来源：市场资讯 中信建投：牛市整理期的市场特征 来源：中信建投证券研究，文|夏凡捷 何盛 交易结构恶化，风险偏好下滑 本周出现首次调整第一个原因是交易结构恶化。八月下旬以来资金显著集中于AI、算力、光模块、光 通信等TMT板块，导致交易结构恶化，因此本周计算机、电子等板块出现集中的短期兑现后带动指数 调整。 第二个原因是风险偏好下滑。8月29日，上交所宣布，根据指数规则决定调整科创50等指数样本，于 2025年9月12日收市后生效。寒武纪在科创50指数中的权重将面临被动调整，从当前约15%的占比降至 10%。同时月初重要活动、美联储降息预期等利好已经或接近兑现，以及彭博对监管动向的传闻引发市 场关注（或有取消部分卖空限制和抑制投机交易的政策出台），这些因素引发了市场风险偏好的短期下 滑。 9月2日至4日连续三日下跌，本轮慢牛行情首次进入整理期。主要由于八月下旬以来市场交易过热，同 时资金显著集中于TMT板块导致交易结构恶化，另一方面月初重要活动、美联储降息预期等利好已经 或接近兑现，也导致风险偏好下降。经过统计11个牛市中指数整理行情，我们认为"慢牛"背景下，指数 回调较为温和，整理期较长，指数呈现震荡修复趋 ...

杭州深度求索公司（DeepSeek）正式推出了其最新版本的人工智能模型——DeepSeek V3.1。这一版本 的模型采用了UE8M0FP8Scale参数精度，特别适配即将发布的下一代国产芯片结构，在技术和性能上取 得了重大突破，有望为国产AI芯片的发展注入强大动力。 据深度求索公司介绍，UE8M0FP8是一种专门为国产芯片架构优化的浮点数格式。相比传统的FP16或 FP32格式，FP8能够在保持相对较高数值精度的同时，显著减少内存占用和计算开销，尤其适合大规模 AI推理与训练。而"UE8M0"这一定制化命名，体现了该技术针对国产芯片特性所做的深度优化。通过 这种适配，DeepSeek V3.1在推理效率上实现了质的飞跃，相较于此前版本提升了40%，能够在更短时 间内输出结果，大大提高了AI应用的响应速度。 DeepSeek V3.1在数学推理和代码生成等关键性能指标上表现卓越。在数学推理任务中，该模型的正确 率高达92%，展现出强大的逻辑运算和问题解决能力。在代码生成方面，DeepSeek V3.1更是超越了行 业标杆GPT-435%，在Aider多语言编程基准测试中取得了71.6%的高分，且完成一次编程任 ...

Core Viewpoint - Nvidia's founder Jensen Huang is making significant efforts to regain market share in China's AI computing sector after losing ground to domestic chip companies during the U.S. export restrictions [4][5][8]. Group 1: Nvidia's Market Strategy - Jensen Huang's visit to China includes meetings with government officials and key industry players, aiming to restore confidence in Nvidia's operations in the region [4][5]. - Nvidia has received assurances from the U.S. government to resume sales of the H20 chip in China, which is a downgraded version of the H100 series designed to comply with export regulations [5][11]. - The company's market share in China has dropped from 95% during the export control period in 2022 to 50% due to the emergence of local competitors [8]. Group 2: Domestic Competitors - Chinese chip manufacturers have rapidly developed alternatives to Nvidia's H20 chip, including products from Kunlun, Moore Threads, Huawei, and Cambricon, which are aggressively targeting Nvidia's market share [7][12]. - Domestic chip companies have reported significant demand, with some experiencing a surge in orders and achieving substantial revenue growth, such as Cambricon's quarterly revenue increasing by 42.3 times [12][13]. - The competitive landscape is shifting as local firms focus on AI inference capabilities, which are less complex than training models, allowing them to better compete against Nvidia [14][15]. Group 3: Financial Implications - Nvidia's revenue loss due to the H20 ban is projected to be around $8 billion (approximately 57.3 billion yuan) in Q2 2025 [17]. - China represents a crucial market for Nvidia, contributing about 15% of its global revenue, equating to approximately $18 billion annually [16]. - The ongoing geopolitical tensions and export restrictions have created uncertainty for Nvidia's long-term prospects in China, despite the potential for short-term sales recovery with the H20's return [19][20].

Group 1: DeepSeek-R1 Model and MLA Technology - The launch of the DeepSeek-R1 model represents a significant breakthrough in AI technology in China, showcasing a competitive performance comparable to industry leaders like OpenAI, with a 30% reduction in required computational resources compared to similar products [1][3] - The multi-head attention mechanism (MLA) developed by the team has achieved a 50% reduction in memory usage, but this has also increased development complexity, extending the average development cycle by 25% in manual optimization scenarios [2][3] - DeepSeek's unique distributed training framework and dynamic quantization technology have improved inference efficiency by 40% per unit of computing power, providing a case study for the co-evolution of algorithms and system engineering [1][3] Group 2: Challenges and Innovations in AI Infrastructure - The traditional fixed architecture, especially GPU-based systems, faces challenges in adapting to the rapidly evolving demands of modern AI and high-performance computing, often requiring significant hardware modifications [6][7] - The energy consumption of AI data centers is projected to rise dramatically, with future power demands expected to reach 600kW per cabinet, contrasting sharply with the current capabilities of most enterprise data centers [7][8] - The industry is witnessing a shift towards intelligent software-defined hardware platforms that can seamlessly integrate existing solutions while supporting future technological advancements [6][8] Group 3: Global AI Computing Power Trends - Global AI computing power spending has surged from 9% in 2016 to 18% in 2022, with expectations to exceed 25% by 2025, indicating a shift in computing power from infrastructure support to a core national strategy [9][11] - The scale of intelligent computing power has increased significantly, with a 94.4% year-on-year growth from 232EFlops in 2021 to 451EFlops in 2022, surpassing traditional computing power for the first time [10][11] - The competition for computing power is intensifying, with major players like the US and China investing heavily in infrastructure to secure a competitive edge in AI technology [12][13] Group 4: China's AI Computing Landscape - China's AI computing demand is expected to exceed 280EFLOPS by the end of 2024, with intelligent computing accounting for over 30%, driven by technological iterations and industrial upgrades [19][21] - The shift from centralized computing pools to distributed computing networks is essential to meet the increasing demands for real-time and concurrent processing in various applications [20][21] - The evolution of China's computing industry is not merely about scale but involves strategic breakthroughs in technology sovereignty, industrial security, and economic resilience [21]