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Privacy

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n

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Field:	Response:
Generatable or Reverse engineerable personally-identifiable information?	None
Was consent obtained for any personal data used?	None Known
Personal data used to create this model?	None Known
How often is dataset reviewed?	Before Release
Is there provenance for all datasets used in training?	Yes
Does data labeling (annotation, metadata) comply with privacy laws?	Yes
Applicable NVIDIA Privacy Policy	https://www.nvidia.com/en-us/about-nvidia/privacy-policy/

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Llama-3.3-Nemotron-Super-49B-v1.5 Overview

Description:

Llama-3.3-Nemotron-Super-49B-v1.5 is a significantly upgraded version of Llama-3.3-Nemotron-Super-49B-v1 and is a large language model (LLM) which is a derivative of Meta Llama-3.3-70B-Instruct (AKA the reference model). It is a reasoning model that is post trained for reasoning, human chat preferences, and agentic tasks, such as Retrieval-Augmented Generation (RAG) and tool calling. The model supports a context length of 128K tokens.

Llama-3.3-Nemotron-Super-49B-v1.5 is a model which offers a great tradeoff between model accuracy and efficiency. Efficiency (throughput) directly translates to savings. Using a novel Neural Architecture Search (NAS) approach, we greatly reduce the model’s memory footprint, enabling larger workloads, as well as fitting the model on a single GPU at high workloads (H200). This NAS approach enables the selection of a desired point in the accuracy-efficiency tradeoff. For more information on the NAS approach, please refer to this paper.

The model underwent a multi-phase post-training process to enhance both its reasoning and non-reasoning capabilities. This includes a supervised fine-tuning stage for Math, Code, Science, and Tool Calling. Additionally, the model went through multiple stages of Reinforcement Learning (RL) including Reward-aware Preference Optimization (RPO) for chat, Reinforcement Learning with Verifiable Rewards (RLVR) for reasoning, and iterative Direct Preference Optimization (DPO) for Tool Calling capability enhancements. The final checkpoint was achieved after merging several RL and DPO checkpoints.

This model is part of the Llama Nemotron Collection. You can find the other model(s) in this family here:

This model is ready for commercial use.

License/Terms of Use

GOVERNING TERMS: The NIM container is governed by the NVIDIA Software License Agreement and the Product-Specific Terms for NVIDIA AI Products; and the use of this model is governed by the NVIDIA Open Model License.

Additional Information: Llama 3.3 Community License Agreement. Built with Llama.

Get Help

Enterprise Support

Get access to knowledge base articles and support cases or submit a ticket.\nYou are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.

Deployment Geography:

Global

Use Case:

Llama-3.3-Nemotron-Super-49B-v1.5 is a general purpose reasoning and chat model intended to be used in English and coding languages. Other non-English languages (German, French, Italian, Portuguese, Hindi, Spanish, and Thai) are also supported. Developers designing AI Agent systems, chatbots, RAG systems, and other AI-powered applications. Also suitable for typical instruction-following tasks.

Release Date:

Hugging Face 7/25/2025 via Llama-3_3-Nemotron-Super-49B-v1_5
Build.NVIDIA.com 7/25/2025 Llama-3_3-Nemotron-Super-49B-v1_5

References

Model Architecture

Architecture Type: Dense decoder-only Transformer model

Network Architecture: Llama 3.3 70B Instruct, customized through Neural Architecture Search (NAS)

The model is a derivative of Meta’s Llama-3.3-70B-Instruct, using Neural Architecture Search (NAS). The NAS algorithm results in non-standard and non-repetitive blocks. This includes the following:

Skip attention: In some blocks, the attention is skipped entirely, or replaced with a single linear layer. Variable FFN: The expansion/compression ratio in the FFN layer is different between blocks.

We utilize a block-wise distillation of the reference model, where for each block we create multiple variants providing different tradeoffs of quality vs. computational complexity, discussed in more depth below. We then search over the blocks to create a model which meets the required throughput and memory (optimized for a single H100-80GB GPU) while minimizing the quality degradation. The model then undergoes knowledge distillation (KD), with a focus on English single and multi-turn chat use cases. The KD step included 40 billion tokens consisting of a mixture of 3 datasets - FineWeb, Buzz-V1.2, and Dolma.

Intended Use
\nLlama-3.3-Nemotron-Super-49B-v1.5 is a general purpose reasoning and chat model intended to be used in English and coding languages. Other non-English languages (German, French, Italian, Portuguese, Hindi, Spanish, and Thai) are also supported.

Input

Input Type(s): Text
Input Format: String
Input Parameters: One-Dimensional (1D)
Other Properties Related to Input: Context length up to 131,072 tokens

Output

Output Type(s): Text
Output Format: String
Output Parameters: One-Dimensional (1D)
Other Properties Related to Output: Context length up to 131,072 tokens

Our AI models are designed and/or 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: vLLM, TensorRT
Supported Hardware Microarchitecture Compatibility:\n
- NVIDIA Ampere
- NVIDIA Hopper
\n
Preferred 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:

Llama-3.3-Nemotron-Super-49B-v1.5-1.12.0
\nLlama-3.3-Nemotron-Super-49B-v1.5-1.13.1
\nLlama-3.3-Nemotron-Super-49B-v1.5-1.14.0
\nLlama-3.3-Nemotron-Super-49B-v1.5-1.15.0
\nLlama-3.3-Nemotron-Super-49B-v1.5-1.15.5

Quick Start and Usage Recommendations:

By default (empty system prompt), the model will respond in reasoning ON mode. Setting /no_think in the system prompt will enable reasoning OFF mode.
We recommend setting temperature to 0.6 and Top P to 0.95 for Reasoning ON mode.
We recommend using greedy decoding for Reasoning OFF mode.

You can try this model out through the preview API, using this link: Llama-3_3-Nemotron-Super-49B-v1_5.

Use It with vLLM

pip install vllm==0.9.2

An example on how to serve with vLLM:

$ python3 -m vllm.entrypoints.openai.api_server \\\n  --model \"nvidia/Llama-3_3-Nemotron-Super-49B-v1_5\" \\\n  --trust-remote-code \\\n  --seed=1 \\\n  --host=\"0.0.0.0\" \\\n  --port=5000 \\\n  --served-model-name \"Llama-3_3-Nemotron-Super-49B-v1_5\" \\\n  --tensor-parallel-size=8 \\\n  --max-model-len=65536 \\\n  --gpu-memory-utilization 0.95 \\\n  --enforce-eager\n

Running a vLLM Server with Tool-call Support

To enable tool calling usage with this model, we provide a tool parser in the repository. Here is an example on how to use it:

$ git clone https://huggingface.co/nvidia/Llama-3_3-Nemotron-Super-49B-v1_5\n\n$ conda create -n vllm python=3.12 -y\n$ conda activate vllm\n$ pip install vllm==0.9.2\n\n$ python3 -m vllm.entrypoints.openai.api_server \\\n  --model Llama-3_3-Nemotron-Super-49B-v1_5 \\\n  --trust-remote-code \\\n  --seed=1 \\\n  --host=\"0.0.0.0\" \\\n  --port=5000 \\\n  --served-model-name \"Llama-3_3-Nemotron-Super-49B-v1_5\" \\\n  --tensor-parallel-size=8 \\\n  --max-model-len=65536 \\\n  --gpu-memory-utilization 0.95 \\\n  --enforce-eager \\\n  --enable-auto-tool-choice \\\n  --tool-parser-plugin \"Llama-3_3-Nemotron-Super-49B-v1_5/llama_nemotron_toolcall_parser_no_streaming.py\" \\\n  --tool-call-parser \"llama_nemotron_json\"\n

After launching a vLLM server, you can call the server with tool-call support using a Python script like below.

\nOkay, let's see. The user has a bill of $100 and wants to know the amount for an 18% tip. Hmm, I need to calculate the tip based on the bill total and the percentage. The tools provided include calculate_tip, which takes bill_total and tip_percentage as parameters. So the bill_total here is 100, and the tip_percentage is 18. I should call the calculate_tip function with these values. Wait, do I need to check if the parameters are integers? The bill is $100, which is an integer, and 18% is also an integer. So that fits the function's requirements. I don't need to convert any currency here because the user is asking about a tip in the same currency. So the correct tool to use is calculate_tip with those parameters.\n\n'''\nprint(completion.choices[0].message.tool_calls)\n'''\n[ChatCompletionMessageToolCall(id='chatcmpl-tool-e341c6954d2c48c2a0e9071c7bdefd8b', function=Function(arguments='{"bill_total": 100, "tip_percentage": 18}', name='calculate_tip'), type='function')]\n'''\n\">

from openai import OpenAI\nclient = OpenAI(\n    base_url=\"http://0.0.0.0:5000/v1\",\n    api_key=\"dummy\",\n)\ncompletion = client.chat.completions.create(\n    model=\"Llama-3_3-Nemotron-Super-49B-v1_5\",\n    messages=[\n        {\"role\": \"system\", \"content\": \"\"},\n        {\"role\": \"user\", \"content\": \"My bill is $100. What will be the amount for 18% tip?\"}\n    ],\n    tools=[\n        {\n            \"type\": \"function\",\n            \"function\": {\n                \"name\": \"calculate_tip\",\n                \"parameters\": {\n                    \"type\": \"object\",\n                    \"properties\": {\n                        \"bill_total\": {\n                            \"type\": \"integer\",\n                            \"description\": \"The total amount of the bill\"\n                        },\n                        \"tip_percentage\": {\n                            \"type\": \"integer\",\n                            \"description\": \"The percentage of tip to be applied\"\n                        }\n                    },\n                    \"required\": [\"bill_total\", \"tip_percentage\"]\n                }\n            }\n        },\n        {\n            \"type\": \"function\",\n            \"function\": {\n                \"name\": \"convert_currency\",\n                \"parameters\": {\n                    \"type\": \"object\",\n                    \"properties\": {\n                        \"amount\": {\n                            \"type\": \"integer\",\n                            \"description\": \"The amount to be converted\"\n                        },\n                        \"from_currency\": {\n                            \"type\": \"string\",\n                            \"description\": \"The currency code to convert from\"\n                        },\n                        \"to_currency\": {\n                            \"type\": \"string\",\n                            \"description\": \"The currency code to convert to\"\n                        }\n                    },\n                    \"required\": [\"from_currency\", \"amount\", \"to_currency\"]\n                }\n            }\n        }\n    ],\n    temperature=0.6,\n    top_p=0.95,\n    max_tokens=32768,\n    stream=False\n)\nprint(completion.choices[0].message.content)\n'''\n<think>\nOkay, let's see. The user has a bill of $100 and wants to know the amount for an 18% tip. Hmm, I need to calculate the tip based on the bill total and the percentage. The tools provided include calculate_tip, which takes bill_total and tip_percentage as parameters. So the bill_total here is 100, and the tip_percentage is 18. I should call the calculate_tip function with these values. Wait, do I need to check if the parameters are integers? The bill is $100, which is an integer, and 18% is also an integer. So that fits the function's requirements. I don't need to convert any currency here because the user is asking about a tip in the same currency. So the correct tool to use is calculate_tip with those parameters.\n</think>\n'''\nprint(completion.choices[0].message.tool_calls)\n'''\n[ChatCompletionMessageToolCall(id='chatcmpl-tool-e341c6954d2c48c2a0e9071c7bdefd8b', function=Function(arguments='{\"bill_total\": 100, \"tip_percentage\": 18}', name='calculate_tip'), type='function')]\n'''\n

Training, Testing, and Evaluation Datasets:

Training Dataset:

A large variety of training data was used for the knowledge distillation phase before the post-training pipeline, 3 of which included: FineWeb, Buzz-V1.2, and Dolma.

The data for the multi-stage post-training phases for improvements in Code, Math, and Reasoning is a compilation of SFT and RL data that supports improvements of math, code, general reasoning, and instruction following capabilities of the original Llama instruct model.

Prompts have been sourced from either public and open corpus or synthetically generated. Responses were synthetically generated by a variety of models, with some prompts containing responses for both reasoning on and off modes, to train the model to distinguish between two modes.

NVIDIA will be releasing the post-training dataset in the coming weeks.

Data Modality: Text

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

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

Properties (Quantity, Dataset Descriptions, Sensor(s)):

Quantity: The knowledge distillation phase used 40 billion tokens. The underlying Dolma dataset contains approximately 3 trillion tokens, and FineWeb contains over 15 trillion tokens.

Dataset Descriptions: The training data is a composite of large-scale web text, synthetically generated conversational data for coding and instruction-following, and a diverse corpus of text from academic, literary, and encyclopedic sources. The model supports a context length of 128,000 tokens.

Testing Dataset:

Data Collection Method by dataset: Hybrid: Human, Synthetic

Labeling Method by dataset: Hybrid: Human, Automated

Properties (Quantity, Dataset Descriptions, Sensor(s)):

Quantity: The benchmarks contain thousands of test items, including:

MMLU: ~15,900 multiple-choice questions.
GPQA: 448 difficult multiple-choice questions.
HumanEval: 164 programming problems.
GSM-8K: ~8,500 grade-school math problems.
MATH: 12,500 competition math problems.
IF-Eval: Over 400 instruction-following prompts.

Dataset Descriptions: The evaluation suite is diverse, covering 57 academic and professional subjects (MMLU), expert-level reasoning (GPQA), Python code generation (HumanEval), mathematical problem-solving (GSM-8K, MATH), and the ability to follow precise instructions (IF-Eval).

Evaluation Dataset:

We used the datasets listed below to evaluate Llama-3.3-Nemotron-Super-49B-v1.5.

Data Collection Method by dataset: Hybrid: Human. Synthetic

Labeling Method by dataset: Hybrid: Human, Synthetic, Automatic

Evaluation Results
\nWe evaluate the model using temperature=0.6, top_p=0.95, and 64k sequence length. We run the benchmarks up to 16 times and average the scores to be more accurate.

MATH500

\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Reasoning Mode	pass@1 (avg. over 4 runs)
Reasoning On	97.4

AIME 2024

\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Reasoning Mode	pass@1 (avg. over 16 runs)
Reasoning On	87.5

AIME 2025

\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Reasoning Mode	pass@1 (avg. over 16 runs)
Reasoning On	82.71

GPQA

\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Reasoning Mode	pass@1 (avg. over 4 runs)
Reasoning On	71.97

LiveCodeBench 24.10-25.02

\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Reasoning Mode	pass@1 (avg. over 4 runs)
Reasoning On	73.58

BFCL v3

\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Reasoning Mode	pass@1 (avg. over 2 runs)
Reasoning On	71.75

IFEval

\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Reasoning Mode	Strict:Instruction
Reasoning On	88.61

ArenaHard

\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Reasoning Mode	pass@1 (avg. over 1 runs)
Reasoning On	92.0

Humanity's Last Exam (Text-Only Subset)

\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Reasoning Mode	pass@1 (avg. over 1 runs)
Reasoning On	7.64

MMLU Pro (CoT)

\n\n\n\n\n\n\n\n\n\n\n\n\n\n

tr:nth-child(odd)]:bg-surface-sunken\">

Reasoning Mode	pass@1 (avg. over 1 runs)
Reasoning On	79.53

All evaluations were done using the NeMo-Skills repository.

Inference:

Acceleration Engine:

vLLM, TensorRT

Test Hardware:

2x NVIDIA H100-80GB
2x NVIDIA A100-80GB GPUs
B200 SXM
H200 SXM
H100 SXM
A100 SXM 80GB
A100 SXM 40GB
L40S PCIe
A10G
H100 NVL
H200 NVL
GH200 96GB
GB200
RTX 5090
RTX 4090
RTX 6000 Ada

Ethical Considerations:

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

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

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

You are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.

Citation

@misc{bercovich2025llamanemotronefficientreasoningmodels,\n      title={Llama-Nemotron: Efficient Reasoning Models}, \n      author={Akhiad Bercovich and Itay Levy and Izik Golan and Mohammad Dabbah and Ran El-Yaniv and Omri Puny and Ido Galil and Zach Moshe and Tomer Ronen and Najeeb Nabwani and Ido Shahaf and Oren Tropp and Ehud Karpas and Ran Zilberstein and Jiaqi Zeng and Soumye Singhal and Alexander Bukharin and Yian Zhang and Tugrul Konuk and Gerald Shen and Ameya Sunil Mahabaleshwarkar and Bilal Kartal and Yoshi Suhara and Olivier Delalleau and Zijia Chen and Zhilin Wang and David Mosallanezhad and Adi Renduchintala and Haifeng Qian and Dima Rekesh and Fei Jia and Somshubra Majumdar and Vahid Noroozi and Wasi Uddin Ahmad and Sean Narenthiran and Aleksander Ficek and Mehrzad Samadi and Jocelyn Huang and Siddhartha Jain and Igor Gitman and Ivan Moshkov and Wei Du and Shubham Toshniwal and George Armstrong and Branislav Kisacanin and Matvei Novikov and Daria Gitman and Evelina Bakhturina and Jane Polak Scowcroft and John Kamalu and Dan Su and Kezhi Kong and Markus Kliegl and Rabeeh Karimi and Ying Lin and Sanjeev Satheesh and Jupinder Parmar and Pritam Gundecha and Brandon Norick and Joseph Jennings and Shrimai Prabhumoye and Syeda Nahida Akter and Mostofa Patwary and Abhinav Khattar and Deepak Narayanan and Roger Waleffe and Jimmy Zhang and Bor-Yiing Su and Guyue Huang and Terry Kong and Parth Chadha and Sahil Jain and Christine Harvey and Elad Segal and Jining Huang and Sergey Kashirsky and Robert McQueen and Izzy Putterman and George Lam and Arun Venkatesan and Sherry Wu and Vinh Nguyen and Manoj Kilaru and Andrew Wang and Anna Warno and Abhilash Somasamudramath and Sandip Bhaskar and Maka Dong and Nave Assaf and Shahar Mor and Omer Ullman Argov and Scot Junkin and Oleksandr Romanenko and Pedro Larroy and Monika Katariya and Marco Rovinelli and Viji Balas and Nicholas Edelman and Anahita Bhiwandiwalla and Muthu Subramaniam and Smita Ithape and Karthik Ramamoorthy and Yuting Wu and Suguna Varshini Velury and Omri Almog and Joyjit Daw and Denys Fridman and Erick Galinkin and Michael Evans and Katherine Luna and Leon Derczynski and Nikki Pope and Eileen Long and Seth Schneider and Guillermo Siman and Tomasz Grzegorzek and Pablo Ribalta and Monika Katariya and Joey Conway and Trisha Saar and Ann Guan and Krzysztof Pawelec and Shyamala Prayaga and Oleksii Kuchaiev and Boris Ginsburg and Oluwatobi Olabiyi and Kari Briski and Jonathan Cohen and Bryan Catanzaro and Jonah Alben and Yonatan Geifman and Eric Chung and Chris Alexiuk},\n      year={2025},\n      eprint={2505.00949},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL},\n      url={https://arxiv.org/abs/2505.00949}, \n}\n

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(hf-checkpoint-bf16-jetart)","nim/nvidia/llama-3.3-nemotron-super-49b-v1.5","key","nim/nvidia/models/llama-3.3-nemotron-super-49b-v1.5","resourceType","models","resourceId","llama-3.3-nemotron-super-49b-v1.5","shortDescription","Llama-3.3-Nemotron-Super-49B-v1.5 is a significantly upgraded version of Llama-3.3-Nemotron-Super-49B-v1 and is a large language model (LLM) which is a derivative of Meta Llama-3.3-70B-Instruct","## **Llama-3.3-Nemotron-Super-49B-v1.5 Overview**\n\n## **Description:**\n\n**Llama-3.3-Nemotron-Super-49B-v1.5** is a significantly upgraded version of Llama-3.3-Nemotron-Super-49B-v1 and is a large language model (LLM) which is a derivative of Meta Llama-3.3-70B-Instruct (AKA the reference model). It is a reasoning model that is post trained for reasoning, human chat preferences, and agentic tasks, such as Retrieval-Augmented Generation (RAG) and tool calling. The model supports a context length of 128K tokens.\n\nLlama-3.3-Nemotron-Super-49B-v1.5 is a model which offers a great tradeoff between model accuracy and efficiency. Efficiency (throughput) directly translates to savings. Using a novel Neural Architecture Search (NAS) approach, we greatly reduce the model’s memory footprint, enabling larger workloads, as well as fitting the model on a single GPU at high workloads (H200). This NAS approach enables the selection of a desired point in the accuracy-efficiency tradeoff. For more information on the NAS approach, please refer to [this paper](https://arxiv.org/abs/2411.19146).\n\nThe model underwent a multi-phase post-training process to enhance both its reasoning and non-reasoning capabilities. This includes a supervised fine-tuning stage for Math, Code, Science, and Tool Calling. Additionally, the model went through multiple stages of Reinforcement Learning (RL) including Reward-aware Preference Optimization (RPO) for chat, Reinforcement Learning with Verifiable Rewards (RLVR) for reasoning, and iterative Direct Preference Optimization (DPO) for Tool Calling capability enhancements. The final checkpoint was achieved after merging several RL and DPO checkpoints.\n\nThis model is part of the Llama Nemotron Collection. You can find the other model(s) in this family here:\n\n* [Llama-3.1-Nemotron-Nano-4B-v1.1](https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1) \n* [Llama-3.1-Nemotron-Ultra-253B-v1](https://huggingface.co/nvidia/Llama-3_1-Nemotron-Ultra-253B-v1)\n\nThis model is ready for commercial use.\n\n## **License/Terms of Use**\n\n**GOVERNING TERMS:** The NIM container is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and the [Product-Specific Terms for NVIDIA AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/); and the use of this model is governed by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).\n\n**Additional Information:** [Llama 3.3 Community License Agreement](https://www.llama.com/llama3_3/license/). Built with Llama.\n\n## **Get Help**\n\n### Enterprise Support\nGet access to knowledge base articles and support cases or [submit a ticket](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/).\n**You are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.**\n\n## **Deployment Geography:**\n\nGlobal\n\n## **Use Case:**\n\nLlama-3.3-Nemotron-Super-49B-v1.5 is a general purpose reasoning and chat model intended to be used in English and coding languages. Other non-English languages (German, French, Italian, Portuguese, Hindi, Spanish, and Thai) are also supported. Developers designing AI Agent systems, chatbots, RAG systems, and other AI-powered applications. Also suitable for typical instruction-following tasks.\n\n## **Release Date:**\n\n* Hugging Face 7/25/2025 via [Llama-3\\_3-Nemotron-Super-49B-v1\\_5](https://huggingface.co/nvidia/Llama-3_3-Nemotron-Super-49B-v1_5) \n* Build.NVIDIA.com 7/25/2025 [Llama-3\\_3-Nemotron-Super-49B-v1\\_5](https://build.nvidia.com/nvidia/llama-3_3-nemotron-super-49b-v1_5)\n\n## **References**\n\n* [\\[2505.00949\\] Llama-Nemotron: Efficient Reasoning Models](https://arxiv.org/abs/2505.00949) \n* [\\[2502.00203\\] Reward-aware Preference Optimization: A Unified Mathematical Framework for Model Alignment](https://arxiv.org/abs/2502.00203) \n* [\\[2411.19146\\]Puzzle: Distillation-Based NAS for Inference-Optimized LLMs](https://arxiv.org/abs/2411.19146)\n\n## **Model Architecture**\n\n**Architecture Type:** Dense decoder-only Transformer model\n\n**Network Architecture:** Llama 3.3 70B Instruct, customized through Neural Architecture Search (NAS)\n\nThe model is a derivative of Meta’s Llama-3.3-70B-Instruct, using Neural Architecture Search (NAS). The NAS algorithm results in non-standard and non-repetitive blocks. This includes the following:\n\nSkip attention: In some blocks, the attention is skipped entirely, or replaced with a single linear layer. Variable FFN: The expansion/compression ratio in the FFN layer is different between blocks.\n\nWe utilize a block-wise distillation of the reference model, where for each block we create multiple variants providing different tradeoffs of quality vs. computational complexity, discussed in more depth below. We then search over the blocks to create a model which meets the required throughput and memory (optimized for a single H100-80GB GPU) while minimizing the quality degradation. The model then undergoes knowledge distillation (KD), with a focus on English single and multi-turn chat use cases. The KD step included 40 billion tokens consisting of a mixture of 3 datasets \\- FineWeb, Buzz-V1.2, and Dolma.\n\n**Intended Use** \nLlama-3.3-Nemotron-Super-49B-v1.5 is a general purpose reasoning and chat model intended to be used in English and coding languages. Other non-English languages (German, French, Italian, Portuguese, Hindi, Spanish, and Thai) are also supported.\n\n## **Input**\n\n* **Input Type(s):** Text \n* **Input Format:** String \n* **Input Parameters:** One-Dimensional (1D) \n* **Other Properties Related to Input:** Context length up to 131,072 tokens\n\n## **Output**\n\n* **Output Type(s):** Text \n* **Output Format:** String \n* **Output Parameters:** One-Dimensional (1D) \n* **Other Properties Related to Output:** Context length up to 131,072 tokens\n\nOur AI models are designed and/or 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.\n\n## **Software Integration:**\n\n* **Runtime Engine:** vLLM, TensorRT \n* **Supported Hardware Microarchitecture Compatibility:** \n * NVIDIA Ampere \n * NVIDIA Hopper \n* **Preferred Operating System(s):** Linux\n\nThe 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.\n\n## **Model Version:**\n\nLlama-3.3-Nemotron-Super-49B-v1.5-1.12.0
\nLlama-3.3-Nemotron-Super-49B-v1.5-1.13.1
\nLlama-3.3-Nemotron-Super-49B-v1.5-1.14.0
\nLlama-3.3-Nemotron-Super-49B-v1.5-1.15.0
\nLlama-3.3-Nemotron-Super-49B-v1.5-1.15.5
\n\n## **Quick Start and Usage Recommendations:**\n\n* By default (empty system prompt), the model will respond in reasoning ON mode. Setting /no\\_think in the system prompt will enable reasoning OFF mode. \n* We recommend setting temperature to 0.6 and Top P to 0.95 for Reasoning ON mode. \n* We recommend using greedy decoding for Reasoning OFF mode.\n\nYou can try this model out through the preview API, using this link: [Llama-3\\_3-Nemotron-Super-49B-v1\\_5](https://build.nvidia.com/nvidia/llama-3_3-nemotron-super-49b-v1_5).\n\n**Use It with vLLM**\n\npip install vllm==0.9.2\n\nAn example on how to serve with vLLM:\n\n```\n$ python3 -m vllm.entrypoints.openai.api_server \\\n --model \"nvidia/Llama-3_3-Nemotron-Super-49B-v1_5\" \\\n --trust-remote-code \\\n --seed=1 \\\n --host=\"0.0.0.0\" \\\n --port=5000 \\\n --served-model-name \"Llama-3_3-Nemotron-Super-49B-v1_5\" \\\n --tensor-parallel-size=8 \\\n --max-model-len=65536 \\\n --gpu-memory-utilization 0.95 \\\n --enforce-eager\n```\n\n**Running a vLLM Server with Tool-call Support**\n\nTo enable tool calling usage with this model, we provide a tool parser in the repository. Here is an example on how to use it:\n\n```\n$ git clone https://huggingface.co/nvidia/Llama-3_3-Nemotron-Super-49B-v1_5\n\n$ conda create -n vllm python=3.12 -y\n$ conda activate vllm\n$ pip install vllm==0.9.2\n\n$ python3 -m vllm.entrypoints.openai.api_server \\\n --model Llama-3_3-Nemotron-Super-49B-v1_5 \\\n --trust-remote-code \\\n --seed=1 \\\n --host=\"0.0.0.0\" \\\n --port=5000 \\\n --served-model-name \"Llama-3_3-Nemotron-Super-49B-v1_5\" \\\n --tensor-parallel-size=8 \\\n --max-model-len=65536 \\\n --gpu-memory-utilization 0.95 \\\n --enforce-eager \\\n --enable-auto-tool-choice \\\n --tool-parser-plugin \"Llama-3_3-Nemotron-Super-49B-v1_5/llama_nemotron_toolcall_parser_no_streaming.py\" \\\n --tool-call-parser \"llama_nemotron_json\"\n```\n\nAfter launching a vLLM server, you can call the server with tool-call support using a Python script like below.\n\n```py\nfrom openai import OpenAI\nclient = OpenAI(\n base_url=\"http://0.0.0.0:5000/v1\",\n api_key=\"dummy\",\n)\ncompletion = client.chat.completions.create(\n model=\"Llama-3_3-Nemotron-Super-49B-v1_5\",\n messages=[\n {\"role\": \"system\", \"content\": \"\"},\n {\"role\": \"user\", \"content\": \"My bill is $100. What will be the amount for 18% tip?\"}\n ],\n tools=[\n {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"calculate_tip\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"bill_total\": {\n \"type\": \"integer\",\n \"description\": \"The total amount of the bill\"\n },\n \"tip_percentage\": {\n \"type\": \"integer\",\n \"description\": \"The percentage of tip to be applied\"\n }\n },\n \"required\": [\"bill_total\", \"tip_percentage\"]\n }\n }\n },\n {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"convert_currency\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"amount\": {\n \"type\": \"integer\",\n \"description\": \"The amount to be converted\"\n },\n \"from_currency\": {\n \"type\": \"string\",\n \"description\": \"The currency code to convert from\"\n },\n \"to_currency\": {\n \"type\": \"string\",\n \"description\": \"The currency code to convert to\"\n }\n },\n \"required\": [\"from_currency\", \"amount\", \"to_currency\"]\n }\n }\n }\n ],\n temperature=0.6,\n top_p=0.95,\n max_tokens=32768,\n stream=False\n)\nprint(completion.choices[0].message.content)\n'''\n\nOkay, let's see. The user has a bill of $100 and wants to know the amount for an 18% tip. Hmm, I need to calculate the tip based on the bill total and the percentage. The tools provided include calculate_tip, which takes bill_total and tip_percentage as parameters. So the bill_total here is 100, and the tip_percentage is 18. I should call the calculate_tip function with these values. Wait, do I need to check if the parameters are integers? The bill is $100, which is an integer, and 18% is also an integer. So that fits the function's requirements. I don't need to convert any currency here because the user is asking about a tip in the same currency. So the correct tool to use is calculate_tip with those parameters.\n\n'''\nprint(completion.choices[0].message.tool_calls)\n'''\n[ChatCompletionMessageToolCall(id='chatcmpl-tool-e341c6954d2c48c2a0e9071c7bdefd8b', function=Function(arguments='{\"bill_total\": 100, \"tip_percentage\": 18}', name='calculate_tip'), type='function')]\n'''\n```\n\n## **Training, Testing, and Evaluation Datasets:**\n\n### **Training Dataset:**\n\nA large variety of training data was used for the knowledge distillation phase before the post-training pipeline, 3 of which included: FineWeb, Buzz-V1.2, and Dolma.\n\nThe data for the multi-stage post-training phases for improvements in Code, Math, and Reasoning is a compilation of SFT and RL data that supports improvements of math, code, general reasoning, and instruction following capabilities of the original Llama instruct model.\n\nPrompts have been sourced from either public and open corpus or synthetically generated. Responses were synthetically generated by a variety of models, with some prompts containing responses for both reasoning on and off modes, to train the model to distinguish between two modes.\n\nNVIDIA will be releasing the post-training dataset in the coming weeks.\n\n**Data Modality:** Text \n\n**Data Collection Method by dataset:** Hybrid: Automated, Human, Synthetic\n\n**Labeling Method by dataset:** Hybrid: Automated, Human, Synthetic\n\n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** \n\nQuantity: The knowledge distillation phase used 40 billion tokens. The underlying Dolma dataset contains approximately 3 trillion tokens, and FineWeb contains over 15 trillion tokens.\n\nDataset Descriptions: The training data is a composite of large-scale web text, synthetically generated conversational data for coding and instruction-following, and a diverse corpus of text from academic, literary, and encyclopedic sources. The model supports a context length of 128,000 tokens.\n\n### \n\n### **Testing Dataset:**\n\n**Data Collection Method by dataset:** Hybrid: Human, Synthetic\n\n**Labeling Method by dataset:** Hybrid: Human, Automated\n\n**Properties (Quantity, Dataset Descriptions, Sensor(s)):**\n\nQuantity: The benchmarks contain thousands of test items, including:\n\n* MMLU: \\~15,900 multiple-choice questions. \n* GPQA: 448 difficult multiple-choice questions. \n* HumanEval: 164 programming problems. \n* GSM-8K: \\~8,500 grade-school math problems. \n* MATH: 12,500 competition math problems. \n* IF-Eval: Over 400 instruction-following prompts.\n\nDataset Descriptions: The evaluation suite is diverse, covering 57 academic and professional subjects (MMLU), expert-level reasoning (GPQA), Python code generation (HumanEval), mathematical problem-solving (GSM-8K, MATH), and the ability to follow precise instructions (IF-Eval).\n\n### **Evaluation Dataset:**\n\nWe used the datasets listed below to evaluate Llama-3.3-Nemotron-Super-49B-v1.5.\n\n**Data Collection Method by dataset:** Hybrid: Human. Synthetic\n\n**Labeling Method by dataset:** Hybrid: Human, Synthetic, Automatic\n\n**Evaluation Results** \nWe evaluate the model using temperature=0.6, top\\_p=0.95, and 64k sequence length. We run the benchmarks up to 16 times and average the scores to be more accurate.\n\n### MATH500\n\n| Reasoning Mode | pass@1 (avg. over 4 runs) |\n| ----- | ----- |\n| Reasoning On | 97.4 |\n\n### AIME 2024\n\n| Reasoning Mode | pass@1 (avg. over 16 runs) |\n| ----- | ----- |\n| Reasoning On | 87.5 |\n\n### AIME 2025\n\n| Reasoning Mode | pass@1 (avg. over 16 runs) |\n| ----- | ----- |\n| Reasoning On | 82.71 |\n\n### GPQA\n\n| Reasoning Mode | pass@1 (avg. over 4 runs) |\n| ----- | ----- |\n| Reasoning On | 71.97 |\n\n### LiveCodeBench 24.10-25.02\n\n| Reasoning Mode | pass@1 (avg. over 4 runs) |\n| ----- | ----- |\n| Reasoning On | 73.58 |\n\n### BFCL v3\n\n| Reasoning Mode | pass@1 (avg. over 2 runs) |\n| ----- | ----- |\n| Reasoning On | 71.75 |\n\n### IFEval\n\n| Reasoning Mode | Strict:Instruction |\n| ----- | ----- |\n| Reasoning On | 88.61 |\n\n### ArenaHard\n\n| Reasoning Mode | pass@1 (avg. over 1 runs) |\n| ----- | ----- |\n| Reasoning On | 92.0 |\n\n### Humanity's Last Exam (Text-Only Subset)\n\n| Reasoning Mode | pass@1 (avg. over 1 runs) |\n| ----- | ----- |\n| Reasoning On | 7.64 |\n\n### MMLU Pro (CoT)\n\n| Reasoning Mode | pass@1 (avg. over 1 runs) |\n| ----- | ----- |\n| Reasoning On | 79.53 |\n\nAll evaluations were done using the [NeMo-Skills](https://github.com/NVIDIA/NeMo-Skills) repository.\n\n## **Inference:**\n\n**Acceleration Engine:**\n\n* vLLM, TensorRT\n\n**Test Hardware:**\n\n* 2x NVIDIA H100-80GB \n* 2x NVIDIA A100-80GB GPUs\n* B200 SXM \n* H200 SXM \n* H100 SXM \n* A100 SXM 80GB \n* A100 SXM 40GB \n* L40S PCIe \n* A10G \n* H100 NVL \n* H200 NVL \n* GH200 96GB \n* GB200 \n* RTX 5090 \n* RTX 4090 \n* RTX 6000 Ada \n\n## **Ethical Considerations:**\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.\n\nFor more detailed information on ethical considerations for this model, please see the Model Card++ [Bias](http://./bias.md), [Explainability](http://./explainability.md), [Safety & Security](http://./safety.md), and [Privacy](http://./privacy.md) Subcards.\n\nPlease report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\nYou are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.\n\n## **Citation**\n\n```py\n@misc{bercovich2025llamanemotronefficientreasoningmodels,\n title={Llama-Nemotron: Efficient Reasoning Models}, \n author={Akhiad Bercovich and Itay Levy and Izik Golan and Mohammad Dabbah and Ran El-Yaniv and Omri Puny and Ido Galil and Zach Moshe and Tomer Ronen and Najeeb Nabwani and Ido Shahaf and Oren Tropp and Ehud Karpas and Ran Zilberstein and Jiaqi Zeng and Soumye Singhal and Alexander Bukharin and Yian Zhang and Tugrul Konuk and Gerald Shen and Ameya Sunil Mahabaleshwarkar and Bilal Kartal and Yoshi Suhara and Olivier Delalleau and Zijia Chen and Zhilin Wang and David Mosallanezhad and Adi Renduchintala and Haifeng Qian and Dima Rekesh and Fei Jia and Somshubra Majumdar and Vahid Noroozi and Wasi Uddin Ahmad and Sean Narenthiran and Aleksander Ficek and Mehrzad Samadi and Jocelyn Huang and Siddhartha Jain and Igor Gitman and Ivan Moshkov and Wei Du and Shubham Toshniwal and George Armstrong and Branislav Kisacanin and Matvei Novikov and Daria Gitman and Evelina Bakhturina and Jane Polak Scowcroft and John Kamalu and Dan Su and Kezhi Kong and Markus Kliegl and Rabeeh Karimi and Ying Lin and Sanjeev Satheesh and Jupinder Parmar and Pritam Gundecha and Brandon Norick and Joseph Jennings and Shrimai Prabhumoye and Syeda Nahida Akter and Mostofa Patwary and Abhinav Khattar and Deepak Narayanan and Roger Waleffe and Jimmy Zhang and Bor-Yiing Su and Guyue Huang and Terry Kong and Parth Chadha and Sahil Jain and Christine Harvey and Elad Segal and Jining Huang and Sergey Kashirsky and Robert McQueen and Izzy Putterman and George Lam and Arun Venkatesan and Sherry Wu and Vinh Nguyen and Manoj Kilaru and Andrew Wang and Anna Warno and Abhilash Somasamudramath and Sandip Bhaskar and Maka Dong and Nave Assaf and Shahar Mor and Omer Ullman Argov and Scot Junkin and Oleksandr Romanenko and Pedro Larroy and Monika Katariya and Marco Rovinelli and Viji Balas and Nicholas Edelman and Anahita Bhiwandiwalla and Muthu Subramaniam and Smita Ithape and Karthik Ramamoorthy and Yuting Wu and Suguna Varshini Velury and Omri Almog and Joyjit Daw and Denys Fridman and Erick Galinkin and Michael Evans and Katherine Luna and Leon Derczynski and Nikki Pope and Eileen Long and Seth Schneider and Guillermo Siman and Tomasz Grzegorzek and Pablo Ribalta and Monika Katariya and Joey Conway and Trisha Saar and Ann Guan and Krzysztof Pawelec and Shyamala Prayaga and Oleksii Kuchaiev and Boris Ginsburg and Oluwatobi Olabiyi and Kari Briski and Jonathan Cohen and Bryan Catanzaro and Jonah Alben and Yonatan Geifman and Eric Chung and Chris Alexiuk},\n year={2025},\n eprint={2505.00949},\n archivePrefix={arXiv},\n primaryClass={cs.CL},\n url={https://arxiv.org/abs/2505.00949}, \n}\n```","displayName","Llama-3.3-Nemotron-Super-49B-v1.5",["D",1770142083556],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nim/nvidia/models/llama-3.3-nemotron-super-49b-v1.5","rawImageUrl","https://catalog.ngc.nvidia.com/_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fllama-3_3-nemotron-49b-instruct.jpg&w=640&q=90","canGuestDownload","latestVersionSizeInBytes",99752316499,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","### **Bias**\n\n|Field:|Response:|\n|:---:|:---:|\n|Participation considerations from adversely impacted groups (protected classes) in model design and testing:|None|\n|Measures taken to mitigate against unwanted bias:|None|","explainability","### **Explainability**\n\n| Field: | Response: |\n| :---- | :---- |\n| Intended Application(s) & Domain(s): | Text generation, reasoning, summarization, and question answering. |\n| Model Type: | Text-to-text transformer |\n| Intended Users: | This model is intended for developers, researchers, and customers building/utilizing LLMs, while balancing accuracy and efficiency. |\n| Output: | Text String(s) |\n| Describe how the model works: | Generates text by predicting the next word or token based on the context provided in the input sequence using multiple self-attention layers. |\n| Technical Limitations: | The model was trained on data that contains toxic language, unsafe content, and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts. The model may generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. The model demonstrates weakness to alignment-breaking attacks. Users are advised to deploy language model guardrails alongside this model to prevent potentially harmful outputs. The Model may generate answers that are inaccurate, omit key information, or include irrelevant or redundant text. |\n| Verified to have met prescribed quality standards? | Yes |\n| Performance Metrics: | Accuracy, Throughput, and user-side throughput |\n| Potential Known Risks: | The model was optimized explicitly for instruction following and as such is susceptible to prompt injection and jailbreaking in various forms as a result of its instruction tuning. The model should be paired with additional rails or system filtering to limit exposure to instructions from malicious sources -- either directly or indirectly by retrieval (e.g. via visiting a website) -- as they may yield outputs that can lead to harmful, system-level outcomes up to and including remote code execution in agentic systems when effective security controls including guardrails are not in place.The model was trained on data that contains toxic language and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts. The model may generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. Model output should be appropriately escaped before viewing or other processing.|\n| End User License Agreement: | Your use of this model is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and the [Product-Specific Terms for NVIDIA AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/); and the use of this model is governed by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). Additional Information: [Llama 3.3 Community License Agreement](https://www.llama.com/llama3_3/license/). Built with Llama. |","privacy","### Privacy\n\n|Field:|Response:|\n|:---:|:---:|\n|Generatable or Reverse engineerable personally-identifiable information?|None|\n|Was consent obtained for any personal data used?|None Known|\n|Personal data used to create this model?|None Known|\n|How often is dataset reviewed?|Before Release|\n|Is there provenance for all datasets used in training?|Yes|\n|Does data labeling (annotation, metadata) comply with privacy laws?|Yes|\n|Applicable NVIDIA Privacy Policy|https://www.nvidia.com/en-us/about-nvidia/privacy-policy/|","safetyAndSecurity","### Safety & Security\n\n|Field:|Response:|\n|:---:|:---:|\n|Model Application(s):|Chat, Instruction Following, Chatbot Development, Code Generation, Reasoning|\n|Describe life critical application (if present):|None Known (please see referenced Known Risks in the Explainability subcard).|\n|Use Case Restrictions:|Your use of this model is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and the [Product-Specific Terms for NVIDIA AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/); and the use of this model is governed by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). Additional Information: [Llama 3.3 Community License Agreement](https://www.llama.com/llama3_3/license/). 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