엔비디아, Nemotron 3 오픈 모델 공개 에이전트 AI 처리량 4배 향상
무슨 발표인가
- Nemotron 3 (Nano/Super/Ultra) 오픈 모델 시리즈 공개
- Nemotron 2 Nano 대비 4배 높은 처리량 달성
- 다중 에이전트 시스템 구축용 학습 데이터·강화학습 라이브러리 제공
원문 (영어)
News Summary: The Nemotron 3 family of open models — in Nano, Super and Ultra sizes — introduces the most efficient family of open models with leading accuracy for building agentic AI applications. Nemotron 3 Nano delivers 4x higher throughput than Nemotron 2 Nano and delivers the most tokens per second for multi-agent systems at scale through a breakthrough hybrid mixture-of-experts architecture.
Nemotron achieves superior accuracy from advanced reinforcement learning techniques with concurrent multi-environment post-training at scale. NVIDIA is the first to release a collection of state-of-the-art open models, training datasets and reinforcement learning environments and libraries for building highly accurate, efficient, specialized AI agents.
NVIDIA today announced the NVIDIA Nemotron 3 family of open models, data and libraries designed to power transparent, efficient and specialized 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 organizations shift from single-model chatbots to collaborative multi-agent AI systems, developers face mounting challenges, including communication overhead, context drift and high inference costs. In addition, developers require transparency to trust the models that will automate their complex workflows.
Nemotron 3 directly addresses these challenges, delivering the performance and openness customers need to build specialized, agentic AI. “Open innovation is the foundation of AI progress,” said Jensen Huang, founder and CEO of NVIDIA.
원문: NVIDIA News — "NVIDIA Debuts Nemotron 3 Family of Open Models" (2025-12-15) 공식 원문: https://nvidianews.nvidia.com/news/nvidia-debuts-nemotron-3-family-of-open-models
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