Open Secret: How NVIDIA Nemotron Models, Datasets and Techniques Fuel AI Development

Open technologies - made available to developers and businesses to adopt, modify and innovate with - have been part of every major technology shift, from the birth of the internet to the early days of cloud computing. AI should follow the same path.

That's why the NVIDIA Nemotron family of multimodal AI models, datasets and techniques is openly available. Accessible for research and commercial use, from local PCs to enterprise-scale systems, Nemotron provides an open foundation for building AI applications. It's available for developers to get started on GitHub, Hugging Face and OpenRouter.

Nemotron enables developers, startups and enterprises of any size to use models trained with transparent, open-source training data. It offers tools to accelerate every phase of development, from customization to deployment.

The technology's transparency means that its adopters can understand how their models work and trust the results they provide.

Nemotron's capabilities for generalized intelligence and agentic AI reasoning - and its adaptability to specialized AI use cases - have led to its widespread use today by AI innovators and leaders across industries such as manufacturing, healthcare, education and retail.

What's NVIDIA Nemotron? NVIDIA Nemotron is a collection of open-source AI technologies designed for efficient AI development at every stage. It includes:

Multimodal models: State-of-the-art AI models, delivered as open checkpoints, that excel at graduate-level scientific reasoning, advanced math, coding, instruction following, tool calling and visual reasoning.

Pretraining, post-training and multimodal datasets: Collections of carefully chosen text, image and video data that teach AI models skills including language, math and problem-solving.

Numerical precision algorithms and recipes: Advanced precision techniques that make AI faster and cheaper to run while keeping answers accurate.

System software for scaling training efficiently on GPU clusters: Optimized software and frameworks that unlock accelerating training and inference on NVIDIA GPUs at massive scale for the largest models.

Post-training methodologies and software: Fine-tuning steps that make AI smarter, safer and better at specific jobs.

Nemotron is part of NVIDIA's wider efforts to provide open, transparent and adaptable AI platforms for developers, industry leaders and AI infrastructure builders across the private and public sectors.

What's the Difference Between Generalized Intelligence and Specialized Intelligence? NVIDIA built Nemotron to raise the bar for generalized intelligence capabilities - including AI reasoning - while also accelerating specialization, helping businesses worldwide adopt AI for industry-specific challenges.

Generalized intelligence refers to models trained on vast public datasets to perform a wide range of tasks. It serves as the engine needed for broad problem-solving and reasoning tasks. Specialized intelligence learns the unique language, processes and priorities of an industry or organization, giving AI models the ability to adapt to specific real-world applications.

To deliver AI at scale across every industry, both are essential.

That's why Nemotron provides pretrained foundation models optimized for a range of computing platforms, as well as tools like NVIDIA NeMo and NVIDIA Dynamo to transform generalized AI models into custom models tailored for specialized intelligence.

How Are Developers and Enterprises Using Nemotron? NVIDIA is building Nemotron to accelerate the work of developers everywhere - and to inform the design of future AI systems.

From researchers to startups and global enterprises, developers need flexible, trustworthy AI. Nemotron offers the tools to build, customize and integrate AI for virtually any field.

CrowdStrike is integrating its Charlotte AI AgentWorks no-code platform for security teams with Nemotron, helping to power and secure the agentic ecosystem. This collaboration redefines security operations by enabling analysts to build and deploy specialized AI agents at scale, leveraging trusted, enterprise-grade security with Nemotron models.

DataRobot is using Nemotron as the open foundation for training, customizing and managing AI agents at scale in the Agent Workforce Platform co-developed with NVIDIA- a solution for building, operating and governing a fully functional AI agent workforce, in on-premises, hybrid and multi-cloud environments.

ServiceNow introduced the Apriel Nemotron 15B model earlier this year in partnership with NVIDIA. Post-trained with data from both companies, the model is purpose-built for real-time workflow execution and delivers advanced reasoning in a smaller size, making it faster, more efficient, and cost-effective.

UK-LLM, a sovereign AI initiative led by University College London, used Nemotron open-source techniques and datasets to develop an AI reasoning model for English and Welsh.

NVIDIA also uses the insights gained from developing Nemotron to inform the design of its next-generation systems, including Grace Blackwell, Vera Rubin and Feynman. The latest innovations in AI models, including reduced precision, sparse arithmetic, new attention mechanisms and optimization algorithms, all shape GPU architectures.

For example, NVFP4, a new data format that uses just four bits per parameter during large language model (LLM) training, was discovered with Nemotron. This advancement - which dramatically reduces energy use - is influencing the design of future NVIDIA systems.

NVIDIA also improves Nemotron with open technologies built by the broader AI community.

Alibaba's Qwen open model has provided data augmentation that has improved Nemotron's pretraining and post-training datasets. The latest Qwen3-Next architecture pushed the frontier of long-context AI, the model leverages Gat

LINK:	https://blogs.nvidia.com/blog/nemotron-open-source-ai/...
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