The open-source LLM space has exploded with models competing across size, efficiency, and reasoning capability. But while frontier models dominate headlines with enormous parameter counts, a different category has quietly become essential for real-world deployment: small yet high-performance models optimized for edge devices, private on-prem systems, and cost-sensitive applications.

NVIDIA’s Nemotron family brings together open models, datasets, and tooling designed to make advanced agentic AI both powerful and accessible. Built for demanding tasks such as reasoning, coding, visual understanding, retrieval, and multi-step agent behavior, Nemotron models are fully open and integrated across the broader AI ecosystem, allowing them to run seamlessly from edge devices to large-scale cloud deployments. 

The newest addition to the family is NVIDIA’s Nemotron 3 Nano that aims to redefine what small models can do — offering surprising reasoning strength, high-quality instruction following, and exceptional efficiency across both cloud and edge deployments.

Where many lightweight models struggle with capability trade-offs, Nemotron 3 Nano takes a different path: it leverages architectural refinements, training strategies, and NVIDIA-optimized inference pipelines to deliver performance that punches far above its parameter class.

This article explores what makes Nemotron 3 Nano unique, why its architecture diverges from standard small LLMs, and where it shines in real workflows.

The Nemotron 3 Model Family

With the rise of collaborative multi-agent systems, companies face challenges such as context shifts, communication overhead, and increasing inference costs. Nemotron 3 addresses these needs with a transparent, scalable model lineup built specifically for long-context reasoning and efficient agent workflows.

The family consists of three models:

• Nemotron 3 Nano – A compact 30B-parameter model with 3B active parameters per token, ideal for efficient tasks like debugging, summarization, assistants, and information retrieval.
• Nemotron 3 Super – A mid-sized reasoning model of ~100B parameters (10B active) designed for multi-agent applications where many agents must collaborate with low latency.
• Nemotron 3 Ultra – A large 500B-parameter inference engine (50B active) built for the most complex reasoning and enterprise-scale AI systems.

Super and Ultra are trained using NVIDIA’s NVFP4 4-bit format on Blackwell hardware, reducing memory demands while preserving accuracy.

Early adopters — including Accenture, CrowdStrike, Oracle Cloud Infrastructure, Palantir, Perplexity, ServiceNow, Siemens, and Zoom — are already integrating Nemotron 3 into workflows across manufacturing, cybersecurity, software development, and enterprise automation. 

Nemotron 3 introduces a set of architectural and training improvements designed specifically for large-scale, real-world agentic systems: 

1. At its core is a hybrid Mamba-Transformer Mixture-of-Experts backbone, combining long-range sequence modeling with precise reasoning and highly efficient expert routing at inference time. 
2. This is complemented by multi-environment reinforcement learning, where the model is trained across realistic agent tasks such as tool use, planning, verification, and multi-step problem solving.
3. To support persistent, multi-document reasoning, Nemotron 3 implements a native 1-million-token context window, allowing agents to maintain long-horizon memory without relying on heavy chunking or retrieval heuristics. 
4. The entire development process is built on an open and transparent training pipeline, including datasets, weights, and detailed recipes that developers can inspect or reuse.

In the following sections, we will go into more detail on these key technologies for the new Nemotron 3 models and explain what makes them unique. 

The Core Architecture: A Purpose-Built, Efficient Transformer

While Nemotron 3 Nano is, broadly speaking, a transformer architecture, several design choices make it uniquely capable among sub-3B models.

1. Hybrid Mamba-Transformer MoE

Nemotron 3 combines three different AI technologies into one model, each contributing a specific strength. The first is Mamba, a type of layer that is very good at handling long pieces of text without using much memory. This allows the model to stay consistent even when it processes extremely large documents or long conversations. The second component is the Transformer, which is excellent at precise reasoning tasks. These layers help the model understand complex relationships in text, such as when solving math problems, writing or editing code, or breaking down multi-step instructions.

Nemotron 3 hybrid architecture, Source: https://developer-blogs.nvidia.com/wp-content/uploads/2025/12/image3-8-png.webp 

The third ingredient is Mixture-of-Experts (MoE). Instead of using the full model for every single word, MoE activates only the parts (“experts”) that are needed. This makes the model feel larger and smarter than its size would suggest, while still running very quickly. This approach is especially useful in multi-agent systems where many small AI agents work together—each performing tasks like planning steps, analyzing information, or calling external tools—because the model can respond fast without sacrificing intelligence.

2. Multi-environment reinforcement learning (RL) training

To make Nemotron 3 behave more like a real multi-step agent, NVIDIA trains the model further using reinforcement learning in NeMo Gym, an open-source environment designed for teaching AI systems how to perform sequences of actions. Instead of simply answering questions one by one, the model is tested on tasks that require several steps in the right order — for example, calling the correct tools, writing code that actually works, or creating detailed plans that meet specific requirements.

By learning from these step-by-step “trajectories,” the model becomes more dependable in complex workflows. It drifts less in its reasoning, stays focused across longer tasks, and handles structured operations more like a well-organized assistant rather than a single-turn chatbot.

Because NeMo Gym is fully open, developers can use the same environments to train their own models, customize tasks for their industry, or build entirely new learning scenarios. NVIDIA is also releasing the reinforcement learning datasets and environments used in Nemotron 3, giving teams everything they need to reproduce or extend this training process.

3. 1M token context length

Nemotron 3 can work with an extremely large amount of information at once — up to one million tokens. This means the model can keep entire codebases, long documents, lengthy conversations, or large sets of retrieved facts in its memory without needing to break them into smaller pieces. Instead of stitching together many small chunks, the model can see the full picture at once, which helps it stay accurate, consistent, and grounded in the original information.

This capability comes from Nemotron 3’s hybrid architecture. The Mamba layers allow the model to process very long sequences efficiently, while the Mixture-of-Experts design keeps the computational cost low enough to make such large contexts practical during real-time use.

For businesses working with retrieval systems, compliance reviews, multi-hour agent sessions, or large software repositories, this huge context window is a major advantage. It reduces fragmentation, preserves continuity, and enables far more reliable long-range reasoning compared to models limited to much smaller inputs.

What is Nemotron 3 Nano?

Nemotron 3 Nano is the newest and most efficient member of the Nemotron family, designed to deliver strong reasoning performance while remaining lightweight enough for fast, cost-effective deployment. Although the full model contains over 30 billion parameters, only a small fraction is active at any given moment thanks to its sparse Mixture-of-Experts design. This allows Nano to act like a much larger model in terms of capability while still behaving like a compact model in speed and resource usage.

Its architecture builds on earlier Nemotron versions with an upgraded hybrid layout that interleaves Mamba-2 layers and Transformer attention, now enhanced by MoE layers that replace traditional feed-forward components. This gives the model more flexibility and expressiveness without increasing inference costs. A learned routing mechanism selects only a handful of experts for each token, ensuring that compute is focused where it matters most.

The result is a small model with impressive reasoning strength, well-suited for agentic tasks, tool use, planning, code assistance, and general chat applications. Like the rest of the Nemotron 3 family, Nano supports extremely long context windows—up to one million tokens—making it capable of handling large documents, extended conversations, or complex multi-stage workflows within a single session.

Nemotron 3 Nano continues NVIDIA’s push toward open, efficient, and highly capable reasoning models, offering a practical entry point for developers who want strong performance without the overhead of a large-scale system.

Why You Should Start with Nemotron 3 Nano

Nemotron 3 Nano is the ideal entry point into the Nemotron ecosystem because it delivers the rare combination of high reasoning performance, exceptional efficiency, and full openness. Despite its compact active parameter count, the model achieves state-of-the-art accuracy and significantly higher throughput than many open models of comparable size. This makes it a powerful choice for anyone building real-time or high-volume applications that need both speed and intelligence.

https://artificialanalysis.ai/models/nvidia-nemotron-3-nano-30b-a3b?intelligence-comparison=intelligence-vs-output-speed&models=gpt-oss-20b-low%2Cllama-4-maverick%2Cgemini-2-5-flash-preview-09-2025-reasoning%2Cgemini-3-flash-reasoning%2Cgemini-3-pro%2Cclaude-opus-4-5-thinking%2Cmagistral-medium-2509%2Cmistral-large-3%2Cdeepseek-r1%2Cdeepseek-v3-2-reasoning%2Cgrok-4-1-fast-reasoning%2Cgrok-4%2Cminimax-m2%2Cnvidia-nemotron-3-nano-30b-a3b%2Cnvidia-nemotron-3-nano-30b-a3b-reasoning%2Cglm-4-6-reasoning%2Capriel-v1-6-15b-thinker%2Cqwen3-235b-a22b-instruct-2507-reasoning#intelligence-vs-output-speed

Its ability to handle a 1-million-token context window also means that even demanding long-context tasks—multi-document reasoning, extended agent workflows, deep codebase analysis—run smoothly without the typical fragmentation challenges smaller models face. Developers can further fine-tune the model’s behavior using Nemotron’s Thinking ON/OFF modes and adjustable thinking budgets, allowing them to control how deeply the model reasons depending on the task.

What makes Nemotron 3 Nano especially appealing is NVIDIA’s commitment to openness. Alongside the model weights, NVIDIA provides the complete training recipe, including supervised fine-tuning and reinforcement-learning steps, as well as most of the datasets used throughout development. With the introduction of NeMo Gym, developers can even explore the same reinforcement learning environments used to train the model or create their own tailored training setups. In short, everything needed to study, reproduce, or extend Nemotron 3 Nano is openly available.

You can download the model, use hosted inference endpoints, deploy it at scale, or even run it on edge devices like RTX AI PCs and DGX Spark systems. But for many teams, the first question is: where should I run it to get the best performance?

Why DeepInfra Is the Best Place to Host Nemotron 3 Nano

Once you’ve decided to build with Nemotron 3 Nano, the next step is choosing the right platform to run it. While the model can be deployed virtually anywhere, DeepInfra stands out as one of the most efficient, cost-effective, and developer-friendly hosting solutions available today.

DeepInfra’s infrastructure is specifically optimized for high-performance LLM inference. In the latest Artificial Analysis benchmarks, Nemotron 3 Nano running on DeepInfra demonstrates excellent throughput, low latency, and competitive pricing—a combination that is especially important for agentic workflows, real-time applications, and large-scale deployments.

Despite supporting an impressive 262k+ context window on DeepInfra, the model maintains strong performance metrics. A price of $0.06 per million input tokens and $0.24 per million output tokens makes it one of the most affordable high-quality models to run in production. The platform also delivers 174 median tokens per second, ensuring fast streaming generation. Even the time to the first token is notably low at 0.23 seconds, which helps interactive applications feel responsive rather than sluggish.

Beyond raw performance, DeepInfra offers a clean developer experience: a simple API, transparent pricing, stable uptime, and consistently optimized model deployments. For many teams, this removes the operational complexity of managing GPUs and lets them focus entirely on building their product.

Key Takeaways

• Nemotron 3 Nano sets a new standard for small LLMs, delivering strong reasoning and coding performance through its hybrid Mamba-Transformer + MoE architecture.
  
• The Nemotron 3 family (Nano, Super, Ultra) is built for modern multi-agent systems, offering scalable performance from lightweight tasks to complex enterprise reasoning.
  
• Key innovations include reinforcement learning with NeMo Gym, a 1M-token context window, and a fully open training pipeline, giving developers both power and transparency.
  
• Nemotron 3 Nano offers exceptional efficiency, behaving like a much larger model while remaining fast, cost-effective, and easy to deploy on edge or cloud.
  
• DeepInfra is an ideal hosting platform, delivering top-tier throughput, low latency, and highly affordable pricing for running Nemotron 3 Nano in production.

Further Reading

About the Nemotron Family: https://www.nvidia.com/en-us/ai-data-science/foundation-models/nemotron/ 

About Nemotron 3 and its Architecture:
https://developer.nvidia.com/blog/inside-nvidia-nemotron-3-techniques-tools-and-data-that-make-it-efficient-and-accurate/

HuggingFace Blogon Nemotron 3 Nano:

The full Nemotron 3 Nano Technical Report:
https://research.nvidia.com/labs/nemotron/files/NVIDIA-Nemotron-3-Nano-Technical-Report.pdf