This container houses the **Llama-3.3-Nemotron-Super-49B-v1.5 PB 25h2**, which 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 PB 25h2 Overview
Description
This container houses the Llama-3.3-Nemotron-Super-49B-v1.5 PB 25h2, which 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).
Llama-3.3-Nemotron-Super-49B-v1.5 PB 25h2 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 PB 25h2 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:
The container components are 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.
You are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.
Deployment Geography
Global
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
Llama-3.3-Nemotron-Super-49B-v1.5 PB 25h2
Llama-3.3-Nemotron-Super-49B-v1.5 Container includes the following model:
| Model Name & Link | Use Case | How to Pull the Model |
|---|---|---|
| Llama-3.3-Nemotron-Super-49B-v1.5 PB 25h2 | This is a large language model (LLM) that is a derivative of the Meta Llama-3.3-70B-Instruct. It is a reasoning model that is post-trained for reasoning, human chat preferences, and agentic tasks, such as RAG and tool calling. | Automatic |
Deployment Details:
Visit the NIM Container LLM page for release documentation, deployment guides, and more.
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.
Container Version(s):
Llama-3.3-Nemotron-Super-49B-v1.5 PB 25h2
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