Nvidia Nemotron 3 Nano: Everything You Need to Know

Nvidia’s newest open model release is less about chasing frontier benchmarks — and more about fixing what breaks when AI leaves the demo stage.

On Monday, the company announced Nemotron 3, a new family of open large language models designed specifically for agentic AI systems: applications where multiple AI agents collaborate, reason over long horizons, and route work between specialized models.

The release includes three model sizes (Nano, Super, and Ultra) alongside open datasets, reinforcement learning environments, and tooling meant to help developers build reliable, production-grade AI agents at scale.

The headline claim is efficiency. But the deeper story is architectural: Nvidia is betting that the future of AI is not a single massive model, but systems of models, and that open, inspectable foundations are critical if enterprises are going to trust those systems with real work.

Why Nemotron 3 exists

As organizations move from single-turn chatbots toward autonomous or semi-autonomous AI agents, several problems quickly emerge. Multi-agent systems increase token usage dramatically. They require long context windows, repeated self-reflection, and coordination across multiple tools and models. Latency compounds. Inference costs spike. Context drifts.

At the same time, enterprises want more transparency — not less. Models that automate business-critical workflows need to be auditable, adaptable, and aligned with domain-specific knowledge and regulatory requirements.

Nemotron 3 is Nvidia’s answer to those constraints. Rather than competing directly with proprietary frontier models, the company is positioning Nemotron as a high-efficiency, open reasoning layer that can work alongside them. In practice, that means routing routine or specialized tasks to Nemotron models while reserving expensive proprietary models for moments where their unique strengths matter most.

The Nemotron 3 lineup

The Nemotron 3 family includes three models optimized for different roles within an agentic system:

• Nemotron 3 Nano: A 30B-parameter model with just 3B active parameters, designed for highly efficient, targeted tasks.
• Nemotron 3 Super: Approximately 100B parameters with 10B active, tuned for multi-agent collaboration and low-latency reasoning.
• Nemotron 3 Ultra: Roughly 500B parameters with 50B active, intended as a large-scale reasoning engine for complex planning and research workflows.

Only Nano is available today (get it here). Super and Ultra are expected in the first half of 2026.

What unites all three is a hybrid latent mixture-of-experts (MoE) architecture, which allows the model to activate only the parameters it needs for a given task. This design dramatically improves throughput and reduces inference costs — an increasingly critical factor as agents “think longer” and generate more reasoning tokens.

A breakthrough in token efficiency

According to Nvidia, Nemotron 3 Nano delivers up to 4× higher token throughput than Nemotron 2 Nano, while reducing reasoning-token generation by as much as 60%. It also supports a 1-million-token context window, enabling agents to reason over entire codebases, long documents, or extensive system logs without losing coherence.

Independent benchmarks from Artificial Analysis reinforce these claims.

In an intelligence vs. output speed comparison, Nemotron 3 Nano sits firmly in the most attractive quadrant — combining high reasoning capability with exceptional token throughput. Among open models of similar size, it delivers the highest output tokens per second, significantly outperforming peers from Meta, Mistral, DeepSeek, and others.

A separate output speed leaderboard shows Nemotron 3 Nano generating roughly 377 tokens per second, ahead of OpenAI’s gpt-oss-20B (high) and far beyond most open alternatives. For multi-agent systems that may be running dozens — or hundreds — of concurrent agents, this difference compounds quickly into real infrastructure savings.

Openness without the trade-offs

Efficiency alone is not enough. Historically, developers choosing open models have been forced to compromise — either sacrificing intelligence for speed, or accepting opaque training pipelines that make enterprise deployment risky.

Artificial Analysis’ Openness Index vs. Intelligence Index highlights why Nemotron 3 Nano stands out. The model ranks among the most open while also landing in the upper tier for intelligence, a combination that remains rare even as open-source AI matures.

That openness goes beyond model weights. Nvidia is releasing:

• Three trillion tokens of new pretraining, post-training, and reinforcement learning data
• A large-scale agentic safety dataset based on real-world telemetry
• NeMo Gym and NeMo RL, open reinforcement learning libraries and environments
• NeMo Evaluator, for validating model performance and safety

All of it is available on GitHub and Hugging Face. The goal is not just transparency, but reproducibility — giving teams confidence that they understand what the model was trained on and how to adapt it safely.

Reinforcement learning at scale

One of the less visible but more consequential changes in Nemotron 3 is how it was trained.

Earlier generations leaned heavily on supervised fine-tuning with limited reinforcement learning. Nemotron 3 shifts that balance. Nvidia applied concurrent multi-environment reinforcement learning at scale, training models across a diverse set of environments to improve instruction-following, reasoning efficiency, and long-horizon decision-making.

The result, according to internal and third-party evaluations, is a substantial jump in reasoning performance relative to earlier Nemotron models — without a proportional increase in inference cost. This matters for agents that must reflect, revise plans, and coordinate with other agents under real-time constraints.

Built for systems of models

A key theme running through the Nemotron 3 announcement is that no single model is enough.

In real-world deployments, enterprises increasingly combine proprietary frontier models with fast, specialized open models. Nemotron is designed to slot directly into those systems. Developers can route tasks dynamically — using Nemotron for summarization, retrieval, code debugging, or domain-specific reasoning, while escalating only the hardest problems to more expensive models.

This approach is already showing up in production. Companies like Perplexity use agent routers to direct workloads to the most appropriate model for each task. Others, including ServiceNow, CrowdStrike, and Siemens, are integrating Nemotron models to power domain-specific AI workflows in IT operations, cybersecurity, and industrial systems.

Sovereign AI and localization

Nemotron 3 also plays into Nvidia’s broader push around sovereign AI — the idea that countries and organizations should be able to build AI systems aligned with their own languages, data, and values.

By releasing open datasets and training tools, Nvidia enables governments and enterprises to fine-tune Nemotron models on local data, translate reasoning datasets into underrepresented languages, and deploy systems that meet regional regulatory requirements.

This approach has already been used to support national-language models and localized AI initiatives, particularly in Europe and Asia, where data sovereignty is a growing concern.

Infrastructure matters

Efficiency gains in Nemotron 3 are tightly coupled to Nvidia’s hardware roadmap.

Nemotron 3 Super and Ultra are trained using NVFP4, a 4-bit training format optimized for NVIDIA’s Blackwell architecture. This significantly reduces memory requirements and accelerates training without the accuracy penalties traditionally associated with lower-precision formats.

For inference, Nemotron 3 Nano can run on relatively modest hardware, including GPUs like the L40S, making it accessible to enterprises that want strong reasoning performance without massive infrastructure investments. At the same time, it scales cleanly across NVIDIA-accelerated data centers for organizations running large agent fleets.

Availability and ecosystem support

Nemotron 3 Nano is available today through:

• Hugging Face
• Inference providers such as Baseten, Deepinfra, Fireworks, FriendliAI, OpenRouter, and Together AI
• Enterprise platforms, including Couchbase, DataRobot, H2O.ai, JFrog, Lambda, and UiPath
• AWS via Amazon Bedrock (serverless), with broader cloud support coming soon

It is also offered as an Nvidia NIM microservice, enabling secure, portable deployment across Nvidia infrastructure.

The broader Nemotron ecosystem is supported by popular inference frameworks like llama.cpp, vLLM, and SGLang, with reinforcement learning integrations underway through partners such as Prime Intellect and Unsloth.

What Nemotron 3 signals

Nemotron 3 is not Nvidia’s attempt to outbuild frontier models from OpenAI or others. Instead, it reflects a strategic belief: that AI’s next phase is architectural, not just parametric.

As agents become more autonomous and workloads grow more complex, efficiency, transparency, and composability matter as much as raw intelligence. Nemotron 3 shows what happens when those constraints are treated as first-class design goals.

Independent benchmarks suggest Nvidia is delivering on that promise — especially at the smaller end of the model spectrum, where token efficiency can make or break real deployments.

If Nemotron 3 Super and Ultra extend those gains to larger-scale reasoning in 2026, Nvidia may have positioned open models not as a compromise — but as the backbone of the agentic AI stack.

Editor’s note: This content originally ran in the newsletter of our sister publication, The Neuron. To read more from The Neuron, sign up for its newsletter here.

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