Core Insights - The article emphasizes the critical role of High Bandwidth Memory (HBM) in supporting AI technologies, highlighting its evolution from a niche technology to a necessity for AI performance [1][2][15]. Understanding HBM - HBM is designed to address the limitations of traditional memory, which struggles to keep up with the computational demands of AI models [4][7]. - Traditional memory types like DDR5 and LPDDR5 have significant drawbacks, including limited bandwidth, high latency, and inefficient data transfer methods [4][10]. HBM Advantages - HBM offers three main advantages: significantly higher bandwidth, reduced power consumption, and a compact form factor suitable for high-density AI servers [11][12][14]. - For instance, HBM3 has a bandwidth of 819GB/s, while HBM4 is expected to double that to 2TB/s, enabling faster AI model training [12][15]. HBM Generational Roadmap - The KAIST report outlines a roadmap for HBM development from HBM4 to HBM8, detailing the technological advancements and their implications for AI [15][17]. - Each generation of HBM is tailored to meet the evolving needs of AI applications, with HBM4 focusing on mid-range AI servers and HBM5 addressing the computational demands of large models [17][27]. HBM Technical Innovations - HBM's architecture includes a "sandwich" 3D stacking design that enhances data transfer efficiency [8][9]. - Innovations such as Near Memory Computing (NMC) in HBM5 allow memory to perform computations, reducing the workload on GPUs and improving processing speed [27][28]. Market Dynamics - The global HBM market is dominated by three major players: SK Hynix, Samsung, and Micron, which together control over 90% of the market share [80][81]. - These companies have secured long-term contracts with major clients, ensuring a steady demand for HBM products [83][84]. Future Challenges - The article identifies key challenges for HBM's widespread adoption, including high costs, thermal management, and the need for a robust ecosystem [80]. - Addressing these challenges is crucial for transitioning HBM from a high-end product to a more accessible solution for various applications [80].