2025.09.29 本文字数：2124，阅读时长大约4分钟 作者 |第一财经 李娜 尽管资本市场对算力泡沫发出的警告不断，但英伟达CEO黄仁勋依然对算力产业的发展调高了预期。在 近期一次访谈中，他预测AI推理即将迎来十亿倍的增长，并指出由AI增强的经济活动将催生一个每年5 万亿美元的AI基础设施资本支出市场。 值得注意的，就在访谈前的72小时，英伟达与OpenAI正式达成战略合作，黄仁勋称这笔交易为"能想象 到的最聪明的投资之一"。但也有券商分析师将这种交易模式比喻为金庸小说中的"梯云纵"——看似向 OpenAI投资1000亿美元，实则这些资金又以订单形式流回自家口袋。这种左脚踩右脚的模式，使英伟 达提前锁定利润。 面对这样的"算力激进者"，比起泡沫更担心的是错过，近一个月以来，如甲骨文、英特尔等厂商开始以 订单、资金或股权方式与英伟达结盟，一个以美国企业主导的AI新生态正在酝酿。而在国内算力市场 上，随着单颗算力芯片的焦虑逐步缓解，建立一个具有同样生命力和吸引力的生态系统也开始变得迫 切。 生态竞争进入拐点 不断被抬高的需求正在驱动全球算力竞赛进入淘汰赛阶段。 在英伟达与OpenAI的合作中，计划建设高达 ...

对于国内算力市场，建立自己的生态系统也开始变得迫切。 尽管资本市场对算力泡沫发出的警告不断，但英伟达CEO黄仁勋依然对算力产业的发展调高了预期。在 近期一次访谈中，他预测AI推理即将迎来十亿倍的增长，并指出由AI增强的经济活动将催生一个每年5 万亿美元的AI基础设施资本支出市场。 值得注意的，就在访谈前的72小时，英伟达与OpenAI正式达成战略合作，黄仁勋称这笔交易为"能想象 到的最聪明的投资之一"。但也有券商分析师将这种交易模式比喻为金庸小说中的"梯云纵"——看似向 OpenAI投资1000亿美元，实则这些资金又以订单形式流回自家口袋。这种左脚踩右脚的模式，使英伟 达提前锁定利润。 面对这样的"算力激进者"，比起泡沫更担心的是错过，近一个月以来，如甲骨文、英特尔等厂商开始以 订单、资金或股权方式与英伟达结盟，一个以美国企业主导的AI新生态正在酝酿。而在国内算力市场 上，随着单颗算力芯片的焦虑逐步缓解，建立一个具有同样生命力和吸引力的生态系统也开始变得迫 切。 生态竞争进入拐点 不断被抬高的需求正在驱动全球算力竞赛进入淘汰赛阶段。 这一决策也意味着华为主动斩断了一条可能的"利润路径"，为了获得互联网等企业的 ...

Core Insights - The discussion around ASIC growth has intensified following comments from NVIDIA CEO Jensen Huang, who stated that 90% of global ASIC projects are likely to fail, emphasizing the high entry barriers and operational difficulties associated with ASICs [2][3] - Despite Huang's caution, the market is witnessing a surge in ASIC development, with major players like Google and AWS pushing the AI computing market towards a new threshold [5][6] - The current market share shows NVIDIA GPUs dominate the AI server market with over 80%, while ASICs hold only 8%-11%. However, projections indicate that by 2025, the shipment volumes of Google’s TPU and AWS’s Trainium will significantly increase, potentially surpassing NVIDIA’s GPU shipments by 2026 [6][7] ASIC Market Dynamics - The ASIC market is expected to see explosive growth, particularly in AI inference applications, with a projected market size increase from $15.8 billion in 2023 to $90.6 billion by 2030, reflecting a compound annual growth rate of 22.6% [18] - ASICs are particularly advantageous in inference tasks due to their energy efficiency and cost-effectiveness, with Google’s TPU v5e achieving three times the energy efficiency of NVIDIA’s H100 and AWS’s Trainium 2 offering 30%-40% better cost performance in inference tasks [17][18] - The competition between ASICs and GPUs is characterized by a trade-off between efficiency and flexibility, with ASICs excelling in specific applications while GPUs maintain a broader utility [21] Major Players and Developments - Major companies like Google, Amazon, Microsoft, and Meta are heavily investing in ASIC technology, with Google’s TPU, Amazon’s Trainium, and Microsoft’s Azure Maia 100 being notable examples of custom ASICs designed for AI workloads [22][24][25] - Meta is set to launch its MTIA V3 chip in 2026, expanding its ASIC applications beyond advertising and social networking to include model training and inference [23] - Broadcom leads the ASIC market with a 55%-60% share, focusing on customized ASIC solutions for data centers and cloud computing, while Marvell is also seeing significant growth in its ASIC business, particularly through partnerships with Amazon and Google [28][29] Future Outlook - The ASIC market is anticipated to reach a tipping point around 2026, as the stability of AI model architectures will allow ASICs to fully leverage their cost and efficiency advantages [20] - The ongoing evolution of AI models and the rapid pace of technological advancement will continue to shape the competitive landscape between ASICs and GPUs, with both types of chips likely coexisting and complementing each other in various applications [21]