Nvidia is making significant strides in the open-source AI landscape with its Nemotron 3 family of language models, designed to power the next generation of AI applications. This move signifies a shift towards more transparent and accessible AI development, offering developers the tools and resources needed to build advanced agentic systems.

A New Era of Open-Source AI

Nvidia's Nemotron 3 is a family of open-source language models tailored for reasoning and multi-agent tasks. The models are available in Nano, Super, and Ultra sizes, catering to a range of applications from targeted tasks to complex workflows. A key feature of Nemotron 3 is its hybrid mixture-of-experts (MoE) architecture, which allows for high throughput and a large 1-million-token context window.

Unlike traditional open-weight releases, Nvidia has open-sourced the entire development stack for Nemotron 3. This includes training data, recipes, and reinforcement learning environments, providing developers with unprecedented transparency and control. According to Nvidia CEO Jensen Huang, this open innovation is crucial for advancing AI progress, giving developers the efficiency and transparency needed to build agentic systems at scale.

Nemotron 3 Models: Nano, Super, and Ultra

The Nemotron 3 family includes three variants designed for different tasks and environments:

• Nano: The smallest model with 30 billion parameters, Nano is designed for speed and efficiency in targeted tasks. Nvidia claims it is four times faster than its predecessor while maintaining similar performance. Nemotron 3 Nano delivers state-of-the-art accuracy in a cost-efficient package. It also offers higher throughput than other open-source models of comparable size and performs well on long-context reasoning benchmarks.
• Super: This model has 100 billion parameters with 10 billion active parameters and is intended for heavier applications, especially those involving multiple AI agents collaborating. It is optimized for collaborative agents and high-volume workloads. Super is expected to be released in early 2026.
• Ultra: The largest model, Ultra, boasts 500 billion parameters with 50 billion active parameters. It is designed to serve as a central brain for complex workflows such as research, analysis, and strategic decision-making. The release of Ultra is anticipated later in 2026.

Technical Innovations and Performance

Nemotron 3 Nano utilizes a hybrid Mamba-Transformer Mixture-of-Experts (MoE) architecture. This design choice enables developers to create reliable agents that are more scalable, accurate, and capable of handling specialized sub-tasks in multi-step workflows. Nemotron 3 Nano also retains the classic Nemotron Thinking ON/OFF modes and Thinking Budget controls, allowing developers to fine-tune the model's "thinking" process for each task.

Nvidia has also released NeMo Gym, an open-source library for building and scaling reinforcement learning (RL) environments. NeMo Gym provides ready-to-use RL environments and the ability to build custom environments with verifiable reward logic.

Open Source Commitment

Nvidia's commitment to open source extends beyond just releasing the model weights. The company has also made publicly available the data used to train its AI models, along with the training recipe and framework. This level of transparency is significant, as it allows developers to understand and reproduce the results, fostering further innovation and collaboration.

To further support development, Nvidia has released the NeMo Gym and NeMo RL open-source libraries, which provide the training environments and post-training foundation for Nemotron models. Additionally, NeMo Evaluator is available to validate model safety and performance. These tools and datasets are accessible on GitHub and Hugging Face.

Strategic Implications

Nvidia's move into open-source AI model development comes at a time when some of its largest customers, such as OpenAI, Google, Meta, and Anthropic, are exploring developing their own chips to reduce reliance on Nvidia's technology. By offering a powerful and accessible open-source alternative, Nvidia aims to maintain its influence in the AI ecosystem and foster broader adoption of its hardware and software platforms. Nvidia is clearly signaling that inference is a critical area for future growth and innovation in the AI landscape.