2024年中国服务器CPU行业概览：信创带动服务器CPU国产化

Industry Investment Rating - The report does not explicitly provide an investment rating for the server CPU industry [1][2] Core Viewpoints - The server CPU industry in China is driven by the rapid development of data centers and the adoption of high-priced domestic CPUs due to the "Xinchuang" initiative [2] - The market size of China's server CPU industry is expected to grow from RMB 1,900 5 billion in 2023 to RMB 3,628 3 billion in 2028, with a CAGR of 14 5% [12] - The competitive landscape is dominated by X86 and ARM architectures, with domestic players like Haiguang and Kunpeng leading in performance and ecosystem development [13] Market Size and Growth - The market size of China's server CPU industry grew from RMB 1,005 9 billion in 2018 to RMB 1,900 5 billion in 2023, with a CAGR of 13 6% [12] - The market is projected to reach RMB 2,111 0 billion in 2024 and RMB 3,628 3 billion in 2028, driven by data center growth and domestic CPU adoption [12] Competitive Landscape - Haiguang (X86) and Kunpeng (ARM) lead in performance, while Haiguang and Zhaoxin have ecosystem advantages due to their X86-based architectures [13] - Shenwei and Loongson are leading in terms of self-developed instruction sets, offering higher levels of autonomy and control [13] - Domestic server CPUs are priced nearly double that of foreign products, with little willingness to reduce prices in the short term [12] Instruction Set and Ecosystem - The CPU industry is dominated by two ecosystems: Wintel (X86 + Windows) for servers and PCs, and AA (ARM + Android) for mobile devices [7] - X86 architecture has a strong presence in the server market with established standards, while ARM is expanding into servers with lower power consumption and better cost efficiency [10] - Shenwei and Loongson use self-developed instruction sets, offering higher autonomy, while Kunpeng and Phytium rely on ARM licenses, which limits their autonomy [13] CPU vs GPU - CPUs are designed for general-purpose tasks with high flexibility, while GPUs excel in parallel processing tasks like image processing and scientific computing [5] - CPUs have fewer cores but handle complex tasks efficiently, whereas GPUs have hundreds of cores optimized for parallel data processing [5] - GPUs outperform CPUs in tasks requiring high throughput, such as deep learning and large-scale simulations [5]