NVIDIA Nemotron 2 Nano 9B Japanese: 日本のソブリンAIを支える最先端小規模言語モデル

2026-02-17 · Source: Hugging Face - Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Advanced, medium

Summary

NVIDIA released Nemotron-Nano-9B-v2-Japanese on February 17, 2026, a small language model (SLM) with fewer than 10 billion parameters, achieving state-of-the-art performance on the Nejumi Leaderboard 4 for its category. This model is designed for advanced Japanese understanding and agent capabilities, facilitating on-premise deployment and streamlined customization for Japanese enterprises. It builds upon the Nemotron-Nano-9B-v2 architecture and utilizes Nemotron-Personas-Japan, a CC BY 4.0 dataset of synthetically generated personas, for high-quality synthetic data generation (SDG) in tool-calling scenarios. The training pipeline involved continued pre-training with Japanese open-source corpora and NVIDIA's Nemotron stack, followed by supervised fine-tuning (SFT) using the persona-seeded tool-calling dataset. The model demonstrates up to 6x throughput improvement compared to open-source alternatives and supports direct deployment or customization via the NeMo Framework.

Key takeaway

For AI Architects and NLP Engineers developing solutions for Japanese enterprises, Nemotron-Nano-9B-v2-Japanese offers a robust foundation. Its compact size and proven agent capabilities enable efficient on-premise deployment and faster fine-tuning cycles. Consider integrating this model for applications requiring advanced Japanese understanding and tool-calling, especially where data sensitivity or infrastructure constraints are critical. Your teams can leverage its architecture and the Nemotron-Personas-Japan dataset to accelerate custom model development.

Key insights

NVIDIA's Nemotron-Nano-9B-v2-Japanese offers SOTA Japanese AI capabilities in a compact, deployable SLM.

Principles

• SLMs reduce infrastructure hurdles for on-premise deployment.
• High-quality synthetic data generation improves model capabilities.
• Culturally accurate personas enhance training data diversity.

Method

The model was built using continued pre-training with Japanese corpora and NVIDIA's Nemotron stack, followed by SFT with a tool-calling dataset seeded by Nemotron-Personas-Japan for culturally appropriate dialogue.

In practice

• Deploy Nemotron-Nano-9B-v2-Japanese for agent workflows.
• Use Nemotron-Personas-Japan for custom SDG.
• Fine-tune with NeMo Framework for domain-specific applications.

Topics

• NVIDIA Nemotron
• Small Language Models
• Japanese AI
• Synthetic Data Generation
• Agentic AI

Code references

• NVIDIA/Megatron-LM
• NVIDIA-NeMo/Curator
• NVIDIA-NeMo/Megatron-Bridge
• NVIDIA-NeMo/Automodel
• NVIDIA-NeMo/RL

Best for: AI Architect, NLP Engineer, CTO, AI Engineer, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Hugging Face - Blog.