state size, varying number of active experts per layer and varying expert intermediate channel size across layers.
Number of model parameters: 75B Total / 9.3B Active

Model Design

Puzzle-75B-A9B is a compressed variant of Nemotron-3-Super optimized for interactive deployment. We designed the model to maximize server throughput under high user throughput constraints.

The model was constructed using a multi-stage pipeline that combines the Iterative Puzzle compression framework with knowledge distillation, reinforcement learning, quantization, and Multi-Token Prediction head. The compression process jointly optimizes heterogeneous MoE pruning, active parameter budget, and Mamba pruning to improve inference efficiency while preserving model quality. Attention layers are left unchanged because the parent model is already KV-cache efficient.

Compression is applied to three architectural dimensions:

Heterogeneous MoE Channel Pruning:
Routed expert intermediate dimensions are pruned non-uniformly across MoE layers. The parent routed expert intermediate size of 2688 is reduced to a layer-dependent range of 1280-2688, preserving more capacity in sensitive layers while pruning more aggressively elsewhere.
Heterogeneous Active Expert Reduction:
The number of activated routed experts per token is reduced from 22 in the parent model to a layer-dependent range of 4-18. This reduces active parameters and improves efficiency in compute-bound inference regimes such as prefill and large-batch decoding.
Mamba SSM State Pruning:
The Mamba SSM state size is reduced from 128 to 96 channels. This reduces Mamba cache I/O and improves decode-stage efficiency, especially at larger batch sizes.

Training and Optimization Procedure

Puzzle-75B-A9B is produced through a post-training compression and recovery pipeline starting from Nemotron-3-Super. The pipeline combines Iterative Puzzle compression, knowledge distillation, reinforcement learning recovery, post-training quantization, and continued MTP training.

Stage 1: Iterative Puzzle Compression

The model is constructed through three compression-and-recovery stages. Each stage prunes the model to a certain intermediate target budget and then performs a short knowledge distillation recovery phase before the next compression step.

In the first stage, MoE weights are reduced to 75% of the teacher capacity, and the Mamba SSM state size is reduced to 75% of the teacher size. The resulting model is recovered with 24B tokens of knowledge distillation. In the second stage, MoE weights are further reduced to 60% of the teacher capacity, followed by 43.2B tokens of knowledge distillation recovery. In the final stage, the activated routed-expert budget (MoE top-k) is constrained to 50% of the teacher budget, with Puzzle allocating this budget heterogeneously across layers. The resulting model is recovered with 52.8B tokens of knowledge distillation.

Stage 2: Long-Context Knowledge Distillation Recovery

After architecture selection, the compressed model undergoes additional knowledge distillation from Nemotron-3-Super to recover quality lost during compression and recover long-context capability.

Training uses a mixture of 30% pretraining data and 70% supervised fine-tuning data. During the Iterative Puzzle stages, knowledge distillation is performed at 32Ki sequence length. The final recovery phase extends distillation to longer contexts, first at 128Ki and then at 512Ki sequence length, using up to 100B training tokens per phase and a global batch size of 16Mi tokens.

Software used for knowledge distillation: Megatron-Bridge and Megatron-LM.

Stage 3: Reinforcement Learning (RL) Recovery

Following knowledge distillation, the model undergoes reinforcement learning recovery focused primarily on software-engineering and agentic capabilities, which are especially sensitive to compression.

The RL stage follows the Nemotron-3-Super software-engineering RL pipeline (SWE-RL). It includes single-step tool-use comparison training and end-to-end sandbox RL, where agents interact with isolated execution environments over multiple turns. Multiple RL runs are trained with different learning rates, and the final checkpoint is obtained through weight averaging across selected runs.

Software used for reinforcement learning: NeMo-RL

Stage 4: Deployment Optimization

The resulting checkpoint is further prepared for deployment using post-training quantization. FP8 checkpoints target Hopper-class GPUs, while NVFP4 checkpoints target Blackwell-class GPUs. The model also uses continued MTP training to improve speculative decoding acceptance length and increase serving throughput.

Input

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

Output

Output Type(s): Text
Output Format: String
Output Parameters: One-Dimensional (1D): 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): Hugging Face Transformers, vLLM
Supported Hardware Microarchitecture Compatibility: NVIDIA Blackwell, NVIDIA Hopper
Supported 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

Quick Start Guide

Serving

vLLM

To deploy the Nemotron Labs 3 Puzzle BF16, and FP8 checkpoint on NVIDIA Hopper GPUs, use the following command:

With MTP:

vllm serve "$path" \
  --served-model-name "$model" \
  --port "$port" \
  --tensor-parallel-size "$tp" \
  --enable-expert-parallel \
  --async-scheduling \
  --trust-remote-code \
  --mamba-backend flashinfer \
  --speculative-config "{\"method\":\"mtp\",\"num_speculative_tokens\":${num_speculative_tokens}}" \
  --tool-call-parser qwen3_coder \
  --reasoning-parser nemotron_v3 \
  --enable-auto-tool-choice \
  --gpu-memory-utilization 0.85

Without MTP:

vllm serve "$path" \
  --served-model-name "$model" \
  --port "$port" \
  --tensor-parallel-size "$tp" \
  --enable-expert-parallel \
  --async-scheduling \
  --trust-remote-code \
  --mamba-backend flashinfer \
  --tool-call-parser qwen3_coder \
  --reasoning-parser nemotron_v3 \
  --enable-auto-tool-choice \
  --gpu-memory-utilization 0.85

Notes:

Tested on vLLM v0.20.0.
NVIDIA recommends setting tp to 2 or 4.
For MTP, num_speculative_tokens=3 is the recommended default (best throughput at typical BS); 5 or 7 may be beneficial for low-batch / latency-sensitive deployments.
For NVIDIA H200 SXM deployments, NVIDIA recommends using --gpu-memory-utilization 0.9.
For very long generation scenarios, it is reccomeneded to use --api-server-count 4.

API Client

The examples below use the OpenAI-compatible client.

NOTE: For coding agents add the following to the API call - extra_body={“chat_template_kwargs”: {“force_nonempty_content”: True}

python

from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
MODEL = "nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-FP8"

Reasoning ON (default)

python

response = client.chat.completions.create(
    model=MODEL,
    messages=[{"role": "user", "content": "Write a haiku about GPUs"}],
    max_tokens=16000,
    temperature=1.0,
    top_p=0.95,
    extra_body={"chat_template_kwargs": {"enable_thinking": True}}
)
print(response.choices[0].message.content)

Reasoning OFF

python

response = client.chat.completions.create(
    model=MODEL,
    messages=[{"role": "user", "content": "What is the capital of Japan?"}],
    max_tokens=16000,
    temperature=1.0,
    top_p=0.95,
    extra_body={"chat_template_kwargs": {"enable_thinking": False}}
)
print(response.choices[0].message.content)

Low-effort reasoning

Uses significantly fewer reasoning tokens than full thinking mode. Recommended as a starting point before tuning explicit token budgets.

python

response = client.chat.completions.create(
    model=MODEL,
    messages=[{"role": "user", "content": "What is the capital of Japan?"}],
    max_tokens=16000,
    temperature=1.0,
    top_p=0.95,
    extra_body={"chat_template_kwargs": {"enable_thinking": True, "low_effort": True}}
)
print(response.choices[0].message.content)

Transformers

We recommend using Transformers ≥ 5.3.0.

python

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-FP8")
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-FP8",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

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.

Here is an example of generating outputs with reasoning enabled (the default):

python

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

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

if not isinstance(tokenized_chat, torch.Tensor):
    input_ids = tokenized_chat["input_ids"]
else:
    input_ids = tokenized_chat

outputs = model.generate(
    input_ids,
    max_new_tokens=50,
    temperature=1.0,
    top_p=0.95,
    eos_token_id=tokenizer.eos_token_id
)

print(tokenizer.decode(outputs[0]))

To disable reasoning, add enable_thinking=False to apply_chat_template(). By default, enable_thinking is set to True.

python

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

Training and Evaluation Datasets

Training

Data Modality: Text
The total size: 15,573,172,908,990 Tokens
Total number of datasets: 153
Dataset partition: Training [100%], testing [0%], validation [0%]
Time period for training data collection: 2013 to February 24, 2026
Time period for testing data collection: 2013 to February 24, 2026
Time period for validation data collection: 2013 to February 24, 2026
Data Collection Method by dataset: Hybrid: Automated, Human, Synthetic
Labeling Method by dataset: Hybrid: Automated, Human, Synthetic

NVIDIA-Nemotron-3-Super-120B-A12B is pre-trained on a large corpus of high-quality curated and synthetically-generated data. It is trained in the English language, as well as 19 other languages and 43 programming languages. Our sources cover a variety of document types such as: webpages, dialogue, articles, and other written materials. The corpus spans domains including legal, math, science, finance, and more. We also include a small portion of question-answering, and alignment style data to improve model accuracy. The model was trained for approximately 25 trillion tokens.

The post-training corpus for NVIDIA-Nemotron-3-Super-120B-A12B of high-quality curated and synthetically-generated data. Primary languages used for post-training include English, French, German, Italian, Japanese, Spanish, and Chinese.

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

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

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

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

More details on the datasets and synthetic data generation methods can be found in the technical report NVIDIA Nemotron 3 Super.

Additional Training Data for Puzzle-75B-A9B

NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-FP8 is initialized from NVIDIA-Nemotron-3-Super-120B-A12B and therefore inherits the parent model's pre-training and post-training data exposure described above.

For compression recovery, the model is trained with knowledge distillation on a mixed dataset consisting of 30% pretraining data and 70% supervised fine-tuning data from the Nemotron-3-Nano training pipeline. Distillation uses NVIDIA-Nemotron-3-Super-120B-A12B-BF16 as the teacher model and is performed during both the Iterative Puzzle compression stages and the subsequent long-context recovery stages.

The long-context recovery data is used at 128Ki and 512Ki sequence lengths to recover long-context capabilities after compression.

After knowledge distillation, the model undergoes reinforcement learning recovery using software-engineering and agentic task data from the Nemotron-3-Super RL pipeline, including single-step tool-use comparison data and end-to-end sandbox RL environments.

Click to explore the full dataset catalogue used for training

Base Pre-Training Corpus (Nemotron 3 Foundation)

The foundation of the model is trained on the Nemotron-3-Nano corpus, comprising the following collections:

Dataset Collection	Token Counts	Description
Nemotron-CC-v2 & v2.1	9.13T	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	1.09T	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	336.4B	Synthetic datasets targeting specialized domains such as STEM reasoning and scientific coding.

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
Reddit Dataset	Legacy Download
SEC's Electronic Data Gathering, Analysis, and Retrieval (EDGAR)	Legacy Download
Advanced Mathematical Problem Solving	Legacy Download
MathPile	Legacy Download
NuminaMath CoT	Legacy Download
PMC Article	Legacy Download
FLAN	Legacy Download
Advanced Reasoning Benchmark	Legacy Download
SciBench	Legacy Download
WikiTableQuestions	Legacy Download
FinQA	Legacy Download
Riddles	Legacy Download
Problems in Elementary Mathematics for Home Study	Legacy Download
MedMCQA	Legacy Download
Cosmos QA	Legacy Download
MCTest	Legacy Download
AI2's Reasoning Challenge	Legacy Download
OpenBookQA	Legacy Download
MMLU Auxiliary Train	Legacy Download
social-chemestry-101	Legacy Download
Moral Stories	Legacy Download
The Common Pile v0.1	Legacy Download
FineMath	Legacy Download
MegaMath	Legacy Download
MultiverseMathHard	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
Competitive Coding RL data from Nemotron-Cascade-RL-SWE	01/10/2026
NL2Bash	01/10/2026
SWE-Gym	01/10/2026
R2E-Gym-Subset	01/10/2026
SWE-bench_Verified	01/10/2026
WorkBench	10/2/2025
OpenCodeReasoning-2	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
Nemotron-Post-Training-Dataset-v2	8/20/2025

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

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 *

Models used for prompt generation only

Private Non-publicly Accessible Datasets by NVIDIA

Dataset	Model(s) used
Simple Minesweeper	-
Simple Sudoku	-
Multitool Typewriter Hard	-
Machine Translation of News Commentary and TAUS Translation Memory	-
Machine Translation of STEM -	Qwen2.5-14B-Instruct
Competitive Coding RL data from Nemotron Cascade	-
Long context RL	-
Single-step SWE RL for patch generation	-
OpenHands SWE	-

NVIDIA-Sourced Synthetic Datasets

Dataset	Modality	Dataset Size	Seed Dataset	Model(s) used for generation
Nemotron-Pretraining-Formal-Logic	Text	128,022,285	Nemotron Personas	Qwen3-235B-A22B-Thinking-2507
Nemotron-Pretraining-Economics	Text	73,374,154	-	Qwen3-235B-A22B-Thinking-2507
Nemotron-Pretraining-Multiple-Choice	Text	1,609,214,470	MMLU Auxiliary Train	DeepSeek-V3; Qwen3-235B-A22B
Nemotron-Pretraining-Code-Concepts	Text	7,294,510,156	-	gpt-oss-20b; gpt-oss-120b
Nemotron-Pretraining-Unconditional-Algorithmic	Text	196,492,899	-	gpt-oss-120b; 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 Mixtral-8x22B-v0.1	Text	327M	social-chemestry-101; Moral Stories	Mixtral-8x22B-v0.1
Synthetic Social Sciences seeded with OpenStax from DeepSeek-V3, Mixtral-8x22B-v0.1, and Qwen2.5-72B	Text	83.6M	OpenStax - CC BY-SA subset	DeepSeek-V3; Mixtral-8x22B-v0.1; Qwen2.5-72B
Synthetic Health Sciences seeded with OpenStax from DeepSeek-V3, Mixtral-8x22B-v0.1, and Qwen2.5-72B	Text	9.7M	OpenStax - CC BY-SA subset	DeepSeek-V3; Mixtral-8x22B-v0.1; Qwen2.5-72B
Synthetic STEM seeded with OpenStax, Open Textbook Library, and GSM8K from DeepSeek-R1, DeepSeek-V3, DeepSeek-V3-0324, and Qwen2.5-72B	Text	175M	OpenStax - CC BY-SA subset; GSM8K; Open Textbook Library - CC BY-SA & GNU subset	DeepSeek-R1, DeepSeek-V3; DeepSeek-V3-0324; Qwen2.5-72B
Nemotron-PrismMath	Text	4.6B	Big-Math-RL-Verified; OpenR1-Math-220k	Qwen2.5-0.5B-instruct, Qwen2.5-72B-Instruct; DeepSeek-R1-Distill-Qwen-32B
Synthetic Question Answering Data from Papers and Permissible Books from Qwen2.5-72B-Instruct	Text	350M	arXiv; National Institutes of Health ExPorter; BioRxiv; PMC Article; USPTO Backgrounds; peS2o; Global Regulation; CORE; PG-19; DOAB CC BY & CC BY-SA subset; NDLTD	Qwen2.5-72B-Instruct
Refreshed Nemotron-MIND from phi-4	Text	73B	Common Crawl	phi-4
Nemotron-CC-Math-4plus	Text	52.3B	Common Crawl	phi-4
Nemotron-CC-Math-3	Text	80.9B	Common Crawl	phi-4
Synthetic AGIEval seeded with AQUA-RAT, LogiQA, and AR-LSAT from DeepSeek-V3 and DeepSeek-V3-0324	Text	4.0B	AQUA-RAT; LogiQA; AR-LSAT	DeepSeek-V3; DeepSeek-V3-0324
Synthetic AGIEval seeded with AQUA-RAT, LogiQA, and AR-LSAT from Qwen3-30B-A3B	Text	4.2B	AQUA-RAT; LogiQA; AR-LSAT	Qwen3-30B-A3B
Synthetic Art of Problem Solving from Qwen2.5-32B-Instruct, Qwen2.5-Math-72B, Qwen2.5-Math-7B, and Qwen2.5-72B-Instruct	Text		Art of Problem Solving; American Mathematics Competitions 8; American Mathematics Competitions 10; GSM8K; PRM800K	Qwen2.5-32B-Instruct; Qwen2.5-Math-72B; Qwen2.5-Math-7B; Qwen2.5-72B-Instruct
Synthetic MMLU Auxiliary Train from DeepSeek-R1	Text	0.5B	MMLU Auxiliary Train	DeepSeek-R1
Synthetic Long Context Continued Post-Training Data from Papers and Permissible Books from Qwen2.5-72B-Instruct	Text		arXiv; National Institutes of Health ExPorter; BioRxiv; PMC Article; USPTO Backgrounds; peS2o; Global Regulation; CORE; PG-19; DOAB CC BY & CC BY-SA subset; NDLTD	Qwen2.5-72B-Instruct
Synthetic Common Crawl from Qwen3-30B-A3B and Mistral-Nemo-12B-Instruct	Text	415.8B	Common Crawl	Qwen3-30B-A3B; Mistral-NeMo-12B-Instruct
Synthetic Multilingual Data from Common Crawl from Qwen3-30B-A3B	Text		Common Crawl	Qwen3-30B-A3B
Synthetic Multilingual Data from Wikimedia from Qwen3-30B-A3B	Text		Wikimedia	Qwen3-30B-A3B
Synthetic Math Data from Wikimedia from Nemotron-4-340B-Instruct	Text		-	Nemotron-4-340B-Instruct
Synthetic Common Crawl Code from phi-4	Text	427.9B	Common Crawl	phi-4
Synthetic Scientific Coding from Qwen3-235B-A22B	Text	1.2B	Wikimedia	Qwen3-235B-A22B
Tool Calling Data	Text	26.2B		Qwen3-235B-A22B-2507; gpt-oss-120b
Synthetic Essential-Web from QwQ-32B	Text	28.1B	Essential-Web	QwQ-32B
Translated Synthetic Crawl	Text	389.9B	Common Crawl	Qwen3-30B-A3B
Translated Synthetic Wikipedia	Text	7.9B	Wikimedia	Qwen3-30B-A3B
Synthetic Art of Problem Solving from gpt-oss-120b and Qwen2.5-32B-Instruct	Text	Undisclosed	Art of Problem Solving; American Mathematics Competitions 8; American Mathematics Competitions 10	gpt-oss-120b; Qwen2.5-32B-Instruct
Synthetic Stack Exchange from gpt-oss-120b and Qwen2.5-32B-Instruct	Text	Undisclosed	Stack Exchange	gpt-oss-120b; Qwen2.5-32B-Instruct
Synthetic OpenCodeReasoning from DeepSeek-R1-0528	Text	Undisclosed	OpenCodeReasoning	DeepSeek-R1-0528
Synthetic HackerRank Coding from DeepSeek-R1-0528	Text	Undisclosed	HackerRank Coding Dataset	DeepSeek-R1-0528
Synthetic SWE-Gym from Qwen3-Coder-480B-A35B-Instruct	Text	Undisclosed	SWE-Gym	Qwen3-Coder-480B-A35B-Instruct
Synthetic Art of Problem Solving and Stack Exchange from gpt-oss-120b, Qwen2.5-32B-Instruct, and Goedel-Prover-V2-32B	Text	Undisclosed	Art of Problem Solving; American Mathematics Competitions 8; American Mathematics Competitions 10; Stack Exchange	gpt-oss-120b; Qwen2.5-32B-Instruct; Goedel-Prover-V2-32B
Synthetic Multilingual Science and Code data from DeepSeek-R1, DeepSeek-R1-0528, Qwen2.5-32B-Instruct, and Qwen3-235B-A22B, translated with Qwen2.5-32B-Instruct and Qwen2.5-14B-Instruct	Text	Undisclosed	Stack Exchange; SCP-116K; LIMO; TACO; Code Contest; Codeforces	DeepSeek-R1; DeepSeek-R1-0528; Qwen2.5-32B-Instruct; Qwen3-235B-A22B;
Synthetic Safety from DeepSeek-R1-0528, gpt-oss-120b and Mixtral-8x7B-v0.1	Text	Undisclosed	Nemotron Content Safety Dataset V2; Gretel Synthetic Safety Alignment Dataset; RedTeam-2K; Malicious Tasks; Nemotron-Personas-USA	DeepSeek-R1-0528; gpt-oss-120b; Mixtral-8x7B-v0.1
Synthetic STEM from Qwen3-235B-A22B-Instruct-2507 and gpt-oss-120b	Text	Undisclosed	arXiv; National Institutes of Health ExPorter; BioRxiv; PMC Article; USPTO Backgrounds; peS2o; Global Regulation; CORE; PG-19; DOAB CC BY & CC BY-SA subset; NDLTD	Qwen3-235B-A22B-Instruct-2507; gpt-oss-120b
Synthetic KernelBook from DeepSeek-R1-0528	Text	Undisclosed	KernelBook	DeepSeek-R1-0528
Synthetic Tool Calling from Qwen3-235B-A22B-Thinking-2507 and Qwen3-Next-80B-A3B-Thinking	Text	Undisclosed	ToolBench; glaive-function-calling-v2; APIGen Function-Calling; Nemotron-Personas-USA	Qwen3-235B-A22B-Thinking-2507; Qwen3-Next-80B-A3B-Thinking
Synthetic Chat from gpt-oss-120b, Mixtral-8x22B-Instruct-v0.1, Qwen3-235B-A22B-Instruct-2507 , and Qwen3-235B-A22B-Thinking-2507	Text	Undisclosed	C4; LMSYS-Chat-1M; ShareGPT; GSM8K; PRM800K; FinQA; WikiTableQuestions; Riddles; glaive-function-calling-v2; SciBench; tigerbot-kaggle-leetcodesolutions-en-2k; OpenBookQA; Advanced Reasoning Benchmark; Software Heritage; Khan Academy Math Keywords; WildChat-1M; Nemotron-Personas-USA	gpt-oss-120b; Mixtral-8x22B-Instruct-v0.1; Qwen3-235B-A22B-Instruct-2507; Qwen3-235B-A22B-Thinking-2507
Synthetic Long Context from Qwen3-235B-A22B-Instruct-2507	Text	Undisclosed	CORE; PG-19; DOAB CC BY & CC BY-SA subset; NDLTD	Qwen3-235B-A22B-Instruct-2507
Synthetic Tool Use Interactive Agent from gpt-oss-120b, DeepSeek-R1-0528, Qwen3-32B, and Qwen3-235B-A22B-Thinking-2507	Text	Undisclosed	NVIDIA Internal	gpt-oss-120b; DeepSeek-R1-0528; Qwen3-32B; and Qwen3-235B-A22B-Thinking-2507
Synthetic STEM from Qwen3-235B-A22B-Thinking-2507	Text	Undisclosed	ICHO-IPH0; Physics Big; Scale HLE; OpenMathReasoning; OpenCodeReasoning	Qwen3-235B-A22B-Thinking-2507
Synthetic DocFinQA and SWE-smith from Qwen3-Coder-480B-A35B-Instruct and Kimi-K2-Thinking	Text	Undisclosed	DocFinQA; SWE-smith	Qwen3-Coder-480B-A35B-Instruct; Kimi-K2-Thinking
Synthetic Math from gpt-oss-120b and Qwen2.5-32B-Instruct	Text	Undisclosed	-	gpt-oss-120b; Qwen2.5-32B-Instruct
Synthetic Essential-Web from gpt-oss-120b	Text	Undisclosed	Essential-Web	gpt-oss-120b
Synthetic Scale HLE from gpt-oss-120b	Text	Undisclosed	Scale HLE	gpt-oss-120b
Synthetic CDQuestions from gpt-oss-120b	Text	Undisclosed	CDQuestions	gpt-oss-120b
Synthetic Stack Exchange from gpt-oss-120b	Text	Undisclosed	Stack Exchange	gpt-oss-120b
Synthetic GPQA from gpt-oss-120b and Qwen2.5-32B-Instruct	Text	Undisclosed	Stack Exchange	gpt-oss-120b; Qwen2.5-32B-Instruct
Synthetic Vedantu from gpt-oss-120b	Text	Undisclosed	Vedantu	gpt-oss-120b
Synthetic SWE-Gym and R2E-Gym-Subset from Qwen3-Coder-480B-A35B-Instruct	Text	Undisclosed	SWE-Gym; R2E-Gym-Subset	Qwen3-Coder-480B-A35B-Instruct
Synthetic SWE-Gym from Qwen3-Coder-480B-A35B-Instruct	Text	Undisclosed	SWE-Gym	Qwen3-Coder-480B-A35B-Instruct
Synthetic SWE-Gym and R2E-Gym-Subset from DeepSeek-R1-0528	Text	Undisclosed	SWE-Gym; R2E-Gym-Subset	DeepSeek-R1-0528
Synthetic HelpSteer, LMSYS-Chat-1M, and Nemotron-Personas-USA from gpt-oss-120b, Qwen3-235B-A22B-Instruct-2507, and Qwen3-235B-A22B-Thinking-2507	Text	Undisclosed	HelpSteer2; HelpSteer3; LMSYS-Chat-1M; Nemotron-Personas-USA	gpt-oss-120b; Qwen3-235B-A22B-Instruct-2507; Qwen3-235B-A22B-Thinking-2507
Synthetic Structured Outputs from Qwen3-30B-A3B-Instruct-2507, Qwen3-30B-A3B-Thinking-2507, Qwen3-235B-A22B-Instruct-2507, and Qwen3-235B-A22B-Thinking-2507	Text	Undisclosed	-	Qwen3-30B-A3B-Instruct-2507; Qwen3-30B-A3B-Thinking-2507; Qwen3-235B-A22B-Instruct-2507; Qwen3-235B-A22B-Thinking-2507
Synthetic Search STEM MCQ from Qwen3-235B-A22B and DeepSeek-R1-0528	Text	Undisclosed	-	Qwen3-235B-A22B; DeepSeek-R1-0528
Synthetic Search STEM OPENQ from DeepSeek-R1-0528	Text	Undisclosed	-	DeepSeek-R1-0528
Synthetic OpenSTEM from Qwen2.5-32B-Instruct and DeepSeek-R1-0528	Text	Undisclosed	-	Qwen2.5-32B-Instruct; DeepSeek-R1-0528
Synthetic MCQ from Qwen2.5-32B-Instruct and DeepSeek-R1-0528	Text	Undisclosed	-	Qwen2.5-32B-Instruct; DeepSeek-R1-0528
Synthetic MCQ10 from DeepSeek-R1-0528	Text	Undisclosed	-	DeepSeek-R1-0528
Synthetic MCQ4 from Qwen3-235B-A22B, DeepSeek-R1-0528, and Qwen3-235B-A22B-Instruct-2507	Text	Undisclosed	-	Qwen3-235B-A22B; DeepSeek-R1-0528; Qwen3-235B-A22B-Instruct-2507
Synthetic OpenMathReasoning from gpt-oss-120b and Qwen2.5-32B-Instruct	Text	Undisclosed	OpenMathReasoning	gpt-oss-120b; Qwen2.5-32B-Instruct
Synthetic Offline Search MCQA HLE from DeepSeek-R1-0528	Text	Undisclosed	-	DeepSeek-R1-0528
Synthetic Offline Search MCQA GPQA from Qwen3-235B-A22B and DeepSeek-R1-0528	Text	Undisclosed	-	Qwen3-235B-A22B; DeepSeek-R1-0528
Synthetic Human Preference from QwQ-32B, Qwen3-30B-A3B, Qwen3-235B-A22B, Qwen3-235B-A22B-Instruct-2507, Mistral-Small-3.1-24B-Instruct-2503, Mistral-Small-3.2-24B-Instruct-2506, MiniMax-M1-80k, MiniMax-M1-40k, Kimi-K2-Instruct, DeepSeek-V3-0324, DeepSeek-R1-0528	Text	Undisclosed	-	QwQ-32B; Qwen3-30B-A3B; Qwen3-235B-A22B; Qwen3-235B-A22B-Instruct-2507; Mistral-Small-3.1-24B-Instruct-2503; Mistral-Small-3.2-24B-Instruct-2506; MiniMax-M1-80k; MiniMax-M1-40k; Kimi-K2-Instruct; DeepSeek-V3-0324; DeepSeek-R1-0528
Synthetic WildChat-1M and arena-human-preference-140k from DeepSeek-R1, gemma-2-2b-it, gemma-3-27b-it, gpt-oss-20b, gpt-oss-120b, Mistral-7B-Instruct-v0.3, Mixtral-8x22B-Instruct-v0.1, Nemotron-4-340B-Instruct, NVIDIA-Nemotron-Nano-9B-v2, Phi-4-mini-instruct, Phi-3-small-8k-instruct, Phi-3-medium-4k-instruct, Qwen3-235B-A22B, QwQ-32B	Text	Undisclosed	WildChat-1M; arena-human-preference-140k	DeepSeek-R1; gemma-2-2b-it; gemma-3-27b-it; gpt-oss-20b; gpt-oss-120b; Mistral-7B-Instruct-v0.3; Mixtral-8x22B-Instruct-v0.1; Nemotron-4-340B-Instruct; NVIDIA-Nemotron-Nano-9B-v2; Phi-4-mini-instruct; Phi-3-small-8k-instruct; Phi-3-medium-4k-instruct; Qwen3-235B-A22B; QwQ-32B
Synthetic Safety from DeepSeek-R1-0528, gpt-oss-120b, DeepSeek-R1-Distill-Qwen-7B, and Mixtral-8x7B-v0.1	Text	Undisclosed	Nemotron Content Safety Dataset V2; Gretel Synthetic Safety Alignment Dataset; RedTeam-2K; Malicious Tasks;	DeepSeek-R1-0528; gpt-oss-120b; DeepSeek-R1-Distill-Qwen-7B; Qwen3-30B-A3B-Thinking-2507; Qwen3-235B-A22B-Instruct-2507; Mixtral-8x7B-v0.1
Synthetic Code from Qwen3-32B	Text	Undisclosed	English Common Crawl; English Common Crawl 1.1	Qwen3-32B
Synthetic OpenCodeReasoning from DeepSeek-R1	Text	Undisclosed	OpenCodeReasoning	DeepSeek-R1
Synthetic LIMO from DeepSeek-R1-0528	Text	Undisclosed	LIMO	DeepSeek-R1-0528
Synthetic SCP from DeepSeek-R1-0528	Text	Undisclosed	SCP-116K	DeepSeek-R1-0528
Synthetic Stack Exchange from DeepSeek-R1-0528	Text	Undisclosed	Stack Exchange	DeepSeek-R1-0528
Synthetic Common Crawl from Qwen3-30B-A3B	Text	Undisclosed	Common Crawl	Qwen3-30B-A3B
Synthetic Wikipedia from Qwen3-30B-A3B	Text	Undisclosed	Wikimedia	Qwen3-30B-A3B
Synthetic Essential-Web from Qwen3-30B-A3B and Qwen3-235B-A22B-Thinking-2507	Text	Undisclosed	Essential-Web	Qwen3-30B-A3B; Qwen3-235B-A22B-Thinking-2507
Synthetic Textbook Math from Qwen3-30B-A3B, Qwen3-235B-A22B, phi-4	Text	Undisclosed	Common Crawl; FineMath	Qwen3-30B-A3B; Qwen3-235B-A22B; phi-4
Synthetic Math and Code from DeepSeek-R1 and DeepSeek-R1-0528	Text	Undisclosed	Magicoder-Evol-Instruct-110K; opc-sft-stage2; TACO; OpenCodeReasoning; OpenMathReasoning; NuminaMath CoT	DeepSeek-R1; DeepSeek-R1-0528
Synthetic Nemotron-Personas-USA from gpt-oss-120b and Qwen3-8B	Text	Undisclosed	Nemotron-Personas-USA	gpt-oss-120b; Qwen3-8B
Synthetic Text-To-SQL	Text	Undisclosed	-	gpt-oss-120b
Synthetic Agentless SWE	Text	Undisclosed	SWE-Bench-Train; SWE-Fixer-Train; SWE-reBench; SWE-smith	DeepSeek-R1-0528
Synthetic Search Graph Walk	Text	Undisclosed	-	MiniMax-M2
Synthetic CUDA 100k	Text	Undisclosed	KernelBook; HuggingFace Transformers; FlashInfer	DeepSeek-R1-0528; gpt-oss-120b
Synthetic Safety	Text	Undisclosed	Nemotron Content Safety Dataset V2; Gretel Synthetic Safety Alignment Dataset; RedTeam-2K; HarmfulTasks	gpt-oss-120b; NVIDIA-Nemotron-Nano-9B-v2; gemma-3-4b-it
Synthetic Agentic Diverse Domains	Text	Undisclosed	-	DeepSeek-R1-0528; Qwen3-235B-A22B-Thinking-2507; Qwen3-235B-A22B-Instruct-2507; Qwen3-32B; gpt-oss-120b; DeepSeek-V3.2
Synthetic SWE Unverified	Text	Undisclosed	-	gpt-oss-120b; Qwen3-Coder-480B-A35B-Instruct; GLM-4.7-Flash
Synthetic Scale HLE from Deepseek-V3	Text	Undisclosed	Scale HLE	DeepSeek-V3-0324
Synthetic CDQuestions from Deepseek-V3	Text	Undisclosed	CDQuestions	DeepSeek-V3-0324
Synthetic Stack Exchange from Deepseek-V3	Text	Undisclosed	Stack Exchange	DeepSeek-V3-0324
Synthetic GPQA from Deepseek-V3	Text	Undisclosed	Stack Exchange	DeepSeek-V3-0324
Synthetic Vedantu from Deepseek-V3	Text	Undisclosed	Vedantu	DeepSeek-V3-0324
Synthetic Tool Call Schema for RL	Text	Undisclosed	ToolBench; glaive-function-calling-v2; APIGen Function-Calling; Nemotron-Personas-USA	Qwen3-235B-A22B-Thinking-2507; Qwen3-Next-80B-A3B-Thinking
Synthetic Data for Search	Text	Undisclosed	Wikimedia	MiniMax-M2
Synthetic Instruction Following for RL	Text	Undisclosed	-	NVIDIA-Nemotron-Nano-9B-v2; Qwen3-235B-A22B-Thinking-2507
Synthetic Conversational Agentic Tool-Use RL	Text	Undisclosed	-	DeepSeek-V3.2; DeepSeek-R1-0528; Qwen3-235B-A22B-Thinking-2507; Qwen3-32B; gpt-oss-120b; Qwen3-235B-A22B-Instruct-2507
Synthetic Terminal Pivot RL	Text	Undisclosed	SWE-smith; Nemotron-Cascade-RL-SWE; Vendor supplied	DeepSeek-V3.2; Qwen3-Coder-480B-A35B-Instruct; Kimi-K2.5; Qwen3-235B-A22B-Instruct-2507

Evaluation Dataset

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

Inference:

Acceleration Engine: vLLM

Test Hardware:

1× NVIDIA H100-80GB
8× NVIDIA H100-80GB
8× NVIDIA B200

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. 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.