Nvidia(US:NVDA) 傅里叶的猫·2026-01-20 16:00Core Viewpoint - The article discusses the significant price increase in DRAM and NAND memory, driven by the rising demand from AI applications, particularly in the context of high bandwidth memory requirements for AI inference tasks [2][7][8]. Group 1: Price Trends - DRAM prices have nearly doubled since New Year's, causing distress among server distributors, with DDR4 32G rising from approximately 2500 to around 4500, and DDR4 64G increasing from about 6500 to 12000 [2]. - A report from Morgan Stanley indicates that DRAM, high bandwidth memory (HBM), NAND, and traditional storage categories are entering a steep upward price cycle [7]. Group 2: Supply and Demand Dynamics - The article highlights the bottleneck in storage due to the increasing demand for high bandwidth memory driven by AI applications, which is forcing the industry to optimize storage efficiency at both architectural and software levels [8]. - The shift in focus from computational power to storage capacity in AI hardware competition is emphasized, as storage becomes a critical constraint for scaling AI systems [8][9]. Group 3: Technological Innovations - NVIDIA's introduction of a context storage platform at CES 2026 aims to enhance inference tasks by integrating enterprise-level SSDs for KV Cache data management, significantly improving storage performance [10]. - The Engram technology aims to separate memory tasks from complex reasoning tasks in large language models, optimizing DRAM utilization and potentially increasing DRAM demand by a factor of three for every unit of storage efficiency gained [11][12]. Group 4: Market Outlook - The transition to Agentic AI is expected to drive massive demand for DRAM and NAND storage, as the industry moves towards more autonomous and sustainable learning systems, leading to a structural growth in storage needs [9][12]. - The ongoing production adjustments by major players like Samsung and Hynix are attributed to process transitions rather than profit maximization, indicating potential short-term supply constraints [14][15].