Core Insights - The demand for AI computing is driving attention towards non-GPU AI chips, with companies like Google and Groq leading the way in alternative architectures [1][2] - The rise of custom ASIC chips is notable, as companies seek to reduce costs and enhance personalized AI capabilities [1][2] - The trend of exploring opportunities beyond GPU chips is becoming increasingly evident in the market [1] Market Trends - New players in Silicon Valley, such as Groq and SambaNova, are focusing on architectural innovation rather than GPU-like structures to achieve performance breakthroughs [2] - The success of GPU chips is largely attributed to NVIDIA's established engineering teams, making it challenging for new entrants to replicate this success [2] - Custom ASIC chips are gaining traction, as evidenced by Broadcom's significant orders and Google's ongoing development of TPU chips [2] Technological Developments - The investment in Tensor Processing Units (TPUs) is seen as cost-effective, especially in the era of large models, where data transmission scales significantly enhance computational efficiency [3][4] - TPUs are compared to 3D printers in their ability to efficiently handle computation tasks, leading to better data migration and lower energy consumption [4] - The challenge for domestic XPU chips lies in scaling "single-point efficiency" to "cluster efficiency" to meet the demands of large-scale AI computing [4][5] Infrastructure and Connectivity - Future data transmission is identified as a potential bottleneck for AI infrastructure, with Tensor Cores offering advantages in handling increased data volumes [5] - Middle and high-speed interconnect capabilities are being developed, with companies like 中昊芯英 supporting large-scale chip interconnectivity [5][6] - The evolution of Ethernet technology has made it competitive for AI chip manufacturers, with significant improvements in physical media and bandwidth capabilities [6] Software Ecosystem - The development of a robust software ecosystem is crucial, as domestic chip platforms must build their own software stacks to ensure compatibility and performance [6][7] - The ongoing evolution of large language models, primarily based on the Transformer architecture, presents opportunities for AI chip manufacturers to align their product development with these advancements [7]

Investment Rating - The industry investment rating is "Positive (Maintain)" [4] Core Insights - The AI server market continues to grow, driven by hardware upgrades and increasing performance requirements [9] - The customized ASIC chip market is expected to grow significantly, with a projected compound annual growth rate (CAGR) of 53% from 2023 to 2028, reaching a market size of 55.4 billion USD [7] - Liquid cooling technology is becoming a focal point, with the Chinese liquid cooling server market expected to reach 10.5 billion USD by 2028, growing at a CAGR of 48% from 2023 to 2028 [7] Summary by Sections Investment Recommendations and Targets - AI server performance upgrades are expected to increase hardware requirements, leading to opportunities in: 1. PCB and upstream CCL materials: Shengyi Technology, Huitian Technology, Jingwang Electronics, Shenzhen South Circuit [2] 2. Memory and storage: Lanke Technology, Jucheng Technology, Demingli, Jiangbolong [2] 3. Connectivity and networking: - Optical chips and modules: Yuanjie Technology, Guangxun Technology, Zhongji Xuchuang, Xinyi Sheng, Cambridge Technology, Huagong Technology [2] - Cables: Wolong Nuclear Materials, Zhaolong Interconnect [2] - Interconnect interfaces: Lanke Technology [2] 4. Cooling systems: Invec, Zhongshi Technology, Sixuan New Materials [2] 5. Power supplies: Magmi Te, Oulutong [2] 6. Complete machines: Industrial Fulian, Lenovo Group, Inspur Information [2] Market Trends - The global AI server shipment is expected to reach 2.14 million units by 2025, with a CAGR of 12% from 2025 to 2028 [8] - The accelerated computing market is projected to reach 22.1 billion USD by 2028 [10] - The AI computing infrastructure is expected to undergo significant advancements, breaking through existing computational bottlenecks [7] Technology Developments - The introduction of NVIDIA's Spectrum-XGS Ethernet technology aims to enhance distributed data center connectivity, potentially leading to the establishment of large-scale AI super factories [7] - The report emphasizes the importance of energy-efficient cooling solutions in data centers, highlighting the need for sustainable practices in the face of rising energy consumption [7]

弘则电子 Q2 策略：国产高端化全面出击 20250627 摘要 消费电子需求疲软导致多家公司二季度展望低于预期，尤其手机和 PC 市场，引发对电子板块持续增长的担忧，投资者风险偏好回暖时可能提 前交易长期逻辑。 中美半导体产业存在差异，美股聚焦 AI，业绩兑现度较高，而中国侧重 消费电子，AI 应用效果待观察。2023 下半年至 2024 年，美股资本开 支浪潮与中国消费电子复苏推动半导体周期，但目前可能处于周期下行 阶段。 消费电子一季度增长停滞，AI 资本开支增速放缓，预计下半年 AI 增速将 减缓。工业和汽车半导体市场虽有改善，但对整体半导体市场拉动作用 有限，当前处于周期景气度高位，后续将逐步降速。 互联网公司资本开支接近经营现金流，预示 2026 年资本开支增速可能 受限。定制化 ASIC 芯片放量，将推动小型算力公司增长，定制化推理 芯片份额提升明显。 国内晶圆厂出货量增长，但消费电子需求疲软和产能过剩导致利润率低。 国内半导体股票估值提前反映 AI 预期，但实际进展不如预期，近期估值 回调明显，国产化突破潜力仍在。 Q&A 过去几个月电子板块的市场表现如何？有哪些主要影响因素？ 工业和汽车半导 ...