存力短板待补，英韧科技披露产品迭代计划

Core Insights - The performance of some GPUs is hindered by insufficient storage speed, leading to underwhelming application results and overall efficiency limitations [2][3] Group 1: Storage and GPU Performance - The main issue affecting GPU performance is the waiting time for storage data, which results in resource wastage and restricts efficiency improvements [2] - The demand for storage solutions is increasing as the pace of computational power development accelerates [2] - New AI scenarios will require SSDs to adopt GPU direct scheduling methods to achieve high throughput and performance [2] Group 2: Market Trends and Innovations - Companies like 英韧科技 are focusing on enterprise-level storage solutions tailored for AI applications, aligning with international trends [2] - Recent product launches include "AI SSD" by 铠侠 and ultra-high-speed products by 三星 and 海力士, designed to meet the demands of AI GPU data scheduling [2] Group 3: Technical Requirements for AI SSDs - Key performance indicators for storage in data centers include high IOPS, low latency, and QoS, which define storage capabilities from efficiency, response speed, and stability perspectives [3] - The construction of AI SSDs requires three main elements: media with low latency and high transfer efficiency, high-speed interfaces and protocols, and a simple yet efficient architecture [3] Group 4: Competitive Product Development - 英韧科技's latest Dongting-N3X series utilizes PCIe Gen5 interface and new XL-Flash media to meet current AI SSD application demands [4] - Future product iterations are planned, with expectations to reach 10M IOPS by 2026 and 25-50M IOPS by 2027, along with the integration of NVMe and CXL protocols [4]