日本企業がNVIDIA Nemotronで業界特化型AI構築、東京科学大学など参画

On July 15, 2026, NVIDIA announced that major Japanese enterprises, startups, and research institutions are building industry-specific AI models and applications using NVIDIA Nemotron's open models, datasets, and libraries. The company emphasized that open models form the foundation of a national AI ecosystem, enabling organizations to customize, deploy, and manage AI under their own control—a capability increasingly critical as Japan confronts aging demographics, labor force structural changes, and the dual imperatives of maintaining productivity and accelerating innovation in ways adapted to domestic industries.

Tokyo Tech developed its Swallow model series using Nemotron datasets and NVIDIA NeMo software stack for continuous pre-training and post-training. Swallow preserves the underlying model's English, math, and coding abilities while enhancing Japanese-language processing and reasoning. Enterprises are already customizing and deploying Swallow for specialized use cases, including financial document translation and asset management report generation. SB Intuitions, a SoftBank subsidiary focused on generative AI R&D and services, used NVIDIA NeMo RL and Megatron-LM libraries (part of Nemotron) to train the Sarashina series of domestically-developed generative AIs. Sarashina3 mini was selected by Japan's Digital Agency for deployment in specific AI use cases. SoftBank is also developing and deploying a Large Telecom Model (LTM) for the telecommunications sector using Sarashina and Nemotron, with the goal of realizing autonomous operation of telecommunications networks. Stockmark released a Japanese-language document comprehension model based on NVIDIA Nemotron 3 Nano Omni and is developing enterprise knowledge application features using NVIDIA NeMo Retriever and Nemotron Persona datasets; it supports manufacturing, energy, and chemicals customers through GENIAC (Generative AI Accelerator Challenge), a Japanese government project.

Industrial transformation is already underway across multiple sectors. Avatarin, an AI and robotics startup, leverages Nemotron's open models and NeMo to develop Japanese voice recognition and reasoning capabilities for enterprise AI agents; NVIDIA HGX B300 systems provide private AI infrastructure enabling agents to safely analyze customer interactions and access corporate knowledge for more accurate responses, while NVIDIA Jetson supports edge AI functions including digital avatar systems deployed at airports nationwide. ENEOS Holdings uses Nemotron open models, NVIDIA AI-Q Blueprint, and NVIDIA ALCHEMY to advance agent-based AI workflows in energy and materials R&D, accelerating materials discovery in applications like immersion cooling fluids and advanced catalysts by combining technical document search, vision-language understanding, and simulation-based molecular screening. NTT Data, an NTT subsidiary, expanded tsuzumi2 model training data using NVIDIA Nemotron-Personas-Japan to improve question-answering accuracy and strengthen response quality for questions requiring additional knowledge; it is evaluating scalable multi-agent frameworks using NVIDIA Agent Toolkit (including Nemotron) to assign tasks to optimal models and realize accurate, efficient, autonomous enterprise workflows. Hitachi is integrating Nemotron and NVIDIA Cosmos open models with its own IT and OT (operational technology) expertise through its "Customer Zero" initiative to address real-world operational challenges; as part of a multi-agent orchestration platform, these technologies coordinate IT and OT operations and transform enterprise-scale business processes across complex workflows. Sakana AI integrated Nemotron into its model-routing platform Fugu, enabling dynamic selection of the optimal model for each task within agent-based AI workflows; by routing each request to the most suitable model, Fugu helps developers balance accuracy, performance, and cost when deploying multiple open and proprietary models.

Nemotron models are openly published with weights, datasets, and training recipes, allowing organizations to customize models for specific workflows, evaluate and optimize them using NVIDIA NeMo, and deploy directly into environments meeting regulatory, sovereignty, and data localization requirements. Nemotron models are available as NVIDIA NIM microservices on Hugging Face, ModelScope, OpenRouter, and build.nvidia.com, as well as through NVIDIA Cloud Partners, inference platforms, and cloud service providers.

Japan faces acute demographic and labor challenges—aging population and structural workforce changes—that make domestically-tailored AI infrastructure strategically essential. NVIDIA's decision to highlight Japanese adoption of Nemotron reflects broader industry momentum toward open, customizable foundation models that preserve organizational control and data sovereignty. NVIDIA's CEO Jensen Huang frames this in national terms: "every country and company needs to own and manage its own AI infrastructure," and open models enable that independence by letting developers verify, adapt, and protect AI to local needs.

The body shows two parallel tracks of adoption. First, research and infrastructure: Tokyo Tech's Swallow model series explicitly strengthens Japanese-language and reasoning abilities atop retained English/math/coding competence, with commercial applications already in motion (financial translation, asset reports). SB Intuitions' Sarashina—selected by Japan's Digital Agency—signals early-stage public-sector validation. Second, enterprise applications: Avatarin uses Nemotron for enterprise agents with Japanese voice recognition and reasoning; ENEOS applies it to materials discovery workflows combining document search, vision-language understanding, and simulation; Hitachi integrates Nemotron and Cosmos models into multi-agent orchestration for enterprise process transformation; NTT Data extended its tsuzumi2 model's training data using Nemotron-Personas-Japan to improve Q&A accuracy. Sakana AI's Fugu platform dynamically routes each task to the optimal model, balancing accuracy, performance, and cost—a pragmatic approach to heterogeneous model ecosystems.

The technical availability—models on Hugging Face, ModelScope, OpenRouter, build.nvidia.com, and through cloud partners as NIM microservices—removes deployment friction. Transparency (open weights, datasets, recipes) and the NVIDIA NeMo toolkit's customization support address regulatory and localization concerns that would otherwise block adoption in regulated sectors (energy, healthcare, finance). This is not merely product distribution; it is the scaffolding for a defensible, domestically-rooted AI capability.

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