Llama-3.3-Nemotron-Super-49B-v1 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 tasks, such as RAG and tool calling. The model supports a context length of 128K tokens.
Llama-3.3-Nemotron-Super-49B-v1 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, Reasoning, and Tool Calling as well as multiple reinforcement learning (RL) stages using REINFORCE (RLOO) and Online Reward-aware Preference Optimization (RPO) algorithms for both chat and instruction-following. The final model checkpoint is obtained after merging the final SFT and Online RPO checkpoints. For more details on how the model was trained, please see this blog.
Training Process
This model is part of the Llama Nemotron Collection. You can find the other model(s) in this family here:
Model Dates: Trained between November 2024 and February 2025
Data Freshness: The pretraining data has a cutoff of 2023 per Meta Llama 3.3 70B
Use Case:
Developers designing AI Agent systems, chatbots, RAG systems, and other AI-powered applications. Also suitable for typical instruction-following tasks.
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
Llama-3.3-Nemotron-Super-49B-v1 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: Text
Input Format: String
Input Parameters: One-Dimensional (1D)
Other Properties Related to Input: Context length up to 131,072 tokens
Output
Output Type: Text
Output Format: String
Output Parameters: One-Dimensional (1D)
Other Properties Related to Output: Context length up to 131,072 tokens
Reasoning mode (ON/OFF) is controlled via the system prompt, which must be set as shown in the example below. All instructions should be contained within the user prompt
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
We have provided a list of prompts to use for evaluation for each benchmark where a specific template is required
See the snippet below for usage with Hugging Face Transformers library. Reasoning mode (ON/OFF) is controlled via system prompt. Please see the example below
We recommend using the transformers package with version 4.48.3.
Example of reasoning on:
A large variety of training data was used for the knowledge distillation phase before 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.
In conjunction with this model release, NVIDIA has released 30M samples of post-training data, as public and permissive. Please see Llama-Nemotron-Postraining-Dataset-v1.
Distribution of the domains is as follows:
Category
Value
math
19,840,970
code
9,612,677
science
708,920
instruction following
56,339
chat
39,792
safety
31,426
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.
Data Collection for Training Datasets:
Hybrid: Automated, Human, Synthetic
Data Labeling for Training Datasets:
Hybrid: Automated, Human, Synthetic
Evaluation Datasets
We used the datasets listed below to evaluate Llama-3.3-Nemotron-Super-49B-v1.
Data Collection for Evaluation Datasets:
Hybrid: Human/Synthetic
Data Labeling for Evaluation Datasets:
Hybrid: Human/Synthetic/Automatic
Evaluation Results
These results contain both “Reasoning On”, and “Reasoning Off”. We recommend using temperature=0.6, top_p=0.95 for “Reasoning On” mode, and greedy decoding for “Reasoning Off” mode. All evaluations are done with 32k sequence length. We run the benchmarks up to 16 times and average the scores to be more accurate.
NOTE: Where applicable, a Prompt Template will be provided. While completing benchmarks, please ensure that you are parsing for the correct output format as per the provided prompt in order to reproduce the benchmarks seen below.
Arena-Hard
Reasoning Mode
Score
Reasoning Off
88.3
MATH500
Reasoning Mode
pass@1
Reasoning Off
74.0
Reasoning On
96.6
User Prompt Template:
"Below is a math question. I want you to reason through the steps and then give a final answer. Your final answer should be in \boxed{}.\nQuestion: {question}"
AIME25
Reasoning Mode
pass@1
Reasoning Off
13.33
Reasoning On
58.4
User Prompt Template:
"Below is a math question. I want you to reason through the steps and then give a final answer. Your final answer should be in \boxed{}.\nQuestion: {question}"
GPQA
Reasoning Mode
pass@1
Reasoning Off
50
Reasoning On
66.67
User Prompt Template:
"What is the correct answer to this question: {question}\nChoices:\nA. {option_A}\nB. {option_B}\nC. {option_C}\nD. {option_D}\nLet's think step by step, and put the final answer (should be a single letter A, B, C, or D) into a \boxed{}"
IFEval
Reasoning Mode
Strict:Instruction
Reasoning Off
89.21
BFCL V2 Live
Reasoning Mode
Score
Reasoning Off
73.7
User Prompt Template:
You are an expert in composing functions. You are given a question and a set of possible functions.
Based on the question, you will need to make one or more function/tool calls to achieve the purpose.
If none of the function can be used, point it out. If the given question lacks the parameters required by the function,
also point it out. You should only return the function call in tools call sections.
If you decide to invoke any of the function(s), you MUST put it in the format of <TOOLCALL>[func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]</TOOLCALL>
You SHOULD NOT include any other text in the response.
Here is a list of functions in JSON format that you can invoke.
<AVAILABLE_TOOLS>{functions}</AVAILABLE_TOOLS>
{user_prompt}
MBPP 0-shot
Reasoning Mode
pass@1
Reasoning Off
84.9
Reasoning On
91.3
User Prompt Template:
You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.
@@ Instruction
Here is the given problem and test examples:
{prompt}
Please use the python programming language to solve this problem.
Please make sure that your code includes the functions from the test samples and that the input and output formats of these functions match the test samples.
Please return all completed codes in one code block.
This code block should be in the following format:
```python
# Your codes here
```
MT-Bench
Reasoning Mode
Score
Reasoning Off
9.17
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.
