Introduction
The constant quest for increasingly powerful artificial intelligence has resulted in an interesting development in model structure. We see an interesting movement towards hybrid models that combine the best aspects of various structures to overcome current shortcomings. One of the biggest challenges has been striking a balance between a model's reasoning power, speed, and computational expense necessary to execute it. New AI model designed to provide advanced reasoning with greatly increased throughput, bringing advanced AI within reach for deployment on more readily available high-performance hardware. The new model is called Nemotron Nano 2.
What is Nemotron Nano 2?
Nemotron Nano 2 is a series of precise and effective combinations of reason based on Mamba -Transformers. It is an innovation by NVIDIA, a provider of artificial intelligence and accelerated computing that is worldwide. These are specially designed computers to enhance the speed of reasoning outputs. They achieve this with or even greater accuracy compared to other models as state of the art and of a comparable size. This combination of high velocity and high cognition is reason enough why they are especially fitted in a new age of AI applications.
Model Variants
The Nemotron Nano 2 line is represented by three different models, each designed to address slightly different requirements but all inheriting the core advantages of the architecture and capable of supporting a whopping 128K context length.
- NVIDIA-Nemotron-Nano-9B-v2: This is an aligned and pruned model with around 8.89 billion parameters. It's a general-purpose reasoning and chat model, best for AI agents and following instructions. The distinct feature is that it can create a "reasoning trace" before its final output. Its knowledge cutoff is September 2024.
- NVIDIA-Nemotron-Nano-9B-v2-Base: This is the base pruned model, also with approximately 8.89 billion parameters. It is a base model that can then be further fine-tuned for other purposes. Its freshness of data is up to May 1, 2025.
- NVIDIA-Nemotron-Nano-12B-v2-Base: This is the base model, the untrimmed version, with a size of around 12.31 billion parameters. This model was pre-trained on an immense 20 trillion tokens of data and has the same cutoff date of data freshness as May 1, 2025.
Key Features of Nemotron Nano 2
Nemotron Nano 2 is supplied with innovative features that have made it powerful and useful.
- Transformer Architecture: The Mamba-2 can significantly reduce the time the models required to process information, particularly, the long sequences of thinking traces with the help of replacing the majority of the traditional Self-attention layers with the Mamba-2.
- Reasoning Budget Control: This exclusive tool enables users to disable the number of thinking tokens which a model will use to come up with the final response. This assists in positioning the responses in a well formed format and not having too much preparatory paragraphs.
- Native Tool-Calling: The models have in-built tool-calling, so that they can interface with external tools and APIs to complete a broader set of tasks.
- Multilingual Capabilities: Nemotron Nano 2 can speak in multiple languages, such as English, Spanish, French, German, Japanese, and Italian, in other words, it is a multilingual model.
Real-World Applications and Capabilities
The architecture and operating characteristics of Nemotron Nano 2 allow a range of valuable, practical uses that need a fast response with profound thinking and constrained response.
- Optimized Real time Decision Support: Nemotron Nano 2 can be implemented on resource-intense applications in specific NVIDIA-accelerated computers because of the advanced compression algorithm. Its hybrid layout guarantees speed of reasoning. The Budget Control enables short and well-structured explanations. This is appropriate in processes such as computerized diagnosis, or real-time control applications where time-efficient, flying auditable reasoning is essential.
- Debugging of code and math support: Given its large knowledge-base of specialized math and code training data, like the 133 billion-token Nemotron-CC-Math-v1 training set, the model becomes a particularly effective debugging assistant. It is able to produce step-by-step explanation to complex STEM problems. The Budget Control Reasoning enables the AI to scale the depth of its explanations- providing a succinct overview of simpler steps and a deeper thought process of more complex concepts.
- Automatic Compliance Report and Audit Trail: In extremely regulated industries, creating easy to understand records is it. Nemotron Nano 2 is able to handle huge regulatory documents due to its 128K context window and as a result create compliance reports. The length of the resulting reasoning output is controlled by it and the audit trail resulting is therefore concise and meets strict formatting requirements which help to streamline the review process.
How does Nemotron Nano 2 Work?
One of the reasons why Nemotron Nano 2 records a great performance is that it comes with an innovative design. The new models make use of a hybrid Mamba-Transformer architecture, referred to as Nemotron-Hybrid, that replaces most of the computationally demanding self-attention layers with Mamba-2 layers instead. This improvement is mostly helpful in speeding up the inference process when one is exposed to long sequence of information as in complex reasoning tasks. Such is the case with the Nemotron-Nano-12B-v2-Base which consists of 62 layers or 28 Mamba-2 layers, and just 6 layers of self-attention.
source - https://research.nvidia.com/labs/adlr/files/NVIDIA-Nemotron-Nano-2-Technical-Report.pdf
A sophisticated compression strategy has been used so that this powerful model can be run on a single GPU (NVIDIA A10G). This entailed two core procedures: Pruning, which identified the less relevant model structures such as layers and hidden dimensions, and Distillation that retrained the pruned model through the remaining original model as a teacher to restore some portion of the initial model accuracy and re-strengthen the decision making.
Training and Alignment
The robust nature of the model is a direct response to a well tailored, but computationally expensive training session. Training with FP8 precision was a design trade-off between numerical accuracy and computational throughput, and it enabled training at a larger scale (20 trillion tokens) thereby enabling free scaling training. It is such efficiency that allowed Jim to develop the curriculum learning idea in which the model was presented with increasing better-quality data developing key strengths in a predictable order as opposed to a random presentation of data. The reliance on artificially generated data particularly to specific and multilingual tasks is a modern, data-informed effort to address the lack of quality human-labeled data and develop a truly adaptive foundation model basing on the ground up.
On top of this good foundation the alignment process that follows makes the model the tool which comes to refinement after a rough predictive tool into an advanced user friendly tool. Supervised Fine-Tuning (SFT) The first carefully idiosyncratic preparation with properly curated post-training datasets effectively teaches the model to learn and follow subtle directions in various fields. And yet it is a final crucial step in RLHF that allows honing the behavior of the model so that what it produces are not merely right but also helpful, safe, and do not go against any expectation of subtle rules of conversation with people. Such a two-stage fine-tuning process is necessary toward moving a model out of research application to a deployable product suitable to be used in the real world and to be deployed.
Performance Evaluation
Nemotron Nano 2 does not only claim efficiency, it achieves leading-edge performance on many industry-standard tests. The Nemotron-Nano-12B-v2-Base model has achieved especially good results in the field of mathematical reasoning. It scored an excellent 91.66 percent on the GSM8K CoT benchmarking and 83.54 percent at the MATH benchmarking offering it great results in resolving complicated mathematical tasks. Such scores reflect that the model can learn and operate very high mathematical concepts.
source - https://research.nvidia.com/labs/adlr/files/NVIDIA-Nemotron-Nano-2-Technical-Report.pdf
Other areas where the models perform well is in the management of long-contexts information. The 12B Base and 9B Base models scored 84.74% and 82.22%, respectively, on the Ruler-128K, which tests a model on how well they retain and are able to retrieve information over long sequences of text. It is a key feature that applications requiring work with large files, conversation histories, or reports will have up to 6x higher inference throughput in generation-intensive workloads.
source - https://research.nvidia.com/labs/adlr/files/NVIDIA-Nemotron-Nano-2-Technical-Report.pdf
In addition to these particular tests, Nemotron Nano 2 has passed numerous different tests as well. The 9B Base model has special strength on multilingual math problems. The models also obtain good scores on code generation tasks such as HumanEval+ and MBPP+. Moreover, in the working mode as the 9B-v2 reasoner, the model displayed a good performance on such benchmarks, as AIME25 (72.1%) or GPQA (64.0%), among open small models.
How to Access and Use Nemotron Nano 2?
In order to access and use Nemotron Nano 2, it needs to be downloaded first. The Nemotron Nano 2 is extensively available to the developer and researcher community by NVIDIA. All their models are published under the NVIDIA Open Model License (allows for commercial use) and the majority of their training data is made openly available on Hugging Face. They are also optimized on NVIDIA GPU accelerated systems such as the A10G and the A100 series and are made to run under Linux. It fits into the most-used runtime engines, such as Hugging Face Transformers and TRT-LLM or vLLM. Attention: we advise those users who use vLLM to add the flag --mamba_ssm_cache_dtype float32 to ensure high quality and avoid deterioration of performance.
Limitations
Although the Nemotron Nano 2 models are highly sophisticated in design with great features, there are significant limitations nonetheless. The main limitation is the hardware efficiency, as the 12.31 billion parameters model successfully deployed on a single NVIDIA A10G GPU is achievable only after a major tick with the approach to model compression through pruning and distillation. This demonstrates that the resource requirements of the uncompressed base model are much high. In addition to that, the models are highly optimized to run within NVIDIA GPU accelerated systems, which reduces the probability of usage with other hardware platforms. Lastly, their knowledge is not exhaustive; their factual resources are limited by the training data and have an expiration limit of September 2024 in the case of the 9B-v2 and May 1, 2025 in the case of base model.
Conclusion
NVIDIA Nemotron Nano 2 is an innovative Mamba-Transformer hybrid architecture that overturns the age-old trade-off between reasoning power, speed and cost. By providing state-of-the-art performance with many times greater throughput, and offering the ability to use a single GPU via judicious compression, it is able to deploy advanced AI in a practical, affordable way. It is an emerging template out to implement capable and economical reasoning models in the real-life applications.
Source
Tech blog: https://research.nvidia.com/labs/adlr/NVIDIA-Nemotron-Nano-2/
Research paper: https://research.nvidia.com/labs/adlr/files/NVIDIA-Nemotron-Nano-2-Technical-Report.pdf
Nano-12B-v2-Base : https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-12B-v2-Base
Nano-9B-v2 : https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2
Nano-9B-v2-Base : https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2-Base
Disclaimer - This article is intended purely for informational purposes. It is not sponsored or endorsed by any company or organization, nor does it serve as an advertisement or promotion for any product or service. All information presented is based on publicly available resources and is subject to change. Readers are encouraged to conduct their own research and due diligence.
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