巨头加速抛弃英伟达

半导体芯闻01-27

如果您希望可以时常见面，欢迎标星收藏哦~微软也加入了大型科技公司摆脱对英伟达依赖的浪潮，推出了自己的人工智能（AI）芯片。各大科技公司都在开发定制芯片或寻求供应商多元化，以降低对英伟达的依赖——英伟达占据了AI芯片市场90%的份额。然而，英伟达以其图形处理器（GPU）为代表，正通过构建AI工厂展开反击。它不再仅仅销售GPU，而是通过垂直整合芯片、服务器、软件和模型，转型为一家“全栈AI”基础设施...

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GPU的高昂成本是关键驱动因素。它们不仅价格昂贵，而且供应常常无法满足需求，导致及时采购困难重重。此外，尽管NVIDIA芯片用途广泛，但它们并未针对特定公司的特定AI任务进行优化。因此，大型科技公司正在开发专为自身数据中心、AI模型和服务量身定制的芯片，以提高能效。当地时间26日，微软发布了其首款商用AI芯片“Maia 200”。此前，微软曾在2023年11月推出过“Maia 100”，该芯片用于微软内部的Azure云平台。这款新芯片在两年后发布，性能有所提升，采用台积电3纳米工艺，并集成了SK海力士的HBM3E显存。Maia 200 专为高性能 AI 推理而设计——即训练好的 AI 模型分析新数据以识别模式并做出预测或决策的过程。微软表示：“在‘轻量级计算’方面，它的性能是 AWS 最新 AI 芯片的三倍，计算效率也高于谷歌的 AI 芯片。” 首席执行官萨蒂亚·纳德拉表示：“这款产品旨在实现业界领先的推理效率，在同等预算下可提供高出 30% 的性能。” Maia 200 预计将支持 OpenAI 的最新 AI 模型 GPT-5.2 和微软的 Copilot。为了跟上人工智能的快速发展步伐，微软也加快了芯片的生产和部署。“我们将从初始生产到数据中心部署的时间缩短到类似人工智能基础设施项目的一半以上，”该公司表示。微软已在爱荷华州的数据中心安装了该芯片，并计划将其扩展到其他数据中心，以便Azure客户能够使用。路透社指出，“微软可能会减少对英伟达的依赖。”其他大型科技公司也在努力降低对英伟达的依赖。谷歌使用其定制的张量处理单元 (TPU) 来训练和运行其 Gemini AI 模型。TPU 专为 AI 而设计，在某些任务中性能优于 GPU，且功耗更低，从而降低了运营成本。AWS 上个月发布了其 Trainium3 AI 芯片，该芯片拥有业界领先的能效。AWS 表示：“与之前的 Trainium2 相比，计算性能提升了四倍，而能耗降低了约 40%。”这款芯片用于训练 AWS 的内部 AI 模型 Nova，并应用于面向客户的云服务中。Meta 开发了自己的 AI 芯片 MTIA，而 OpenAI 正在与博通合作开发一款定制芯片，计划于今年下半年发布。各公司也在推进供应链多元化。例如，Meta公司正在探讨在其计划于2027年投入运营的数据中心中使用谷歌的TPU。随着人工智能芯片需求的增长，谷歌——此前仅在内部使用TPU进行诸如Gemini训练之类的任务——正计划扩大外部销售。Anthropic公司去年10月透露，其人工智能模型Claude的构建和部署使用了超过100万个AWS的Trainium2芯片。NVIDIA 的积极防御尽管包括微软在内的大型科技公司都是英伟达的客户，但一些分析师认为英伟达的市场主导地位正在动摇。《华尔街日报》指出，“人工智能公司正致力于实现数据中心人工智能芯片供应商多元化”，并补充道，“他们的定制芯片对英伟达构成了又一威胁。”然而，英伟达的业务范围已从人工智能芯片扩展到人工智能模型和机器人领域。即使其在人工智能芯片市场的份额略有下降，该公司仍致力于保持其作为一家拥有完善人工智能生态系统的企业的竞争力。除了GPU之外，英伟达还在积极供应中央处理器（CPU），即计算的核心。就在同一天，英伟达宣布向数据中心运营商CoreWeave追加20亿美元的投资，计划在其数据中心部署自家的CPU。彭博社指出：“这是英伟达首次独立向客户供应CPU，对英特尔和AMD构成了挑战。”NVIDIA 在人工智能模型开发方面也积极进取。它发布了一款开源天气预报人工智能模型。此外，它还在开发用于自动驾驶汽车的 Alpamayo 和用于推理的 Cosmos。为了迎接物理人工智能时代，NVIDIA 运营着 Omniverse 平台，这是一个数字训练场，机器人可以在虚拟空间中进行模拟和协作。近期收购 Groq 等公司，使 NVIDIA 得以巩固其在人工智能推理市场的技术地位。其目标是使 NVIDIA 基础设施即使在推理阶段也至关重要，即人工智能应用于现实世界场景的阶段。预计英伟达今年将超越苹果，成为台积电最大的客户。科技咨询公司Creative 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