【半导体】大厂自研芯片加速，逃离英伟达

人工智能产业链...12-14 08:00

芯片项目组创始团队招募！◀点击查看！面对全球AI热潮推升的半导体需求，科技巨头正加速摆脱对英伟达的依赖。最新消息指出，微软正与博通洽谈共同研发客制化AI芯片的计划，为数据中心打造更具成本效益与掌控度的运算核心。这个动向不仅是微软自身策略转变的一环，也与Google、亚马逊、OpenAI等巨头全面启动的自研芯片战略相呼应。根据《The Information》12月6日报导，微软正与博通商谈开发新...

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Trainium3主打低成本、高能效，被视为辉达H100、B100的替代方案。AWS甚至宣称，Trainium3在特定AI训练场景的效能与能耗比优于市面主流GPU，展现亚马逊削弱对辉达依赖的决心。OpenAI也加入战局。该公司正与博通合作开发自家客制化AI芯片，预计明年下半年开始部署。近来GPT模型需求暴增，导致OpenAI每年购买的辉达GPU数量所费不赀。为了降低成本并提升运算自主性，OpenAI加快自研芯片计划，与微软的策略方向不谋而合。黄仁勋：定制芯片远不如英伟达芯片英伟达首席执行官再次就与谷歌和亚马逊等ASIC制造商的竞争发表评论，声称没有多少团队能做到“绿色团队”所做的事。自从谷歌等公司发布最新解决方案以来，围绕英伟达与ASIC芯片的争论愈演愈烈，其核心观点是，随着世界从训练工作负载转向推理工作负载，英伟达的技术栈是可以被替代的。在最近的第三季度财报电话会议上，首席执行官黄仁勋谈到了大型科技公司内部的ASIC芯片建设，当被问及这些项目是否会导致实际的大规模部署时，英伟达首席执行官是这样回答的：分析师问到：Jensen，这个问题是问你的。考虑到你宣布的与Anthropologie的交易以及你客户的整体规模，我很好奇你对AI ASIC或专用XPU在这些架构构建中所扮演的角色有何看法？你是否注意到，你过去一直相当坚定地认为，其中一些项目最终都无法真正部署？黄仁勋回应道：是的。非常感谢，我也很感激你的提问。首先，你不是在和团队竞争——抱歉，是和公司竞争，你是在和团队竞争。而且——世界上真正擅长构建这些极其复杂系统的团队并不多。如果你仍然不明白Jensen在这里的意思，他指的是最近Anthropic达成的协议，其中包括围绕Blackwell和Rubin系统构建的基础设施。与此同时，Anthropic还签署了谷歌最新Ironwood TPU的协议，这引发了人们对ASIC芯片能否真正与NVIDIA竞争的新疑问。詹森就分析师的这一推断发表了评论，他指出，当公司开发定制芯片时，真正的竞争并非来自NVIDIA，而是来自各自的工程团队。Jensen表示，市面上很少有团队能像NVIDIA那样投入大量精力，这也是该公司否认与ASIC芯片存在激烈竞争的原因之一。我最近讨论过谷歌的TPU在推理领域也是一个有竞争力的选择，但Jensen声称NVIDIA在所有AI细分领域都是最优秀的，这表明该公司致力于在AI行业的三个主要方面——预训练、后训练和推理——都保持“不可替代”的地位。有趣的是，Jensen指出，对于云服务提供商（CSP）而言，在数据中心部署“随机ASIC”远不如选择NVIDIA的技术栈来得理想，因为NVIDIA的产品应用范围更广。因此，Jensen最终认为，大型科技公司提供的定制芯片在“工程”层面仍无法与NVIDIA匹敌。而且，即便他们能够复制NVIDIA的计算能力，NVIDIA也拥有名为CUDA的强大软件栈，这才是真正吸引业界目光的关键所在。（来源：半导体行业观察综合）","kind":"news","is_publish_news":true,"is_publish_highlight":false,"is_publish_live":false,"is_publish_wemedia":null,"editions":null,"column":"","sentiment":"-1","news_tag":"","news_rank":0,"symbols":[],"gpt_button":0,"need_auth":false,"code":"91000000","status":"200"},"commentList":[],"isCommentEnd":true,"newsSizeData":{"likeSize":0,"commentSize":0,"repostSize":0,"favoriteSize":0,"likeStatus":false,"favoriteStatus":false},"APP":{"userAgent":"Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; 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