In late 2025, NVIDIA unveiled Nemotron 3 Nano, which is a significant leap in open-source big language models (LLMs). It is designed to accommodate advanced AI systems that require an extensive understanding of the context as well as a large context. Nano introduces new innovations in performance, architecture, and flexibility, which are geared towards the future of agent-centric AI applications. This article explains what NVIDIA Nemotron 3 Nano is, how it functions and why it is essential and what it has to offer to developers and companies.
What is NVIDIA Nemotron 3 Nano?
Nemotron 3 Nano is part of the larger Nemotron 3 model family, which is a collection of open models specifically designed for efficient reasoning, long-context comprehension and high-throughput deployments in the real world of AI agents. The Nano version, which will be launched in December 2025, is the earliest model available of the family, with the larger Super models and Ultra models expected to be available in early 2026.
At its heart, Nemotron 3 Nano (also named Nemotron 3 Nano 30B-A3B) is a Mixture-of-Experts (MoE) mixed Mamba Transformer with around 31.6 billion variables, of which approximately 3-3.6 billion of them are active on each forward move. This strategy of sparse activation allows massive efficiency gains without sacrificing the quality of reasoning.
Hybrid MoE Architecture: Mamba Meets Transformer
NVIDIA Nemotron 3 Nano: Hybrid Mamba-Transformer Design
Nemotron 3 Nano’s architecture blends two powerful paradigms:
- Mamba (State-Space Models): Effectively handles very long sequences and linear scaling, making it suitable for tasks that require a long context.
- Transformer Attention Layers: Offer complete reasoning and context understanding for more complex inference.
- Mixture-of-Experts (MoE): Introduces expert routing that is sparse, where only a small fraction of the model parameters are used for each token, which lowers compute costs while maintaining capacity.
Combining the elements mentioned above, Nemotron 3 Nano achieves significantly higher processing efficiency when compared with conventional thick Transformers, specifically for tasks that require lengthy stretches of text or multiple-step thinking.
NVIDIA Nemotron 3 Nano: Sparse Activation for Efficiency
The MoE design is a model that is able to selectively activate a specific subset of experts per token. While the total number of parameters is huge (~31.6B), only 3.2-3.6B parameters are activated during inference. This activation of the sparse is a significant boost in the speed of inference, lowers the cost of inference, and allows Nemotron 3 Nano to deliver the same performance as much larger models, but with less computation cost.
1 Million Token Context: Scaling Beyond Traditional Limits
The most impressive characteristics of Nemotron 3 Nano are the support for up to 1 million tokens in context, a feature that is uncommon in many LLMs currently. This increased context window permits the model to take in information, process the context, and ensure coherence across huge inputs, for example:
- Long technical documents
- Multi-stage agent workflows
- Full technical specifications
- Complex multi-document reasoning pipelines
This expanded context is a great way to power the most advanced scenarios, such as long-duration memory, complex planning and multi-agent coordination. These are areas where traditional models using smaller context windows have difficulty.
Performance: Throughput and Accuracy
Throughput Leadership
Nemotron 3 Nano has significant improvements in performance in real-world conditions when compared with other similar-sized Open models
- Up to 3.3x higher inference throughput than comparable models like Qwen3-30B-A3B-Thinking and GPT-OSS-20B.
- Four times faster throughput compared to its predecessor, Nemotron 2 Nano.
These benefits are derived from the efficiency of the MoE design and the hybrid architecture, which decreases the amount of computation per token, but without compromising the level of analysis.
Competitive Accuracy
Despite its limited activation, Nemotron 3 Nano delivers higher or similar precision across a variety of benchmarking tests for reasoning. Internal evaluations demonstrate strong performance in:
- Mathematical and Logical Reasoning
- Coding and other technical tasks
- Long-form comprehension tests
- Agentic multi-step tool use
These results show that NVIDIA Nemotron 3 Nano is not only efficient, but it also retains the quality and depth you would expect from top-of-the-line LLMs.
NVIDIA Nemotron 3 Nano: Training and Openness
Massive Pretraining and Reinforcement Learning
Nemotron 3 Nano was trained on an extensive set of around 25 trillion tokens, which includes data derived from previous Nemotron generations and recently collected sources. After teaching the model, it underwent controlled fine-tuning, as well as large-scale reinforcement learning (RL) across a variety of environments to increase its capabilities for reasoning and interaction.
RL training aids the model to perform multi-step tasks more efficiently, enabling it to be equipped with a robust behaviour in situations that are modelled after real-world decision processes.
Open Source With Full Transparency
In keeping with NVIDIA’s policy of openness, NVIDIA has announced:
- Weights of models (base as well as post-trained checkpoints)
- Recipes for training and codes
- HTML0Large parts of the training information
The tools let developers fine-tune their applications, extend and implement Nemotron 3 Nano across platforms and applications, encouraging community-driven innovation and acceptance.
NVIDIA Nemotron 3 Nano: Ecosystem and Use Cases
Nemotron 3 Nano is positioned not as a stand-alone model, but rather as a key part of larger AI systems. Its capabilities allow it to be used for:
- Artificial aids require deep reasoning and memory
- Autonomous agents managing multi-step workflows
- Enterprise knowledge systems that have a significant document ingestion
- Tool-enabled AI workflows integrating external APIs
In NVIDIA’s broader AI ecosystem, it is able to integrate with other tools such as NeMo Gym and major deployment frameworks, which makes it suitable for both production and research usage cases.
Looking Ahead: Super and Ultra Models
Although Nemotron 3 Nano is available today, NVIDIA plans to release more powerful models of the Nemotron 3 series, Nemotron 3 Super and Nemotron 3 Ultra in 2026. These models are expected to include:
- Even higher reasoning capacity
- Enhanced multi-agent collaboration features
- LatentMoE for refined expert specialisation
- Continuous support for long-context reasoning using a pertimized throughput
Together, these models are designed to push the limits of free-source AI further than what the closed ecosystem giants provide.
Final Thoughts
Nemotron 3 Nano signals a significant shift in the way the high-performance models of language are constructed. Instead of solely relying on ever-growing complex architectures, NVIDIA has focused on more efficient use of compute, fewer activations, and long-context efficiency. The result is a system which delivers high reasoning performance, significantly higher performance, and the ability to deploy to real-world, agentic workflows. Its open-source release, which comes with training recipes and tools, can further increase its effectiveness through the possibility of experimentation, fine-tuning and the power of community-driven innovation.
As AI systems are increasingly requiring permanent memory, multi-step planning, and coordination across documents and tools, models such as Nemotron 3 Nano will play an important role. With bigger Nemotron 3 variants coming in the future, NVIDIA is positioning this family as an open option for creating large-scale, long-context, reasoning-first AI systems, setting the bar for what effective large-language models can accomplish.
Frequently Asked Questions
1. What is it that makes Nemotron 3 Nano different from the traditional Transformer models?
Nemotron 3 Nano uses a hybrid architecture that combines Mamba state space layers with Transformer Attention and the sparse Mixture of Experts layer, which allows efficient long-context processing and a high inference throughput.
2. What is the size of your context window?
The model is able to support as many as 1,000,000 tokens in context, significantly beyond the usual limits of modern LLMs.
3. Is Nemotron 3 Nano open source?
Yes. NVIDIA has made available the weights of the model, training code recipes, and a lot of the training information under an open license.
4. What is the best way to balance costs and performance?
By using the sparse MoE activation, only a small percentage of parameters are activated in a token. That dramatically enhances the efficiency of computation while reducing cost.
5. What kinds of tasks do you think Nemotron 3 Nano is appropriate for?
It’s perfect for agentic AI and long-document processing code tasks that require multi-step thinking and other applications that require a greater comprehension of context.
6. When will more powerful models from the Nemotron 3 family be available?
Nemotron 3 Super and Ultra are scheduled to be launched sometime in 2026 in the first or second quarter of 2026 with upgraded capabilities, along with additional functions.
Also Read –
Language Model Safety: Game-Theoretic AI Alignment
