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    3. Western Digital Analysis Claims Storage is the New Bottleneck for AI Infrastructure

    Western Digital Analysis Claims Storage is the New Bottleneck for AI Infrastructure

    By: rootdata|2026/08/15 02:03:28
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    Western Digital analysis indicates that as the scale of artificial intelligence applications rapidly expands, the construction of AI data centers is shifting from competition in GPU computing power to competition in data storage capacity, making storage planning a core aspect of AI infrastructure. IDC predicts that by 2030, the global annual increase in data volume will reach 718ZB. The data generated by AI systems continues to accumulate even after computational tasks are completed, including training data, model checkpoints, and inference logs. Currently, many AI infrastructure plans overly focus on GPU utilization, neglecting the importance of data retention. The data generated during training and inference processes will become crucial assets for model iteration and quality assessment, and storage costs will directly impact the long-term operational efficiency of AI systems. As data scales reach PB and even EB levels, a single storage architecture will struggle to meet demands, requiring enterprises to adopt a tiered storage strategy, utilizing high-performance flash for training and real-time inference, while employing high-capacity HDDs and object storage for long-term data preservation. Analysts believe that key metrics for future AI infrastructure competition will include storage costs per PB of data, energy consumption, and data lifecycle management capabilities. If enterprises continue to view storage as a secondary aspect, they may face issues such as uncontrolled data costs and decreased model iteration efficiency.

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