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NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-BF16

Name: NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-BF16
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NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-BF16

Model Developer: NVIDIA

Model Dates:

September 2025 - December 2025

Data Freshness:

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

Model Overview

NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-BF16 is a 3-in-1 elastic large language model (LLM) developed by NVIDIA. It contains three nested model variants (30B, 23B, and 12B parameters) within a single BF16 checkpoint, all sharing the same parameter space. The 23B and 12B variants can be extracted zero-shot from this checkpoint using the provided slicing script.

This model was derived from NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 using the Elastic post-training framework, which produces elastic (many-in-one) reasoning LLMs from hybrid Mamba-Transformer-MoE architectures. The method takes the parent reasoning model and embeds N nested submodels within it using the compute of a single training run. The technology was originally developed for hybrid Mamba-Transformer architectures and has been extended to hybrid MoE models for Nemotron 3 Nano.

📄 The accompanying paper, "Star Elastic: Many-in-One Reasoning LLMs with Efficient Budget Control", has been accepted for publication at ICML 2026 (see Citation).

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

All three nested variants share the same 52-layer architecture pattern and the same number of attention heads (32), Mamba heads (64), and MoE experts (128). The variants differ in embedding dimension and MoE FFN dimension:

Variant	Total Params	Active Params	Embedding Dim	MoE FFN Dim
30B	30B	3.6B	2688	1856
23B	23B	2.8B	2304	1600
12B	12B	2.0B	1920	960

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

This model is ready for commercial use.

Average accuracy of Elastic variants compared to the parent Nemotron 3 Nano 30B and Qwen3-30B-A3B across key reasoning benchmarks (BF16 precision). The Elastic-30B variant matches or exceeds the parent model on most benchmarks, while the 23B and 12B variants provide strong accuracy at reduced compute.

Quantized Accuracy Recovery

The following table summarizes accuracy recovery of quantized variants relative to BF16:

Model Variant	FP8 Recovery (Avg)	NVFP4 Recovery (Avg)
30B (3.6A)	98.69%	97.79%
23B (2.8A)	99.03%	99.15%
12B (2.0A)	100.26%	97.10%

Throughput Improvements

The smaller elastic variants provide substantial throughput improvements when served with vLLM (measured on H100 GPU, ISL=8192 / OSL=16384, BF16 precision):

Variant	Max Batch Size	Throughput Multiplier
30B (3.6A)	36	1.0x (baseline)
23B (2.8A)	108	1.8x
12B (2.0A)	224	2.4x

The smaller nested models also enable much higher batch sizes on the same GPU (224 vs 36), providing significant serving cost reductions.

Elastic Budget Control

Elastic Budget Control is a novel inference-time mechanism enabled by the nested architecture. Instead of using a fixed model for both the thinking (<think>) and answering phases, elastic budget control uses different-sized nested models for each phase.

Pareto frontier of accuracy vs. latency for different elastic budget control configurations. The figure shows that using different model sizes for the thinking and answering phases (e.g., 23B thinking -> 30B answering) achieves better accuracy-latency tradeoffs than using a single model size throughout. The optimal configuration uses a smaller model for high-volume reasoning (thinking phase) and a larger model for high-fidelity synthesis (answering phase), achieving up to 16% higher accuracy and 1.9x lower latency compared to standard single-model budget control.

Four configurations are possible using models M_L (Large) and M_S (Small):

M_L -> M_L: Large model for both thinking and answering
M_S -> M_S: Small model for both thinking and answering
M_L -> M_S: Large model for thinking, small for answering
M_S -> M_L: Small model for thinking, large for answering (identified as optimal)

The M_S -> M_L configuration is optimal because:

Thinking phase (high-volume reasoning): Benefits from larger token budgets to explore reasoning paths; using the smaller model generates extensive reasoning traces with minimal computational overhead.
Answering phase (high-fidelity synthesis): Requires superior instruction-following and consistency; the larger model provides the necessary capacity for robust synthesis.

The 23B -> 30B configuration achieves the best accuracy-latency tradeoffs over a wide range of budgets.

⚠️ Note on inference support. Elastic budget control is not yet supported in the standard vLLM inference engine — switching nested sub-models within a single generation (e.g., 23B → 30B think → answer) currently requires a custom inference path. Nested models preserve the Mamba and attention layer structure, enabling cache-state transplantation between models, and efficient native vLLM integration is actively being worked on.

Elastic Architecture Overview

A key innovation of this model is its Elastic Architecture, which enables the extraction of smaller, nested variants (23B and 12B parameters) from the same parameter space without requiring separate training runs.

The Elastic pipeline operates in three stages:

Importance Estimation: Components (embedding dimensions, attention heads, Mamba heads, MoE experts, FFN channels) are ranked by importance scores using calibration data.
Elastic Formulation: Smaller-budget sub-networks are defined as contiguous subsets of the most salient components, forming a nested hierarchy.
Elastic Training: A learnable router with Gumbel-Softmax selection is trained end-to-end using knowledge distillation from the frozen parent model, with a two-stage curriculum (8K context, then 49K context).

The entire elastic family was produced by post-training the Nemotron 3 Nano 30B parent with only approximately 160B tokens — roughly 0.6% of the parent's ~25T-token pretraining budget, and far less than what would be required to train three independent compressed variants.

Key Benefits

3-in-1 Nested Checkpoint: All three model sizes (12B/23B/30B) are embedded in a single checkpoint via nested weight sharing. Deploying all three variants requires only 58.9 GB in BF16, a 2.14x memory reduction compared to storing three independent checkpoints (126.1 GB).
Compute-efficient elastification: The entire 30B + 23B + 12B nested family was produced by post-training the Nemotron 3 Nano 30B parent in a single training run with only ~160B tokens — about 0.6% of the parent's ~25T pretraining budget, and far less than retraining or independently compressing three separate models.
Full Accuracy-Latency Frontier: The nested BF16 model covers all latency regimes via elastic budget control, delivering up to 16% higher accuracy and 1.9x faster inference compared to standard single-model budget control.
BF16 Accuracy: The full 30B elastic model is on par with the parent Nemotron 3 Nano 30B, with 2 smaller models extracted for free, providing an accuracy-size Pareto frontier.
Unlocks lower-tier RTX community prototyping: As a bonus, the 12B and 23B variants in FP8 / NVFP4 fit on consumer/prosumer RTX series lower tiers, including 6000 / 5090 / 5080. This should make Nano V3 architecture easier to prototype and debug.

Zero-Shot Slicing Before Deployment

This checkpoint contains the full 30B 3-in-1 model. Before deploying a smaller variant, you can perform zero-shot slicing to extract the 23B or 12B model directly from this checkpoint — no additional training or fine-tuning is required. The sliced model is ready for immediate deployment.

Use the provided zero_shot_slicing.py script:

bash

# Zero-shot slice the 23B variant for deployment
python zero_shot_slicing.py \
    --source-checkpoint <path-to-this-30B-checkpoint> \
    --target-checkpoint ./nemotron-elastic-23b-bf16 \
    --size 23B \
    --precision bf16

# Zero-shot slice the 12B variant for deployment
python zero_shot_slicing.py \
    --source-checkpoint <path-to-this-30B-checkpoint> \
    --target-checkpoint ./nemotron-elastic-12b-bf16 \
    --size 12B \
    --precision bf16

The zero-shot slicing process preserves the hybrid MoE architecture while reducing model size through structured pruning of embedding dimensions and MoE FFN dimensions. Because the nested variants share the most salient weights with the parent model, the sliced checkpoints retain strong accuracy without any additional knowledge distillation or fine-tuning. The shared expert intermediate size (3712) is left unchanged.

Available size presets:

Size	Target Hidden Size	Target Intermediate Size
23B	2304	1600
12B	1920	960

Deployment Memory

Configuration	Models	Total Memory (BF16)
Nemotron 3 Elastic	12B + 23B + 30B	58.9 GB
Separate NanoV3	12B + 23B + 30B	126.1 GB

Quick Start Guide

Use it with Transformers

python

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load the full 30B elastic model
tokenizer = AutoTokenizer.from_pretrained("nvidia/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-BF16", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    "nvidia/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-BF16",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

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)

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

temperature=1.0 and top_p=1.0 are recommended for reasoning tasks.

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

Note: To use the 23B or 12B variants, first extract them using the slicing script as described in the Zero-Shot Slicing Before Deployment section, then load the extracted checkpoint.

Use it with vLLM

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

pip install -U "vllm>=0.12.0"

Download the custom parser from the Hugging Face repository.

wget https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16/resolve/main/nano_v3_reasoning_parser.py

Launch a vLLM server using the custom parser.

bash

vllm serve nvidia/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-BF16 \
  --served-model-name model \
  --max-num-seqs 8 \
  --tensor-parallel-size 1 \
  --max-model-len 131072 \
  --port 8000 \
  --trust-remote-code \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_coder \
  --reasoning-parser-plugin nano_v3_reasoning_parser.py \
  --reasoning-parser nano_v3

In the example above, we use a context length of 128k. You can increase the context size up to 1M to support longer contexts. To enable this, set the VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 environment variable as shown below:

bash

VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 \
vllm serve nvidia/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-BF16 \
  --served-model-name model \
  --max-num-seqs 8 \
  --tensor-parallel-size 1 \
  --max-model-len 1M \
  --port 8000 \
  --trust-remote-code \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_coder \
  --reasoning-parser-plugin nano_v3_reasoning_parser.py \
  --reasoning-parser nano_v3

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

bash

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

If you'd like to use reasoning off with vLLM, you can do the following:

vLLM OpenAI curl request:

bash

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

vLLM OpenAI client:

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

Note: To serve the 23B or 12B variants with vLLM, first extract them using the zero-shot slicing script as described in the Zero-Shot Slicing Before Deployment section, then point vLLM to the sliced checkpoint.

Prompt Format

The prompt format is the same as the NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 model. Please refer to that model card for detailed prompt formatting information and usage examples.

License/Terms of Use

Your use of this model is governed by the NVIDIA Open Model License.

Deployment Geography

Global

Use Case

This model is intended for developers designing AI Agent systems, chatbots, RAG systems, and other AI-powered applications. Supported for English and coding languages, with additional support for Spanish, French, German, Japanese, and Italian.

Release Date

Hugging Face: 05/07/2026 via NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-BF16

Model Architecture

Architecture Type: Mamba2-Transformer Hybrid Mixture of Experts (MoE)
Network Architecture: Nemotron Hybrid MoE
Number of model parameters: 30B (full), 23B and 12B (nested variants)
Number of layers: 52
Layer pattern: M-E-M-E-M*-E-M-E-M-E-M*-E-... (M=Mamba, E=MoE, *=Attention)
Number of attention heads: 32
Number of Mamba heads: 64
Number of MoE experts: 128 routed + 1 shared (6 active per token)

Input

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

Output

Output Type(s): Text
Output Format: String
Output Parameters: One-Dimensional (1D): Sequences
Maximum output size: 128K tokens
Other Properties Related to Output: Context length up to 128K tokens for output generation. Text-only output in supported languages (English, Spanish, French, German, Japanese, Italian).

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
Supported Hardware Microarchitecture Compatibility: NVIDIA Hopper, NVIDIA Blackwell
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.

This AI model can be embedded as an Application Programming Interface (API) call into the software environment described above.

Model Version(s)

NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-BF16 v1.0 — Elastic 3-in-1 BF16 model containing 30B, 23B, and 12B nested variants (trained, deployable)

Training Methodology

This elastic model was post-trained from NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 using the Elastic framework with knowledge distillation.

Elastic Training Details:

Total training budget: ~160B tokens
Stage 1: Uniform budget sampling, sequence length 8192, ~100B tokens
Stage 2: Curriculum-based non-uniform sampling (skewed toward larger budgets), sequence length 49152, ~60B tokens
Optimizer: LR 1e-4 for model, LR 1e-2 for router, 60-step linear warmup
Data blend: 70% reasoning (post-training/SFT) + 30% pretraining data
Loss: KL divergence between frozen parent (teacher) and elastic model (student) + router resource cost loss

The extended context in Stage 2 is critical for reasoning performance. The two-stage curriculum (8K then 49K context) outperforms training at 49K from the start.

For details on the parent model's pre-training, supervised fine-tuning, and reinforcement learning stages, please refer to the NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 model card.

Training and Evaluation Datasets

The elastic post-training used a blend of 70% reasoning (post-training/SFT) data and 30% pretraining data from the parent model's training corpus. The full dataset disclosure below is inherited from the parent model NVIDIA-Nemotron-3-Nano-30B-A3B-BF16.

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

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

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

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

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

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

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

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

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

Private Non-publicly Accessible Datasets of Third Parties

Dataset
Global Regulation
TAUS Translation Memory
Scale HLE
HackerRank Coding

Private Non-publicly Accessible Datasets by NVIDIA

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

Crawled and Scraped from Online Sources by NVIDIA

The English Common Crawl data was downloaded from the Common Crawl Foundation (see their FAQ for details on their crawling) and includes the snapshots CC-MAIN-2013-20 through CC-MAIN-2025-13. The data was subsequently deduplicated and filtered in various ways described in the Nemotron-CC paper. Additionally, we extracted data for fifteen languages from the following three Common Crawl snapshots: CC-MAIN-2024-51, CC-MAIN-2025-08, CC-MAIN-2025-18. The fifteen languages included were Arabic, Chinese, Danish, Dutch, French, German, Italian, Japanese, Korean, Polish, Portuguese, Russian, Spanish, Swedish, and Thai. As we did not have reliable multilingual model-based quality classifiers available, we applied just heuristic filtering instead—similar to what we did for lower quality English data in the Nemotron-CC pipeline, but selectively removing some filters for some languages that did not work well. Deduplication was done in the same way as for Nemotron-CC.

The GitHub Crawl was collected using the GitHub REST API and the Amazon S3 API. Each crawl was operated in accordance with the rate limits set by its respective source, either GitHub or S3. We collect raw source code and subsequently remove any having a license which does not exist in our permissive-license set (for additional details, refer to the technical report).

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

NVIDIA-Sourced Synthetic Datasets

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

Training Dataset

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

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

We extracted data for fifteen languages from the following three Common Crawl snapshots: CC-MAIN-2024-51, CC-MAIN-2025-08, CC-MAIN-2025-18. The fifteen languages included were Arabic, Chinese, Danish, Dutch, French, German, Italian, Japanese, Korean, Polish, Portuguese, Russian, Spanish, Swedish, and Thai. As we did not have reliable multilingual model-based quality classifiers available, we applied just heuristic filtering instead—similar to what we did for lower quality English data in the Nemotron-CC pipeline, but selectively removing some filters for some languages that did not work well. Deduplication was done in the same way as for Nemotron-CC. Additionally, we used data from Wikipedia and FineWeb-2 (Penedo et al., 2025) for these fifteen languages as well as four additional languages: Czech, Finnish, Hebrew, and Hindi.

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

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

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

Language Distribution in Post-Training

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

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

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

Evaluation Dataset

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

Inference

Acceleration Engine: HF, vLLM, TRT-LLM, SGLang, Llama.cpp
Test Hardware: NVIDIA A100 80GB, H100 80GB, B200 192GB, RTX PRO 6000 96GB, Jetson Thor, DGX Spark

Ethical Considerations

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our Trustworthy AI terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

For more detailed information on ethical considerations for this model, please see the Model Card++ subcards: Bias, Explainability, Privacy, and Safety.

Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns here.

Benchmark Results

Reasoning Evaluations (BF16 Precision)

Benchmark	Elastic-12B (2.0A)	Elastic-23B (2.8A)	Elastic-30B (3.6A)	NanoV3-30B (3.6A)	Qwen3-30B-A3B (3.3A)
AIME-2025	78.54	85.63	88.54	87.92	80.00
GPQA	57.39	69.82	72.10	73.11	70.83
LiveCodeBench v5	55.24	67.30	72.70	71.75	68.25
MMLU-Pro	68.28	76.07	78.63	78.86	81.11
IFBench (prompt)	64.03	67.43	70.58	70.82	43.28
IFBench (instruct)	67.39	70.75	73.96	73.19	46.57
Tau-Airline	24.67	38.67	43.33	44.67	52.67
Tau-Retail	49.12	55.56	59.36	53.51	56.43
Tau-Telecom	29.33	30.99	33.33	30.99	28.36

Key observations: Elastic-30B matches or exceeds the parent NanoV3-30B on most benchmarks (e.g., AIME-2025: 88.54 vs 87.92, IFBench instruct: 73.96 vs 73.19, Tau-Retail: 59.36 vs 53.51). Both Elastic-23B and Elastic-12B outperform Qwen3-30B-A3B on AIME-2025 and IFBench by large margins.

Citation

If you use this model, please cite our paper, accepted to ICML 2026:

bibtex

@inproceedings{taghibakhshi2026starelastic,
  title     = {Star Elastic: Many-in-One Reasoning {LLMs} with Efficient Budget Control},
  author    = {Taghibakhshi, Ali and Cai, Ruisi and Muralidharan, Saurav and Turuvekere Sreenivas, Sharath and Mahabaleshwarkar, Ameya and Chochowski, Marcin and Bercovich, Akhiad and Zilberstein, Ran and El-Yaniv, Ran and Geifman, Yonatan and Korzekwa, Daniel and Suhara, Yoshi and Olabiyi, Oluwatobi and Aithal, Ashwath and Tajbakhsh, Nima and Molchanov, Pavlo},
  booktitle = {Proceedings of the 43rd International Conference on Machine Learning},
  series    = {ICML 2026},
  year      = {2026},
  note      = {Accepted}
}

bibtex

@article{taghibakhshi2025nemotronelastic,
  title   = {Nemotron Elastic: Towards Efficient Many-in-One Reasoning LLMs},
  author  = {Taghibakhshi, Ali and Turuvekere Sreenivas, Sharath and Muralidharan, Saurav and Cai, Ruisi and Chochowski, Marcin and Mahabaleshwarkar, Ameya Sunil and Suhara, Yoshi and Olabiyi, Oluwatobi and Korzekwa, Daniel and Patwary, Mostofa and Shoeybi, Mohammad and Kautz, Jan and Catanzaro, Bryan and Aithal, Ashwath and Tajbakhsh, Nima and Molchanov, Pavlo},
  journal = {arXiv preprint arXiv:2511.16664},
  year    = {2025},
  url     = {https://arxiv.org/abs/2511.16664}
}

bibtex

@article{blakeman2025nemotron,
  title   = {Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning},
  author  = {Blakeman, Aaron and Grattafiori, Aaron and Basant, Aarti and Gupta, Abhibha and Khattar, Abhinav and Renduchintala, Adi and Vavre, Aditya and Shukla, Akanksha and Bercovich, Akhiad and Ficek, Aleksander and others},
  journal = {arXiv preprint arXiv:2512.20848},
  year    = {2025},
  url     = {https://arxiv.org/abs/2512.20848}
}

Arsh9210/NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-BF16

NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-BF16

Model Overview

Quantized Accuracy Recovery

Throughput Improvements

Elastic Budget Control

Elastic Architecture Overview

Key Benefits

Zero-Shot Slicing Before Deployment

Deployment Memory

Quick Start Guide

Use it with Transformers

Use it with vLLM

Prompt Format

License/Terms of Use

Deployment Geography

Use Case

Release Date

Model Architecture

Input

Output

Software Integration

Model Version(s)

Training Methodology

Training and Evaluation Datasets

Private Non-publicly Accessible Datasets of Third Parties

Private Non-publicly Accessible Datasets by NVIDIA

Crawled and Scraped from Online Sources by NVIDIA

NVIDIA-Sourced Synthetic Datasets

Training Dataset

Language Distribution in Post-Training

Evaluation Dataset

Inference

Ethical Considerations

Benchmark Results

Reasoning Evaluations (BF16 Precision)

Citation

Arsh9210/
NVIDIA-Nemotron-Labs-3-Elastic-30B-A3B-BF16