NVIDIA launched Nemotron-Nano-9B-v2-Japanese, an optimized Japanese version of its Nemotron 2 Nano family. Why should you care if you work in enterprise AI or on-prem development? Because it combines strong Japanese capability, robust agent features and a manageable footprint under 10B parameters — exactly where many companies seek security and performance without the overhead of very large models.
¿Qué es Nemotron-Nano-9B-v2-Japanese?
It's an adaptation of the Nemotron-Nano-9B-v2, designed to reach state-of-the-art (SOTA) performance in the under-10B parameter category according to Nejumi Leaderboard 4. NVIDIA started from an efficient architecture (known as Transformer-Mamba in Nemotron 2 Nano) and reinforced it with Japan-specific data and training recipes. The result: better understanding and generation in Japanese, tool-calling and reasoning capabilities, all with latency and inference characteristics tuned for real infrastructures.
Nemotron-Nano-9B-v2-Japanese aims to be a practical base for on-prem deployments and agent prototypes in Japanese without sacrificing capability.
Arquitectura y rendimiento técnico
- Based on the efficient Nemotron 2 Nano architecture (Transformer-Mamba), optimized for parameter efficiency and throughput.
- NVIDIA reports up to 6x improvement in throughput versus certain open-source alternatives in specific inference scenarios, enabling deployments on edge GPUs.
- Supports multi-turn contexts and workflows with
tool callingplus structured data generation. - The training recipe builds on
Megatron-LMfor pretraining/SFT and usesNeMo Curatorfor data preprocessing and filtering. For customization, NVIDIA recommends theNeMoecosystem (NeMo Megatron-Bridge, NeMo AutoModel, NeMo-RL).
If you're an engineer, this means you have a reproducible base: recipes, libraries and microservices to integrate and measure performance in production.
Estrategia de datos: Nemotron-Personas-Japan y SDG
The key piece was using Nemotron-Personas-Japan (CC BY 4.0) as the seed for synthetic data generation (SDG). What exactly did they do?
- They built a collection of synthetic personas based on Japan's demographic and cultural distributions.
- They scaled the seed (coverage in the millions of personas is mentioned) to generate training datasets for tool-calling tasks, culturally aligned dialogue and real-world scenarios.
- They maintained cultural coherence in dialogues, which helps responses avoid sounding generic or out of context.
This isn't just a scaling trick: it's a bet on producing culturally accurate data to boost robustness in Japanese.
Pipeline de entrenamiento y componentes
The pipeline combines multiple stages and sources:
- Japanese OSS corpora:
Wikipedia,fineweb-2 Japanese,aozorabunko,sip3-ja-general-web-corpus. - Nemotron-CC-v2.1 and Nemotron-Pretraining-Specialized-v1 to enrich pretraining.
Nemotron-Personas-Japanas the seed forTool Callingand SFT datasets.Nemotron-Post-Training-v3for final tuning.- Tools:
Megatron-LM(pretraining and SFT) andNeMo Curator(filtering/prep).
The training recipe reuses Nemotron Nano 2 practices to improve stability and throughput without introducing training instabilities.
Benchmarks: Nejumi Leaderboard y resultados
Nemotron-Nano-9B-v2-Japanese took first place in the <10B category of Nejumi Leaderboard 4, which evaluates ~40 benchmarks in areas such as:
- Japanese understanding and generation.
- Agent capabilities: coding, mathematical reasoning, tool usage.
- Alignment: instruction following, toxicity, truthfulness and robustness.
It also outperforms similar-sized models like Qwen3-8B on several tasks in size-for-performance. In practice, that means better QA responses, higher fidelity in API calls, and more reliability in agent workflows.
Casos de uso prácticos y recomendaciones técnicas
- On-prem deployments for institutions handling sensitive data (banks, healthcare, government): the <10B category eases infrastructure requirements.
- Customer service agents in Japanese with external API calls: the model is already trained on
tool callingand structured generation. - Rapid prototyping of multi-agent systems or complex workflows without the overhead of much larger models.
If you're going to customize it:
- Start from the base model to save training cycles: focus fine-tuning on the specific domain instead of rebuilding general capabilities.
- Use
NeMoand the Nemotron recipes to keep reproducibility and benefit from training optimizations. - Validate alignment and biases with local benchmarks and adversarial tests before deployment.
Riesgos, licencias y consideraciones de adopción
- Check the CC BY 4.0 license of
Nemotron-Personas-Japanto understand attribution obligations and commercial use. - Even though SDG aims for cultural coherence, you still need to audit outputs for bias and safety, especially in regulated domains.
- Evaluations like Nejumi are useful, but complement them with your own tests on real data and edge cases.
Reflexión final
NVIDIA delivers not just a model, but an ecosystem: models, datasets, recipes and libraries designed so you can adapt and deploy in real Japanese contexts. Want the advantage? You start with a strong agent base and culturally aligned Japanese that lowers cost and time for customization. If you build enterprise solutions for Japan, this is a tool worth evaluating seriously.
Fuente original
https://huggingface.co/blog/nvidia/nemotron-nano-9b-v2-japanese-ja