Core Viewpoint - The article discusses the easing of export controls on NVIDIA's H20 GPU to China, highlighting the ongoing demand for AI GPUs in the Chinese market and the potential impact on NVIDIA's inventory and sales strategy [3][4]. Group 1: NVIDIA's H20 GPU and Market Demand - The U.S. has relaxed strict export controls on NVIDIA's H20 GPU designed for the Chinese market, which is part of a broader compromise related to China's rare earth magnet export restrictions [3]. - NVIDIA claims to have received assurances from U.S. officials to obtain necessary authorizations to resume H20 GPU sales to China [4]. - Jefferies estimates that NVIDIA currently has between 600,000 to 900,000 H20 GPUs in inventory, while the demand in China is around 1.8 million units [4]. Group 2: Capital Expenditure Projections - Jefferies has raised its forecast for AI capital expenditure in China by 40% this year to $108 billion, and increased the 2025-2030 forecast by 28% to $806 billion [5]. Group 3: Repair Market for NVIDIA GPUs in China - Due to U.S. sanctions limiting NVIDIA GPU supply, repair shops in China are thriving, focusing on older models like H100 and A100 GPUs [6][7]. - Repair costs for GPUs can reach up to $2,400, with some shops repairing around 500 chips monthly [7]. - Despite the sanctions, the demand for NVIDIA's GPUs remains high in China, as local alternatives like Huawei's products are limited [7].

证券研究报告|专题研究 25年05月05日 华福证券 AI浪潮中，谁将盈利突围？ 证券分析师： 研究助理： 周浦寒 S0210524040007 杨逸帆 S0210124110046 请务必阅读报告末页的重要声明 华福证券 投资要点 风险提示：历史经验不代表未来;行业不确定性风险;国内经济复苏速度不及预期;海外降息节奏不及预 期;地缘政治风险。 2 华福证券 华福证券 "宏观叙事→股价驱动→财务筛选"方法论：我们认为，技术革命中主要受益的是三类"风口"公司 。对应股价，第一波行情驱动是估值，走出第二波行情多需要盈利验证。财务视角下，我们寻找到3 个领先盈利的信号。最终，希望筛选出：AI浪潮中，或将率先盈利、走出第二波行情的核心标的。 宏观叙事中，技术革命中3类"风口"上的公司或受益腾飞：1）上游"卖铲子"，2）技术新需求， 3）赋能全行业。而且，我们可以通过普及率、渗透率，观察产业的整体进展。 公司股价中，行情驱动或从估值转向盈利。第一波行情的股价多由估值驱动，而股价走出第二波行情 ，就需要得到公司业绩的验证。2次技术革命、3类"风口"公司，都验证了这一股价驱动因素的转变 。映射当下，多数AI公司或已上涨"估 ...

Group 1 - The core focus of the 2025 Shanghai International Auto Show is on automotive intelligence, with AI technology driving the shift from high-end to mainstream markets for intelligent driving assistance [1] - The competition in the automotive market has shifted from price to intelligence, with high-level intelligent driving features like NOA expected to penetrate the mainstream price range of 100,000 to 200,000 yuan by the end of 2025, reaching a penetration rate of 20% for passenger cars [1] - The competition surrounding intelligent driving assistance is testing automakers' algorithm innovation capabilities and the completeness of their computing infrastructure [1][2] Group 2 - The development of intelligent driving assistance faces challenges in complex urban scenarios, necessitating significant cloud computing power and data training costs for training visual language models [2] - Tesla has emerged as a global leader in intelligent driving assistance due to its substantial investments in computing power, with its Texas Gigafactory deploying a supercomputing cluster with 50,000 GPUs, expected to expand to 100,000 [2] - Some Chinese automakers, like Geely and BYD, are following Tesla's lead by building their own computing platforms, while others are partnering with cloud computing firms [2] Group 3 - The safety of intelligent driving assistance is paramount, requiring automakers to ensure data security and continuously enhance the safety of their features [3] - The development process for intelligent driving includes data collection, filtering, labeling, model training, and simulation testing, with a reliable computing platform directly impacting safety improvements [3] - The efficiency of training and iteration in intelligent driving technology is crucial for market success, necessitating high technical requirements for computing platforms [3] Group 4 - Consumer-grade GPUs, while appearing cost-effective, are not suitable for large-scale AI projects, as they are designed for gaming and may lead to higher failure rates in deployment [4] - High-performance GPUs like A100 and H100 are specifically designed for data centers and large-scale computing, making them more suitable for enterprise-level applications [4] Group 5 - The automotive industry's intelligent development is expected to continue vigorously in 2025, presenting both opportunities and intensified competition [5] - Core competitive advantages will include data accumulation, processing capabilities, and algorithm optimization, ultimately revolving around the effectiveness of computing platforms [5] - Preparing for computing challenges is essential for success in the future of intelligent driving [5]