• NVIDIA (NVDA) launches Nemotron 3 family, including Nano 3 and Nano 2 VL models, with immediate availability.
  • Nemotron 3 Super (49B parameters) and Ultra (253B parameters) expected in the first half of 2026, targeting high-accuracy multi-GPU applications.
  • The release boosts NVIDIA's ecosystem, with open models driving demand for its hardware amid AI infrastructure expansion.

NVIDIA has rolled out the Nemotron 3 family of open AI models, a move that underscores its aggressive push into the foundation model space beyond its core hardware business. The launch includes the Nemotron Nano 3 and Nano 2 VL models, which are available now, while the more powerful Nemotron 3 Super and Ultra versions are slated for release in the first half of 2026, according to company announcements. This development comes as NVIDIA's stock (NVDA) has surged over 150% in 2025, fueled by robust AI demand, with Q3 FY2026 revenue jumping 94% year-over-year to $35 billion.

The Nemotron 3 family builds on NVIDIA's earlier open models, such as Nemotron Nano and Cosmos, and includes specialized variants like Parse, Safety Guard, and RAG models. The Safety Guard model, for instance, covers 23 safety categories across 9 languages, a feature that developers have praised on forums for enhancing moderation capabilities. One anonymous source familiar with the matter noted that the reasoning benchmarks, including top scores on GPQA Diamond and AIME 2025, are sparking discussions about open AI accessibility versus proprietary risks. Efforts to reach NVIDIA for additional comments on the timeline were not immediately successful.

In the short term, the immediate availability of the Nano models is expected to drive adoption of agentic AI on edge devices and data centers, while the Super and Ultra versions in 2026 will target more complex, high-accuracy use cases requiring multiple GPUs. This aligns with global AI market trends, where open-source models like those distilled from Meta's Llama series are gaining traction for cost-efficient enterprise solutions. Analysts predict that NVIDIA could capture 40-50% of agentic AI workloads long-term, thanks to this ecosystem expansion. Meanwhile, related developments include NVIDIA's release of complementary open datasets for multimodal training on November 3, 2025, and parallel launches by competitors like xAI and Anthropic earlier this year.

Without these open models, NVIDIA might risk losing ground in the rapidly evolving AI software landscape, but the move positions it to leverage its hardware dominance. The company's shift from hardware to AI foundation models since 2023 appears to be paying off, with experts highlighting the potential for enhanced lock-in across its product suite. As the AI market projects toward $1 trillion by 2030, NVIDIA's latest offering could be a key driver in maintaining its leadership, especially as U.S. export controls on advanced chips to China indirectly spur innovation in open models domestically.