Core Insights - The article discusses the challenges and solutions in localizing AI deployment, particularly focusing on the high costs associated with GPU memory and data storage limitations [2][11][17] - It highlights two main approaches to overcoming these challenges: the "Compute Power" approach, which involves stacking high-end GPUs, and the "System" approach, which introduces a flexible storage layer to optimize data management [2][3] Group 1: AI Deployment Challenges - Companies face significant barriers in AI localization due to data supply and storage issues, as well as the high costs of GPU memory [2] - The "memory wall" created by the exponential growth of AI model parameters versus the linear increase in GPU memory presents a critical dilemma for organizations [11] Group 2: Innovative Solutions - Chuang Hsing Technology has developed a dual-layer approach to storage, utilizing high-performance eSSD matrices and FPGA controllers to optimize memory usage and reduce costs by 90% [3][4] - Their QLC eSSD series offers unprecedented single-disk capacity of 122.88TB, significantly simplifying data center deployment and reducing overall operational costs [7][8] Group 3: Enhanced Performance and Cost Efficiency - The "Super Memory Fusion" solution allows for a 20-fold expansion of effective GPU memory without the need for additional high-end GPUs, making large model training more accessible [12][14] - The integration of the "AI Link Algorithm Platform" enhances concurrent performance by up to 50%, achieving a balance between cost savings and efficiency [15] Group 4: Comprehensive Product Offerings - Chuang Hsing Technology provides a range of products tailored for different AI applications, including Super AI PCs, workstations, and servers, designed to meet the needs of startups to enterprise-level deployments [16] - Their solutions have been successfully implemented in various sectors, including government and education, demonstrating their reliability and adaptability [16][17]