Nvidia(US:NVDA) Xin Lang Cai Jing·2025-11-17 05:22Core Insights - Huawei has developed a breakthrough technology that can increase AI computing power utilization from 30% to 70%, effectively doubling the performance of existing chips [1][3] - This advancement positions the domestic AI industry for significant growth opportunities, potentially allowing for a "leapfrog" in development [1] Technological Breakthrough - The average computing power utilization in the industry is currently between 30% and 40%, leading to substantial resource wastage [1] - Huawei's new technology is likened to transforming a standard tractor into a high-performance sports car, showcasing the potential of software to enhance hardware capabilities [1] Competitive Positioning - Huawei's technology directly competes with Nvidia's acquisition of Run:ai, but Huawei achieves similar functionality through independent research and development [3] - The ability to "mask" hardware differences and manage various computing resources, including Nvidia and Ascend, adds significant value in a mixed computing environment [3] Core Principles - Huawei's Scale-up technology allows for flexible resource allocation among CPU, memory, and storage, enabling a "pooling" approach [4] - This architecture facilitates hundreds of AI processors to work collaboratively as a single computing unit, overcoming individual chip performance limitations [4] Computing Scheduling - Huawei's container technology acts as an "intelligent scheduling commander," ensuring optimal task distribution to the most suitable computing units [6] - This dynamic scheduling and resource partitioning technology keeps expensive AI chips operating efficiently [6] Domestic Computing Evolution - In the context of advanced process limitations, Huawei's strategy of system architecture innovation compensates for specific shortcomings [7] - Software optimization is unlocking hardware potential, transitioning domestic computing platforms from "usable" to "well-performing" [7] AI Application Impact - Increased computing power utilization will significantly reduce AI training costs, allowing tasks that previously required 10 cards to be completed with just 5 [9] - This reduction in costs will lower barriers to AI application, accelerating the implementation of smart manufacturing, smart cities, and autonomous driving [9] Ecosystem Development - As Huawei's computing platform efficiency improves, more developers are likely to choose its ecosystem for AI application development [10] - This technological leadership is expected to translate into ecosystem advantages, creating a virtuous cycle of "better usability - more users - enhanced features" [10] Industry Opportunities - The software breakthrough will drive demand for supporting hardware, including high-performance servers, optical modules, and connectors [12] - AI application developers will benefit from reduced computing costs, leading to broader market opportunities [12]