Nvidia is taking a different approach in the AI world by focusing on open infrastructure for building AI agents. Instead of offering just ready-made services, the company is providing tools and models that developers can customize and own. This move aims to support AI systems that can work across large contexts and over long periods, which requires a new kind of open and flexible setup.

Introducing Nemotron 3 Family of Models

Nvidia has unveiled the Nemotron 3 series, a set of open models designed to help developers create their own AI agents and applications. These models come in three sizes—Nano, Super, and Ultra—each tailored for different tasks and levels of complexity. The idea is to let developers skip building foundational models from scratch and instead focus on customizing and deploying AI solutions quickly.

One of the key features of Nemotron 3 is its hybrid latent mixture-of-experts (MoE) architecture, which enhances performance and efficiency. Nvidia is also sharing much of its training data and reinforcement learning libraries openly, making it easier for anyone to build and improve AI agents. The Nano version is now available on platforms like Hugging Face and will soon be accessible on cloud services like AWS, Google Cloud, and others.

Different Models for Different Needs

The smallest model, Nemotron 3 Nano, is designed for quick, targeted tasks such as information retrieval, content summarization, or acting as an AI assistant. It has 30 billion parameters and can activate 3 billion at a time, allowing for fast responses. Its large context window of one million tokens means it can handle multi-step tasks and remember a lot of information, making it ideal for lightweight but efficient applications.

The larger models, Super and Ultra, are meant for more complex reasoning and multi-agent collaboration. The Super model has about 100 billion parameters, with up to 10 billion active per token, making it suitable for deep research or strategic planning. The Ultra is the biggest, with 500 billion parameters and 50 billion active per token, aimed at tackling the most demanding AI applications. These larger models are expected to become available in the first half of 2026.

Nvidia’s approach is different from traditional AI providers. Rather than offering ready-to-use APIs, Nvidia positions itself as the backbone infrastructure that enterprises can build on. This allows companies to own their AI systems and tailor them to specific needs, giving more control and customization options.

Strategic Positioning and Industry Impact

Experts view Nvidia’s move as a shift from competing directly with API-based AI services toward enabling enterprises to develop their own AI agents on open platforms. This infrastructure-focused approach provides a foundation for long-term growth, especially as AI applications become more complex.

Industry analysts compare the Nemotron models to a “meal kit” for developers—offering ready-made components they can modify to suit their needs. Nvidia’s hybrid architecture aims to deliver high performance while maintaining efficiency, making it a promising option for organizations wanting to own and control their AI ecosystems.

Overall, Nvidia’s open model strategy could accelerate innovation in AI by empowering more players to build specialized, scalable agents. As these models become more accessible, they could shape the next generation of AI tools and applications across various industries.

Inspired by

• https://www.infoworld.com/article/4106756/nvidia-bets-on-open-infrastructure-for-the-agentic-ai-era-with-nemotron-3.html

Sources

• nvidia.com
• computerworld.com
• networkworld.com
• nvidia.com
• huggingface.co