英伟达AI“帝国”B面：20年收购史的“克制和清醒”

21世纪经济报道03-26

英伟达的AI“帝国”，早已不止于GPU本身，而是一整套围绕算力、网络与软件平台构建的生态体系。　　最新一笔投资动作再次印证这一点。2026年3月，英伟达分别向光技术公司Lumentum和Coherent各投资20亿美元。这并非简单的财务投资，而是对AI基础设施关键环节的提前卡位。　　回溯半导体产业的发展史，投资、收购、兼并与重组始终是行业主旋律。这是全球最激荡诡谲、波澜壮波的市场，企业的兴衰往往...

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Images，将光线追踪技术收入囊中，为后来的RTX图形与专业渲染市场奠定基础；2008年收购AGEIA，则获得PhysX物理引擎，使其在游戏乃至仿真领域建立差异化优势。　　这一阶段的核心特征，是围绕GPU做能力扩张，不断向图形计算的上下游延伸，强化技术护城河。但进入2010年后，英伟达的收购策略一度转向更为激进的多元化探索和试错。　　一个典型的案例，是2011年以3.67亿美元收购Icera，试图借此切入移动通信基带市场，在智能手机浪潮中分一杯羹。然而，这一布局最终因竞争格局与战略偏差而失败，英伟达于2015年关闭Icera业务，正式退出手机芯片市场。　　这次挫折，成为英伟达收购策略的重要分水岭。其后，公司明显收缩非核心战线，将资源重新集中到自身最具优势的计算能力上。与此同时，随着AI技术开始萌芽，英伟达逐步将目光投向数据中心领域，通过一系列规模不大的收购，提前布局数据存储、网络传输等关键基础环节。　　从结果来看，这一阶段虽缺乏标志性的大额交易，但战略意图已悄然转变，从多元试错回归核心能力聚焦。看似零散的收购，实则围绕未来数据中心与高性能计算体系进行铺垫，为英伟达后续全面转向AI基础设施奠定了技术与资源基础。　　数据中心爆发与监管挑战　　2019年到2022年，英伟达的收购战略迎来真正意义上的定型时刻。　　2019年成为英伟达收购历程的关键转折点，这一年，英伟达宣布以69亿美元收购以色列高速网络公司Mellanox，这也是当时英伟达历史上最大的一笔收购，并于2020年正式完成。　　这笔收购，也被业内普遍视为英伟达从GPU芯片厂商迈向数据中心整体解决方案提供商的关键一步。通过这笔交易，英伟达不仅获得了领先的InfiniBand高速互连技术及数千项专利，更重要的是补齐了数据中心网络传输短板，形成了CPU、GPU与DPU协同的基础架构能力。　　从结果来看，Mellanox迅速成为英伟达数据中心的重要助力。并表后首个完整季度，其收入即贡献公司约14%的营收和超过30%的数据中心收入。此后几年，网络业务成为增长最快的板块之一，截至2026财年，相关收入已突破310亿美元，相比2021财年布局网络业务时增长超10倍，足以印证这笔收购的战略价值。　　这意味着，英伟达不仅掌握了算力生产，也开始掌控算力流动，在AI基础设施中的地位发生质变。在Mellanox奠定基础之后，英伟达试图进一步向上整合算力体系。　　2020年，英伟达发起了其史上最受关注的一笔收购——以400亿美元要约收购Arm，意图打造“GPU+CPU”的半导体帝国，进一步完善算力生态。然而，这场交易从一开始就面临重重阻力：英国竞争与市场管理局以“损害竞争、威胁国家安全”为由干预，欧盟警告可能推高全球芯片价格，美国联邦贸易委员会直接提起诉讼，同时高通、微软、谷歌等Arm核心客户集体反对，担心Arm被英伟达掌控后失去技术授权的公平性。　　最终，这场备受瞩目的收购在2022年宣告终止，英伟达支付12.5亿美元终止费用。这一事件也成为公司战略的重要分水岭，在触及全球半导体生态核心资产时，监管边界与产业中立性已成为不可回避的约束条件。此后，英伟达明显调整策略，从试图“大规模整合核心架构”，转向更加灵活的能力补齐与生态绑定。　　与此同时，围绕数据中心的软件与系统能力，英伟达也在持续加码。包括收购SwiftStack（对象存储软件）和Cumulus Networks（数据中心网络操作系统）等在内的一系列交易，逐步补齐从硬件到软件的全栈能力，使其不仅提供算力芯片，更能够输出完整的数据中心解决方案。　　整体来看，2019年至2022年，是英伟达从转型探索走向路径确立的关键阶段，一方面通过Mellanox完成数据中心能力闭环，另一方面在Arm受阻后完成战略收敛，并加速向全栈AI基础设施平台演进，推数据中心业务成为公司核心增长引擎。　　AI全栈卡位玩转“类收购”新模式　　随着AI技术的爆发式增长，英伟达的收购战略全面聚焦AI生态，收购步伐明显加快。同时，还创新采用“技术授权+团队加盟”的类收购模式，在规避监管压力的同时，高效补齐技术短板，围绕AI全栈生态进行系统性卡位。　　与此前的节奏相比，近两年英伟达明显提高了出手频率。据Crunchbase数据，截至2025年12月15日，英伟达（包括直投+NVentures）共计出手83次，涉及76家企业，若算上循环交易，公开出手近90次，约是2024年的1.6倍。其中2024年完成7例收购/类收购，2025年更是多次出手，呈现出高频、精准、灵活的特点，英伟达也由此加速从硬件优势向平台生态全面延伸。　　从具体布局来看，英伟达的收购重点已高度集中在AI软件栈与算力调度体系。2023年，公司收购AI模型压缩初创公司OmniML，强化模型推理效率；2024年连续完成多笔关键交易，包括以7亿美元收购Run:ai（AI工作负载调度）、3亿美元收购Deci（深度学习优化平台）、2.5亿美元收购OctoAI（AI开发平台），逐步打通从模型开发、训练到部署的关键环节。　　这一系列动作标志着，英伟达将GPU的竞争升级为平台生态的竞争，从硬件优势出发，持续向软件、调度和开发入口延伸，控制算力体系中的关键节点。　　进入2025年，这一战略进一步提速并深化。英伟达全年完成至少6起收购或“类收购”，从以3.2亿美元收购合成数据公司Gretel Technologies，到整合Lepton、CentML等AI优化与服务能力，再到收购高性能计算调度系统开发商SchedMD，持续强化数据、算法与算力之间的协同效率。　　更具标志性的，是其在底层算力与互连领域的两笔“非常规操作”。一方面，英伟达通过技术授权与招聘方式整合Enfabrica的AI网络芯片技术和团队，补强数据中心互连；另一方面，则以约200亿美元规模，通过类似路径“收编”AI推理芯片公司Groq的核心技术与团队，直接强化AI推理这一关键赛道的竞争力。　　值得注意的是，这两笔交易均未采用传统意义上的全资收购，而是以“技术授权+核心团队加盟”为核心结构。这种“类收购”模式，既降低了收购成本，又成功规避了反垄断监管压力。　　这种模式并非英伟达首创，此前微软、谷歌、Meta均有采用，但英伟达将其运用得极为熟练，尽管英伟达强调这并非正式收购，但Groq和Enfabrica的核心骨干（包括创始人）均加入英伟达，核心技术也被英伟达掌握，实际上达到了“收编”的效果。　　如果将时间线拉长来看，英伟达过去二十年的收购脉络，正在这一阶段完成最终跃迁，从早期的技术补位，到中期的数据中心能力构建，再到如今以AI为核心的生态主导。　　与其他科技巨头的高频并购不同，英伟达的收购始终保持克制，成立至今收购/类收购仅30例左右，多数年份收购数量不超过3起，却每一次都精准踩中产业转型节点，从早期巩固GPU霸权，到试水移动市场的试错，再到数据中心转型的突破，最终聚焦AI全栈生态的构建，每一笔收购都围绕“算力、互连、软件栈与生态入口”四大核心，从未偏离核心战略。　　从收购模式来看，英伟达也经历了从全资收购到“类收购”的进化，尤其是近两年，面对日益严格的反垄断监管，新模式成为其首选，既实现了能力整合，又规避了监管风险，展现出成熟的战略考量。　　英伟达的收购逻辑的核心是“不求拥有全部，只求掌控关键”，这种克制与清醒，让其在行业迭代中始终占据主动，从一家图形芯片初创公司，成长为全球AI基础设施的绝对领导者。　　可以预见，未来英伟达的收购仍将围绕AI生态持续展开，重点聚焦AI推理、算力调度、数据安全与基础软件等关键领域，并进一步优化“类收购”模式。在监管边界与技术竞赛并存的环境下，持续完善全栈布局，巩固其在AI算力领域的统治地位。（文章来源：21世纪经济报道）","kind":"news","is_publish_news":true,"is_publish_highlight":false,"is_publish_live":false,"is_publish_wemedia":null,"editions":null,"column":"","sentiment":"0","news_tag":"merge","news_rank":0,"isVideo":false,"video":null,"symbols":[],"gpt_button":0,"need_auth":false,"need_login_tip":false,"code":"91000000","status":"200"},"commentList":[],"isCommentEnd":true,"newsSizeData":{"likeSize":1,"commentSize":0,"repostSize":0,"favoriteSize":0,"likeStatus":false,"favoriteStatus":false},"APP":{"userAgent":"Mozilla/5.0 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