Chan Ye Xin Xi Wang·2026-02-03 01:28Core Insights - The global High Bandwidth Memory (HBM) market is experiencing rapid growth, with shipments expected to increase from 1.5 billion gigabytes (GB) in 2023 to 5.7 billion GB by 2026, and revenues projected to rise from $4.35 billion in 2023 to $50 billion in 2026 [6][7][8]. HBM Industry Definition and Advantages - HBM is a high-performance semiconductor memory based on 3D stacking technology, offering high bandwidth and energy efficiency, primarily used in high-performance computing and networking applications [1][4]. - HBM has four main advantages over traditional DRAM: high bandwidth, high capacity, low power consumption, and small size [2][3]. HBM Industry Development Status - HBM technology is becoming a standard for AI acceleration cards (GPUs, TPUs, etc.), with its value share continuing to increase [4][6]. - The demand for HBM is driven by the needs of AI and high-performance computing, with significant growth expected in the coming years [6][10]. HBM Industry Chain - The HBM industry chain includes upstream materials (electrolytes, precursors, IC substrates) and semiconductor equipment (lithography machines, etching machines), with midstream focusing on HBM production and downstream applications in AI, data centers, and high-performance computing [8][9]. HBM Industry Competitive Landscape - The global HBM market is dominated by foreign manufacturers, with SK Hynix holding a 53% market share, followed by Samsung at 38% and Micron at 9% [14][15]. - Domestic companies in China, such as Changxin Memory, Changdian Technology, and others, are making significant progress in the HBM supply chain, aiming to increase local production capabilities [15][16]. HBM Industry Development Trends - HBM is positioned as a critical hardware component for AI and high-performance computing, with its unique 3D stacked structure providing superior bandwidth compared to traditional memory solutions [16][17]. - The future memory landscape will be heterogeneous, with HBM focusing on training scenarios, while other memory types will cater to specific workloads, creating a diverse memory ecosystem for the AI era [17].