海光信息释疑“终止重组中科曙光” 称两家公司独立发展也具有充足的市场空间

Core Viewpoint - The merger between Haiguang Information and Zhongke Shuguang has been terminated due to significant fluctuations in stock prices and changes in market conditions, which made the conditions for the merger unfeasible [1][2][3] Group 1: Merger Termination - Haiguang Information announced the termination of the merger with Zhongke Shuguang, leading to a drop in Zhongke Shuguang's stock price and a slight decline in Haiguang Information's stock [1] - The companies cited the large scale of the transaction and the involvement of multiple parties as reasons for the prolonged discussion and eventual termination of the merger [1][2] - The decision to terminate the merger was made after careful consideration and discussions among the parties involved, reflecting a prudent approach given the current market environment [1][2][3] Group 2: Impact on Companies - The termination of the merger is not expected to have a significant negative impact on the operational and financial status of either company, as both are leaders in China's computing power industry with strong technological foundations [3] - Both companies have sufficient market space for independent development, and their separation allows for continued innovation and competition within the industry [3][4] - Haiguang Information focuses on chip development, while Zhongke Shuguang operates as a system integrator, creating a competitive ecosystem in the domestic computing power industry [3][4] Group 3: Future Prospects - Haiguang Information is optimistic about its future, with new products like the "Deep Computing 3" already in the market and the "Deep Computing 4" in development, indicating a strong position in the AI computing race [5][6] - The company is well-positioned to meet the growing demand for AI hardware, with projections indicating a significant increase in the market for accelerated servers in China by 2029 [6] - Haiguang Information's dual product matrix of CPUs and DCUs allows for comprehensive computing solutions, enhancing efficiency in AI model training and deployment [6]