Core Viewpoint - The explosive growth of the AI application market is driving a significant demand for high-performance storage, while high GPU procurement costs and the "memory wall" challenge hinder innovation for many companies [2][8]. Group 1: Storage as a Core Driver - In the AI wave, the value of storage has been completely transformed from a mere "warehouse" in IT systems to a key strategic element for enhancing AI system efficiency and reducing Total Cost of Ownership (TCO) [3][4]. - Storage module manufacturers play a crucial role in bridging the gap between different stages of AI data flow, necessitating deep optimization of controllers and flash memory chips to meet future application trends [4][5]. Group 2: Product Differentiation and Performance - AI workflows are driving further differentiation in storage products, with a clear need for tailored eSSD product matrices to meet diverse enterprise requirements [4][5]. - The PCIe 5.0 QLC eSSD series offers a capacity of up to 122.88TB and a sequential read speed of 14,000MB/s, significantly improving TCO and space efficiency compared to traditional hard drives [4][5][7]. Group 3: Overcoming the Memory Wall - The growth rate of AI model parameters has outpaced the linear expansion of top-tier GPU memory, creating a structural gap that traditional strategies cannot bridge [9][10]. - The "Wing AI Super Memory Fusion Solution" aims to address this challenge by expanding GPU memory capacity by 20 times through a high-speed, high-lifespan external cache [10][12]. Group 4: Cost Reduction and Efficiency Gains - The new system architecture allows for a dramatic reduction in training costs, with a 95% decrease in deployment costs for large models, while improving model inference concurrency by up to 50% [12][15]. - The integration of larger model parameters into Flash storage is seen as essential for promoting AI accessibility and cost-effectiveness [12][15]. Group 5: Future Directions and Goals - The company plans to upgrade its eSSD product matrix and integrate storage-computing technologies, with a roadmap to introduce PCIe 6.0 products in the second half of next year [14]. - By 2026, the goal is to deploy 200 billion parameter models on a single PC for under 10,000 yuan, and by 2027, to move trillion-level parameters to personal PCs, promoting widespread AI adoption [14][15].