NVIDIA Debuts Nemotron 3 Family of Open Models for Agentic AI Development
NVIDIA unveils Nemotron 3 open model family with Nano, Super and Ultra sizes featuring breakthrough hybrid mixture-of-experts architecture for building efficient multi-agent AI systems at scale.
Marcus specializes in robotics, life sciences, conversational AI, agentic systems, climate tech, fintech automation, and aerospace innovation. Expert in AI systems and automation
Executive Summary
NVIDIA has announced the Nemotron 3 family of open models, a groundbreaking collection of AI models designed to power transparent, efficient, and specialized agentic AI development across industries. The family includes three sizes—Nano, Super, and Ultra—featuring a breakthrough hybrid latent mixture-of-experts (MoE) architecture that enables developers to build and deploy reliable multi-agent systems at scale.
The Nemotron 3 Model Family
The Nemotron 3 family introduces a hybrid latent mixture-of-experts architecture that addresses critical challenges facing organizations shifting from single-model chatbots to collaborative multi-agent AI systems:
| Model | Total Parameters | Active Parameters | Primary Use Case |
|---|---|---|---|
| Nano | 30 billion | Up to 3 billion | Software debugging, content summarization, AI assistant workflows |
| Super | ~100 billion | Up to 10 billion | Multi-agent applications with low latency |
| Ultra | ~500 billion | Up to 50 billion | Complex AI applications requiring deep reasoning |
Nemotron 3 Nano: Available Now
The Nemotron 3 Nano is the most compute-cost-efficient model in the family, delivering:
- 4x higher token throughput compared to Nemotron 2 Nano
- Up to 60% reduction in reasoning-token generation
- 1-million-token context window for improved accuracy on long, multistep tasks
Artificial Analysis, an independent benchmarking organization, ranked Nemotron 3 Nano as the most open and efficient model among its size class with leading accuracy.
Enterprise Adoption and Industry Impact
Major enterprises are already integrating Nemotron 3 models across diverse industries:
| Company | Industry Focus |
|---|---|
| Accenture | Professional Services |
| ServiceNow | Enterprise Software |
| CrowdStrike | Cybersecurity |
| Cursor | Software Development |
| Perplexity | AI Search |
| Siemens | Manufacturing |
| Synopsys | Electronic Design |
| Cadence | Electronic Design |
| Palantir | Data Analytics |
| Zoom | Communications |
Executive Perspectives
Jensen Huang, founder and CEO of NVIDIA, stated: "Open innovation is the foundation of AI progress. With Nemotron, we're transforming advanced AI into an open platform that gives developers the transparency and efficiency they need to build agentic systems at scale."
Bill McDermott, chairman and CEO of ServiceNow, added: "ServiceNow's intelligent workflow automation combined with NVIDIA Nemotron 3 will continue to define the standard with unmatched efficiency, speed and accuracy."
Technical Specifications and Training
Nemotron 3 Super and Ultra utilize NVIDIA's ultraefficient 4-bit NVFP4 training format on the NVIDIA Blackwell architecture, offering:
- Significantly reduced memory requirements
- Faster training speeds
- No compromise in accuracy relative to higher-precision formats
Open Training Resources
NVIDIA has released comprehensive training resources:
- 3 trillion tokens of pretraining, post-training, and reinforcement learning datasets
- NeMo Gym: Open-source training environments
- NeMo RL: Post-training foundation libraries
- Nemotron Agentic Safety Dataset: Real-world telemetry for safety evaluation
Availability and Deployment Options
Nemotron 3 Nano is available today through:
| Platform Type | Providers |
|---|---|
| Model Hub | Hugging Face |
| Inference Services | Baseten, DeepInfra, Fireworks, Together AI, OpenRouter |
| Enterprise Platforms | Couchbase, DataRobot, H2O.ai, JFrog, Lambda, UiPath |
| Cloud Providers | AWS (Amazon Bedrock), Google Cloud, CoreWeave, Microsoft Foundry, Nebius |
The model is supported by leading AI frameworks including LM Studio, llama.cpp, SGLang, and vLLM.
Sovereign AI Initiative
Nemotron 3 supports NVIDIA's broader sovereign AI efforts, with organizations from Europe to South Korea adopting open, transparent models that allow them to build AI systems aligned with their own data, regulations, and values.
Future Roadmap
| Model | Expected Availability |
|---|---|
| Nemotron 3 Super | H1 2026 |
| Nemotron 3 Ultra | H1 2026 |
References
About the Author
Marcus Rodriguez
Robotics & AI Systems Editor
Marcus specializes in robotics, life sciences, conversational AI, agentic systems, climate tech, fintech automation, and aerospace innovation. Expert in AI systems and automation
Frequently Asked Questions
What is NVIDIA Nemotron 3?
Nemotron 3 is NVIDIA's new family of open AI models designed for building agentic AI applications. It includes three sizes—Nano (30B parameters), Super (~100B), and Ultra (~500B)—featuring a breakthrough hybrid mixture-of-experts architecture for efficient multi-agent systems.
What makes Nemotron 3 Nano efficient?
Nemotron 3 Nano delivers 4x higher token throughput than its predecessor, reduces reasoning-token generation by up to 60%, and features a 1-million-token context window. It activates only 3 billion of its 30 billion parameters per task for maximum efficiency.
When will Nemotron 3 Super and Ultra be available?
NVIDIA expects to release Nemotron 3 Super and Ultra in the first half of 2026. Currently, only Nemotron 3 Nano is available through Hugging Face and various inference providers.
Which companies are adopting Nemotron 3?
Early adopters include Accenture, ServiceNow, CrowdStrike, Cursor, Perplexity, Siemens, Synopsys, Cadence, Palantir, Zoom, Deloitte, EY, and Oracle Cloud Infrastructure across manufacturing, cybersecurity, software development, and communications industries.
Is Nemotron 3 truly open source?
Yes, NVIDIA is releasing the models, training datasets (3 trillion tokens), and reinforcement learning libraries as open resources. The NeMo Gym and NeMo RL libraries are available on GitHub, and datasets are on Hugging Face for transparent AI development.
