Core Insights - The report highlights the launch of AI high-end chips by suppliers and the memory upgrades that are driving both volume and price increases in DRAM [1][3]. Industry Perspective and Investment Recommendations - The next-generation NVIDIA Rubin CPX has offloaded AI inference computational loads at the hardware level, with memory upgrades providing faster data transmission [2]. - The NVIDIA Vera Rubin NVL144 CPX server integrates 36 Vera CPUs, 144 Rubin GPUs, and 144 Rubin CPX GPUs, offering 100 TB of high-speed memory and 1.7 PB/s memory bandwidth per rack [2]. - The performance of the Rubin CPX in handling large context windows is up to 6.5 times higher than the current flagship GB300 NVL72 [2]. - The Rubin CPX is optimized for long context performance at the "millions of tokens" level, featuring 30 peta FLOPs of NVFP4 computing power and 128 GB of GDDR7 memory [2]. - The acquisition of Shenzhen Jintaike's storage line by Kaipu Cloud aims to enhance enterprise-level DDR capabilities [3]. - The average capacity of DRAM and NAND Flash in various AI applications, particularly in servers, is expected to grow, with a projected 17.3% annual increase in average capacity for Server DRAM in 2024 [3]. - The demand for AI servers continues to rise, with high-end chips like NVIDIA's next-generation Rubin and self-developed ASICs from cloud service providers (CSPs) being launched or entering mass production, contributing to the increase in both volume and price of high-speed DRAM products [3].

Core Viewpoint - The article discusses the significance of Nvidia's Rubin CPX, highlighting its tailored design for AI model inference, particularly addressing the inefficiencies in hardware utilization during the prefill and decode stages of AI processing [1][2][3]. Group 1: AI Inference Dilemma - The key contradiction in AI large model inference lies between the prefill and decode stages, which have opposing hardware requirements [2]. - Prefill requires high computational power but low memory bandwidth, while decode relies on high memory bandwidth with lower computational needs [3]. Group 2: Rubin CPX Configuration - Rubin CPX is designed specifically for the prefill stage, optimizing cost and performance by using GDDR7 instead of HBM, significantly reducing BOM costs to 25% of R200 while providing 60% of its computational power [4][6]. - The memory bandwidth utilization during prefill tasks is drastically improved, with Rubin CPX achieving 4.2% utilization compared to R200's 0.7% [7]. Group 3: Oberon Rack Innovations - Nvidia introduced the third-generation Oberon architecture, featuring a cable-free design that enhances reliability and space efficiency [9]. - The new rack employs a 100% liquid cooling solution to manage the increased power demands, with a power budget of 370kW [10]. Group 4: Competitive Landscape - Nvidia's advancements have intensified competition, particularly affecting AMD, Google, and AWS, as they must adapt their strategies to keep pace with Nvidia's innovations [13][14]. - The introduction of specialized chips for prefill and potential future developments in decode chips could further solidify Nvidia's market position [14]. Group 5: Future Implications - The demand for GDDR7 is expected to surge due to its use in Rubin CPX, with Samsung poised to benefit from increased orders [15][16]. - The article suggests that companies developing custom ASIC chips may face challenges in keeping up with Nvidia's rapid advancements in specialized hardware [14].