US EditionNVIDIA Debuts Nemotron 3 Family of Open Models
NVIDIA's Nemotron 3 models deliver up to 4x higher token throughput and reduce inference costs by up to 60%, enabling scalable, specialized agentic AI development.
- NVIDIA announced the Nemotron 3 family in Santa Clara, including Nemotron 3 Nano with training datasets, NeMo Gym, and open libraries for agentic AI.
- Using a hybrid latent mixture‑of‑experts and Mamba‑Transformer design, NVIDIA aimed to boost scalability and provide developers transparent, efficient open models for agentic AI, Kari Briski said.
- Technically, Nemotron 3 adds a 1 million token context and NVFP4 training, with sizes including Nemotron 3 Nano, Super, and Ultra, offering up to 4x throughput and 60% fewer reasoning tokens.
- Early adopters including Accenture, CrowdStrike, and Siemens are integrating Nemotron 3 models, which are available today on Hugging Face and inference providers, with Amazon Bedrock support coming soon.
- The family is designed to scale multi‑agent systems and reduce inference costs, with NVIDIA positioning Nemotron 3 to support sovereign AI amid competitors like AWS Nova Forge.
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Nvidia debuts Nemotron 3 with hybrid MoE and Mamba-Transformer to drive efficient agentic AI
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NVIDIA debuts Nemotron 3 family of open models
NVIDIA announced the NVIDIA Nemotron 3 family of open models, data and libraries designed to power transparent, efficient and specialised agentic AI development across industries. The Nemotron 3 models — with Nano, Super and Ultra sizes — introduce a breakthrough hybrid latent mixture-of-experts (MoE) architecture that helps developers build and deploy reliable multi-agent systems at scale. As organisations shift from single-model chatbots to co…
NVIDIA Expands Its Role in AI with Nemotron 3 Open Model Family
What to knowNVIDIA has unveiled the Nemotron 3 lineup, including Nano, Super, and Ultra variants, aimed at enabling efficient, transparent development of agentic and multi-agent AI systems.Nemotron 3 Nano delivers up to four times higher throughput than earlier versions, supports a one-million-token context window, and is optimized for long-context and reasoning-intensive workloads.The release is accompanied by open datasets, reinforcement learn…
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