Llama-3_3-Nemotron-Super-v1_5

Model Overview

Llama-3.3-Nemotron-Super-49B-v1.5-FP8 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 RAG and tool calling. The model supports a context length of 128K tokens.

Llama-3.3-Nemotron-Super-49B-v1.5-FP8 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:

• Llama-3.1-Nemotron-Nano-4B-v1.1
• Llama-3.1-Nemotron-Ultra-253B-v1

This model is ready for commercial use.

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License/Terms of Use

GOVERNING TERMS: Your use of this model is governed by the NVIDIA Open Model License. Additional Information: Llama 3.3 Community License Agreement. Built with Llama.

Model Developer: NVIDIA

Model Dates: Trained between November 2024 and July 2025

Data Freshness: The pretraining data has a cutoff of 2023 per Meta Llama 3.3 70B

Deployment Geography

Global

Use Case:

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

• [2505.00949] Llama-Nemotron: Efficient Reasoning Models
• [2502.00203] Reward-aware Preference Optimization: A Unified Mathematical Framework for Model Alignment
• [2411.19146]Puzzle: Distillation-Based NAS for Inference-Optimized LLMs

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

Llama-3.3-Nemotron-Super-49B-v1.5-FP8 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

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.

Model Version

1.5 (07/25/2025)

Software Integration

• Runtime Engine: Transformers

• Recommended Hardware Microarchitecture Compatibility:

  • NVIDIA Ampere
  • NVIDIA Hopper

• Preferred Operating System(s): Linux

Quick Start and Usage Recommendations:

1. By default (empty system prompt) the model will respond in reasoning ON mode. Setting /no_think in the system prompt will enable reasoning OF