NVIDIA has introduced the Nemotron 3 family of open AI models, positioning it as the next iteration in its ongoing effort to support agent-based artificial intelligence systems. The Nemotron 3 lineup includes three variants—Nano, Super, and Ultra—designed to address different levels of computational demand while maintaining a focus on efficiency, transparency, and adaptability. Rather than targeting consumer-facing chatbots, the release is aimed squarely at developers building multi-agent AI systems for enterprise and industrial use cases.
At the core of Nemotron 3 is a hybrid latent mixture-of-experts architecture intended to reduce inference costs and improve throughput without requiring every parameter to be active at all times. This approach reflects a broader industry shift toward models that can scale across many collaborating agents while managing latency, context drift, and compute overhead. NVIDIA frames Nemotron 3 as a response to the growing complexity of agentic AI workflows, where multiple models coordinate to complete tasks such as software debugging, cybersecurity analysis, or long-horizon planning.
Nemotron 3 Nano, the smallest model in the family, is positioned as a cost-efficient option for high-volume tasks like summarization, information retrieval, and assistant-style workflows. NVIDIA reports that Nano delivers significantly higher token throughput than its predecessor and supports a one-million-token context window, allowing it to retain and reason over extended inputs. Independent benchmarking organization Artificial Analysis has ranked it favorably among open models of similar size, particularly in terms of efficiency.
The larger Nemotron 3 Super and Ultra models target more complex reasoning scenarios. Super is designed for low-latency multi-agent collaboration, while Ultra is positioned as a higher-capacity reasoning engine for research-heavy or strategic applications. Both models use a 4-bit NVFP4 training format optimized for NVIDIA’s Blackwell architecture, which reduces memory requirements and shortens training times without materially degrading accuracy. This allows larger models to be trained on existing infrastructure rather than requiring entirely new hardware investments.
Alongside the models, NVIDIA has released a substantial collection of open datasets and reinforcement learning tools. These include trillions of tokens spanning pretraining, post-training, and reinforcement learning data, as well as agent safety datasets intended to help teams evaluate real-world behavior. Open-source libraries such as NeMo Gym, NeMo RL, and NeMo Evaluator are intended to support customization, training, and validation of agentic systems.
Nemotron 3 Nano is already available through open platforms and inference providers, with broader enterprise and cloud support planned. The Super and Ultra models are expected to follow in the first half of 2026. Taken together, the Nemotron 3 release reflects NVIDIA’s strategy to balance open models with specialized tooling, giving developers more control over how agentic AI systems are built, deployed, and governed as they move beyond single-model interactions.
