NVIDIA has announced the launch of its Nemotron 3 family of open models, data, and libraries, a significant move designed to fuel the development of transparent, efficient, and specialized agentic AI applications. The announcement, made from Dubai, introduces a new toolkit for developers building sophisticated multi-agent AI systems.
The Nemotron 3 family directly addresses the growing challenges developers face as they transition from single-model chatbots to more complex, collaborative AI systems, including high inference costs, communication overhead, and context drift.
“Open innovation is the foundation of AI progress,” said Jensen Huang, founder and CEO of NVIDIA. “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.”
A New Architecture for Multi-Agent Systems
At the core of the new models is a breakthrough hybrid latent mixture-of-experts (MoE) architecture. This design helps developers build and deploy reliable multi-agent systems that can handle complex workflows by routing tasks between different models to optimize for both intelligence and cost-efficiency, or “tokenomics.”
The open nature of Nemotron 3 allows startups and enterprises to iterate faster on AI agents, accelerating innovation from early prototypes to enterprise-grade deployments. Early adopters include major players like Accenture, Deloitte, Palantir, Perplexity, and ServiceNow.
“NVIDIA and ServiceNow have been shaping the future of AI for years, and the best is yet to come,” said Bill McDermott, chairman and CEO of ServiceNow. “Today, we’re taking a major step forward in empowering leaders across all industries to fast-track their agentic AI strategy. ServiceNow’s intelligent workflow automation combined with NVIDIA Nemotron 3 will continue to define the standard with unmatched efficiency, speed and accuracy.”
The Nemotron 3 Family Explained
The Nemotron 3 family includes three distinct model sizes, allowing developers to select the optimal model for their specific workload:
- Nemotron 3 Nano: A 30-billion-parameter model designed for highly efficient and targeted tasks. Available today, it delivers up to 4x higher token throughput compared to its predecessor and features a 1-million-token context window for improved accuracy over long, multistep tasks.
- Nemotron 3 Super: An approximately 100-billion-parameter model built for high-accuracy reasoning in multi-agent applications that require low latency.
- Nemotron 3 Ultra: A large reasoning engine with about 500 billion parameters, intended for the most complex AI workflows that demand deep research and strategic planning.
Nemotron 3 Super and Ultra, expected in the first half of 2026, will utilize NVIDIA’s 4-bit NVFP4 training format on the Blackwell architecture, reducing memory requirements and accelerating training without compromising accuracy.
Relevance for the MENA Tech Ecosystem
The launch of Nemotron 3 from Dubai underscores its significance for the MENA region’s rapidly advancing tech landscape. As governments and businesses across the Middle East invest heavily in becoming AI leaders, the need for sovereign AI capabilities—systems aligned with local data, regulations, and values—is paramount.
NVIDIA’s open and transparent models provide a powerful foundation for MENA-based startups and enterprises to build customized AI agents. This can accelerate innovation in key regional sectors such as FinTech, smart city development, logistics, and energy. By leveraging these open tools, local developers can create sophisticated AI teammates and automated workflows tailored to the unique economic and cultural context of the region, fostering a self-sufficient and competitive AI ecosystem.
Fostering an Open Ecosystem
Beyond the models, NVIDIA also released a suite of open-source tools and datasets to support the developer community. This includes three trillion tokens of new training datasets, the Nemotron Agentic Safety Dataset for evaluating complex systems, and the NeMo Gym and NeMo RL open-source libraries for training and post-training reinforcement learning. These resources are now available on GitHub and Hugging Face, lowering the barrier to entry for building highly capable, domain-specialized agents.
Source: Zawya
