NetApp Taiwan on AI Storage Bottlenecks, Hybrid Cloud, and Data Management
Original: AI 訓練卡在儲存？專訪 NetApp 台灣技術總監許宏俊，拆解從 10TB 擴到 EB 不停機，混合雲與雙中心備援背後的資料管理邏輯
NetApp argues enterprise AI bottlenecks are increasingly about data management, not just storage capacity.
INSIDE interviews NetApp Taiwan technical director Hsu Hung-chun about enterprise AI infrastructure challenges. The article emphasizes nonstop scaling, automated data tiering, preprocessing, vectorization, hybrid cloud, and dual-site backup. NetApp frames storage as an active data management layer for AI projects, also integrating ransomware protection to simplify operations and improve resilience.
This INSIDE interview approaches the topic from the practical bottlenecks of enterprise adoption of generative AI, with the core view that the reason AI projects get stuck is not necessarily the model or compute power, but rather that data management cannot keep up. NetApp Taiwan Technical Director Hsu Hung-chun points out that an enterprise's data volume may grow rapidly from 10TB to the EB scale, and if the underlying storage architecture cannot scale without interruption, schedule data across environments, and manage automatically, then AI training and applications will be constrained. Therefore, what enterprises need is not simply passive storage that "holds data," but intelligent infrastructure that can support AI workflows.
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