NTT tsuzumi 2: Why This Single-GPU Lightweight LLM Is Revolutionizing Japanese Enterprise AI

Traditional Large Language Models (LLMs) often demand dozens or even hundreds of GPUs, creating immense operational costs and energy consumption barriers that make AI deployment impractical for many enterprises. NTT is challenging this status quo with tsuzumi 2, a lightweight LLM designed to run efficiently on a single GPU, drastically reducing the total cost of ownership.
This model delivers "world-top results among models of comparable size" in Japanese language performance, prioritizing crucial business domains like knowledge, analysis, instruction-following, and safety. By optimizing specifically for the Japanese market, NTT helps local enterprises avoid the resource drain associated with deploying larger, multilingual models.
Beyond cost savings, tsuzumi 2 addresses critical issues of data sovereignty and security. As a "purely domestic model" developed in Japan, it is engineered for on-premise or private cloud deployment, directly alleviating concerns about data residency and regulatory compliance prevalent in Asia-Pacific markets.
Learn how enterprises are already adopting this powerful, efficient AI:
• Tokyo Online University utilized tsuzumi 2 on-premise to maintain student data privacy, supporting complex context understanding and long-document processing for enhanced Q&A and personalized guidance, avoiding high capital expenditure for GPU clusters.
• FUJIFILM Business Innovation partnered with NTT to integrate tsuzumi 2’s generative capabilities with its REiLI technology, enabling advanced document analysis (including mixed text and images) without sending sensitive corporate data to external AI services.
Furthermore, tsuzumi 2 features built-in multimodal support—handling text, images, and voice inputs—which simplifies complex business workflows like manufacturing quality control and customer service operations that typically rely on multiple data types.
Discover how this lightweight, specialized AI strategy is proving to be a practical path forward for organizations that cannot afford the hyperscale infrastructure required by frontier models from OpenAI, Anthropic, or Google, demonstrating that sophisticated AI implementation does not always require massive scale.
(Information drawn from sources.)

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