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ExponTech创始人曹羽中:传统存储已触及天花板,统一通用架构重构AI存储
Tai Mei Ti A P P· 2025-08-18 08:26
Core Insights - The evolution of large models is slowing down, indicating that many associated technologies are reaching the productization stage rather than mere incremental improvements [2] - The storage industry is facing a fundamental architectural overhaul rather than a gradual upgrade, as traditional storage arrays are becoming performance and scalability bottlenecks in the context of AI [2][3] - The AI storage sector is witnessing a surge in valuations for unicorns, with a market acceptance of the "unified storage layer + AI-native interface" approach [2] Industry Changes - Traditional storage arrays are becoming bottlenecks due to four core changes driven by AI: 1. The need for ultra-high performance driven by large model training, requiring storage systems to provide high bandwidth and concurrency [3] 2. Efficiency optimization during the inference phase, necessitating a unified management of fragmented data [3][4] 3. Data control and security concerns, as enterprises are reluctant to share core data with public models [5] 4. Limitations of traditional architectures, including isolated designs and inadequate adaptation to new hardware environments [5] Ideal Storage System Characteristics - An ideal AI-era storage system should feature: 1. A unified data platform that simplifies management and avoids complex data migrations [6] 2. A flat architecture that utilizes a single unified storage layer adaptable to various business needs [6] 3. Support for new AI-native interfaces alongside traditional storage interfaces [8] ExponTech's WADP Platform - ExponTech has launched the WADP (WiDE AI Data Platform) to address core pain points in AI applications, focusing on efficient integration of storage and management of vast multi-source data [6][7] - The WADP is built on a self-developed distributed storage engine and metadata engine, capable of managing trillions of files and achieving high performance metrics [8] - The platform aims to modernize traditional storage arrays and provide a future-proof AI data infrastructure for enterprises [7][8]