博通：AI ASIC与网络芯片龙头，业绩或迎来跨越式增长_老虎社区_美港股上老虎

博通：AI ASIC与网络芯片龙头，业绩或迎来跨越式增长

前瞻科技研究01-15

=主要观点=投资逻辑：1）作为全球加速计算与网络连接的领军者，博通充分受益于AI ASIC和AI网络的爆发，通过与超大规模云服务商的深度绑定，博通占据了ASIC市场的主导地位；2）AI推理场景占比提升驱动ASIC需求激增，凭借丰富的IP储备和多年的ASIC工程经验，博通在谷歌、OpenAI等客户的ASIC订单量有望持续超预期；3）公司业绩跨越式增长，预计未来三年净利润年复合增速近43%。丰富的IP...

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PHY等丰富而顶尖的IP储备、以及长达十多年的ASIC设计工程经验，构建了深厚的竞争壁垒。公司不仅助力谷歌TPU系列完成了从v1到v7的迭代，还通过与Meta、OpenAI等客户的战略合作，确立了其在后续推理芯片市场的领先地位。谷歌TPU产品力突飞猛进，其叙事逻辑已从谷歌自用开始向外销扩展，这也为博通未来ASIC的出货打开了更广阔的成长空间。网络技术全栈布局，构建端到端网络解决方案。在AI集群向十万卡、百万卡演进的过程中，网络带宽与延迟已成为算力释放的瓶颈。公司通过Tomahawk交换芯片、Jericho路由芯片以及Thor网卡，构建了端到端的以太网解决方案。特别是其主导的超以太网联盟（UEC），正利用开放生态和在大规模AI训练任务中的卓越效率，获得了Meta、Oracle等巨头的青睐。AI驱动公司成长逻辑从并购向内生增长转变。从早期安华高对博通品牌的整合，到近年来对CA、Symantec以及VMware的战略收购，博通已发展成为业务覆盖半导体与基础设施软件的综合体。随着头部科技大厂自研ASIC、AI集群对高带宽网络的需求爆发，公司有望迎来业绩的跨越式增长。盈利预测与评级：作为全球AI ASIC龙头企业，预计公司未来三年Non-GAAP净利润年复合增速为42.8%。给予公司2027财年30xPE (Non-GAAP)，目标价469.95美元，对应市值2.2万亿美元。首次覆盖，给予“买入”评级。风险提示：AI ASIC需求或不及预期、下游客户Capex力度或不及预期、与GPU竞争加剧等风险。=正文=1全球AI ASIC与网络芯片龙头博通是全球AI ASIC和网络芯片的龙头企业。公司的历史上是一部通过精准并购实现价值跃迁的商业史，其发展轨迹可分为三个关键阶段：1）惠普/安捷伦时期与Avago的诞生（1961-2015）：公司的根源可追溯至1961年惠普公司的半导体部门。1999年，该部门随安捷伦科技（Agilent）分拆。2005年，私募股权巨头KKR和Silver Lake以26.6亿美元收购安捷伦的半导体事业部，成立安华高科技（Avago Technologies）。这一阶段确立了公司注重现金流和高毛利的经营基因。2）“经典”博通的整合与重组（2016-2018）：2016年，安华高（Avago Technologies）以370亿美元收购了无线通信巨头博通公司（Broadcom Corp.），并沿用了“博通”这一品牌。这次合并是半导体历史上最成功的案例之一，通过剥离非核心低毛利业务，保留了博通在网络交换、宽带和无线连接领域的资产，构建了强大的护城河。3）基础设施软件的版图扩张（2018年至今）：意识到半导体周期的波动性，博通开启了向基础设施软件的战略转型。2018年公司以189亿美元收购CA Technologies（大型机软件），2019年以107亿美元收购赛门铁克（Symantec）企业安全业务，2023年以690亿美元完成对VMware的收购。这一系列动作将博通打造成为“半导体+软件”的双引擎巨头，极大地平滑了业绩波动。公司划分为多个拥有高度自主权的业务部门，每个事业部都是其细分领域的绝对领导者。这些事业部主要归类为两大核心板块：1）半导体解决方案（Semiconductor Solutions）：该板块贡献了公司FY2025财年约58%的营收，是AI增长的核心引擎。 定制加速器（Custom Accelerators / XPU）：服务于超大规模云服务商的AI ASIC业务。博通利用其IP核为Google、Meta等客户设计专用的AI训练和推理芯片。 网络（Networking）：包括以太网交换芯片Tomahawk、路由芯片Jericho和网卡/计算卸载芯片Thor系列，直接受益于AI数据中心对高带宽互联的需求。 无线（Wireless）：主要为苹果等消费电子企业提供Wi-Fi、蓝牙和射频前端模组（FBAR），是稳定的现金牛业务。 服务器与存储连接（Server & Storage Connectivity）：包括PCIe交换芯片（PCIe Gen 5/6/7 Switch）、Retimer芯片以及HBA/RAID存储控制器。 宽带与工业（Broadband & Industrial）：包括光纤接入（PON）、电缆调制解调器（DOCSIS）以及用于工业自动化的光耦合器和编码器等。2）基础设施软件（Infrastructure Software）：该板块在FY2025财年营收占比达42%。其不仅提供了高额的经常性收入，还通过捆绑销售增强了对企业级客户的粘性。 VMware：私有云和混合云虚拟化平台，是企业构建AI私有云的核心底座。 Symantec：企业网络安全。 CA Technologies：IT运维管理（AIOps）和大型机软件。 Brocade：光纤通道存储网络交换机。尽管是硬件，但因其极高的软件壁垒和在金融/政府数据中心的不可替代性，常被归类于高毛利的基础设施业务中。从财务表现方面看，博通通过并购和运营优化，在半导体周期的波动中实现穿越式增长。 2019-2020（软件转型期）：公司收购CA和Symantec导致收入结构剧变。尽管半导体业务受周期性影响（2020年微跌），但软件业务的高毛利拉升了整体盈利能力。 2021-2022（缺芯潮与5G）：全球半导体短缺和5G手机换机潮推动半导体业务在2021-2022财年分别增长18%和27%。网络业务开始受益于云厂商的早期扩容。 2023（AI黎明）：尽管消费电子和企业存储疲软导致半导体增速放缓至9%，但生成式AI的需求开始爆发。AI ASIC业务开始抵消传统业务的下滑。 2024-2025（AI超级周期 + VMware）：这是博通历史上的转折点。VMware的并入使得基础设施软件业务收入翻倍。更重要的是，进入AI超级周期后，公司半导体收入呈现高速增长。截止2025年9月30日，博通前五大机构持股合计13.24亿股，占比28%。其中，Vanguard（先锋领航集团）、BlackRock（贝莱德集团）、State Street三家机构持股占比分别为10.14%、8.01%、3.92%，资本集团(Capital Group)旗下的两个基金合计持股占比近6%。2 AI ASIC（XPU）业务：定制芯片的黄金时代2.1 下游资本支出高企，推理驱动AI ASIC蓝海市场2.1.1 资本开支的晴雨表：北美科技巨头的算力军备竞赛随着大模型的算法复杂度和训练/推理体量不断增长，算力基础设施的供给水平将直接影响应用落地及其迭代的进程。反应各大互联网厂商对算力基础设施投入最直接的资本支出（Capex）指标近三年来也在不断攀升。2023年一季度至今，北美四大互联网公司（亚马逊、微软、谷歌、Meta）的Capex总额基本实现每季度均环比增长的态势，2025年三季度四家公司的Capex总额达到1133.2亿美元，同比和环比分别增长74.7%、18.2%，相比23Q1的体量增长了2.2倍。资本支出的结构方面，微软率先在本轮AI周期中加大投资力度，从23Q1的78亿美元增长至25Q3的349亿美元，增幅高达3.5倍；谷歌23Q3开始显著提升Capex力度，截至25Q3单季度支出近240亿美元；亚马逊24Q1开始扩张Capex，从24Q1的149亿美元提升至25Q3的351亿美元，目前其Capex体量与微软基本持平；Meta从24Q2开始显著上调Capex，25Q3其体量为193.7亿美元，同比增长1.1倍。2.1.2 推理驱动AI ASIC蓝海市场在人工智能的军备竞赛中，通用GPU虽然占据了早期训练市场的统治地位，但随着推理需求的爆发，全球科技巨头正在大规模转向自研定制芯片AI ASIC。超大规模云服务商正在构建自己的芯片护城河，以追求极致的TCO（总拥有成本）和供应链安全。科技巨头的业务模型、应用场景等很多通过自身的云来承载，每个云承载了独特的应用和商业模型，包括内部应用（比如搜索引擎、社交媒体等）、SaaS服务（比如AI聊天机器人、Copilot等）、IaaS服务等。ASIC 可以适应不同的业务场景和商业模式的需求：1）内部工作负载的架构优化；2）更低的功耗，更低的成本；3）为AI工作负载定制的内存和I/O架构。训练集群对AI加速计算芯片的需求已提升到十万卡级别。而在推理阶段，由于计算量与业务和应用密切相关，单个推理集群对加速计算芯片的需求低于训练集群，但推理集群的部署数量要远多于训练集群。AI算力集群特别是推理集群对加速计算芯片的庞大需求，是AI ASIC快速成长的核心驱动力。与GPU相比，ASIC针对特定算法和应用进行优化设计，在特定任务上的计算能力强大，例如在某些AI深度学习算法中实现高效的矩阵运算和数据处理。GPU具有强大的并行计算能力，拥有众多计算核心，可同时处理多个任务，在通用计算和图形处理方面表现出色，适用于大规模的数据并行计算，如科学计算、图形渲染、视频处理等；但GPU在某些特定任务上的计算效率可能不如ASIC。ASIC在处理特定任务时，能实现高吞吐量，数据处理速度快，可快速完成大量的数据处理工作。GPU具有较高的带宽和并行处理能力，在图形处理和通用计算中能实现较高吞吐量，但在处理一些复杂、非图形相关的特定任务时，其吞吐量可能会受到一定限制。ASIC在绝对算力和片间互联方面普遍低于AI GPU，但ASIC的服务器间互联由于采用以太网为主，具有生态开放、低成本等优势。2.2 全球ASIC龙头，丰富的IP储备+XPU平台工程经验构建ASIC护城河博通是全球AI ASIC的龙头企业。公司广泛的IP储备可为其XPU (ASIC)产品线赋能，其IP产品主要分为4类：计算，存储，网络I/O，封装。其中SerDes、基于AI优化NICs、高端封装、交换机、CPO、内存等IP处于行业领先水平。SerDes（串行器/解串器）技术是芯片间数据传输的咽喉，博通拥有全球领先的SerDes IP，这是构建高性能AI集群的物理层基础。博通在IP相关领域投入了至少30亿美元研发费用。 计算：设计流和性能优化 存储：HBM PHY，内存整合和性能 网络IO：架构实现，Chiplets软硬一体化解决方案 封装：2.5D/3D封装，硅光架构和实现，垂直整合等博通充分利用已经布局完成的XPU平台工程，实现了业界最快的ASIC产品落地时间。XPU平台涵盖了公司经过10年ASIC经验优化的AI设计流程、AI IP、SoC封装等一体化解决方案。ASIC产品设计阶段耗费7-9个月的联合开发时间，再用3个月左右的时间完成产品的生产和产能爬坡。博通与客户在架构阶段就展开了深度的战略合作。在ASIC设计启动的前几年，完成了关键组件的早期IP投资；并且为后两代ASIC同时进行技术、IP和封装投资。随着算力性能增长、网络和内存带宽提升、对电力输送、热完整度、机械可靠性等要求的升级，ASIC的复杂度在加深。博通积极响应了客户对更复杂的ASIC的需求。博通的ASIC客户名单几乎涵盖全球AI领域的核心公司。博通是谷歌TPU的外部核心设计合作伙伴，双方的合作从第一代TPU v1持续至今，已迭代至TPU v7产品。博通还与Meta深度绑定，为其设计自研的MTIA芯片。此外，OpenAI也是博通新晋的超级客户。2.3 顶级朋友圈：赋能全球AI巨头的自研芯片之路2.3.1 谷歌：AI ASIC的鼻祖与领航者谷歌拥有成熟的软件栈（JAX/TensorFlow），其TPU集群通过光路交换（OCS）互联，不仅降低了对英伟达GPU的依赖，更构建了难以复制的系统级优势。谷歌早在2015年推出了TPU v1，截止到目前已经迭代到TPU v7，广泛应用于谷歌Gemini模型的训练和推理。针对推理优化的TPU v7相比TPU v5峰值算力提升了4倍，HBM容量和带宽分别提升了1倍和1.7倍。谷歌专为大规模AI设计的Ironwood TPU，其突破性创新包括：单SuperPod节点最多可容纳9216颗芯片，使用OCS（光电路交换机）共享内存；可直接寻址的共享HBM内存容量为1.77PB；FP8精度下，单SuperPod性能可扩展至42.5 EFLOPS；每瓦性能是Trillium TPU的2倍；第三代液冷基础设施；用于嵌入和集体卸载的第四代SparseCore。Ironwood是谷歌首款双计算die TPU，其采用8层HBM3e内存，提供192GB容量和7.3TB/s带宽，并集成了更多可靠性和安全性功能，支持加密计算。Ironwood在设计中AI被用于设计ALU电路和优化布局。Ironwood支持单SuperPod最多扩展至9216个芯片，同时可横向扩展到数十个SuperPod。Ironwood TPU超级集群由144个机架组成，每机架包含64个TPU（分布在16个托盘上，每个托盘4个TPU），扩展至9216个TPU。TPU v4算力集群采用3D环面架构和OCS，提供高速的网络连接，增强拓展性与互联效率。在TPUv4的架构中，每64颗TPU v4芯片组成4x4x4的立方体，每个CPU配备4颗TPU v4，64颗TPU v4和16颗CPU放入一个机架，形成一个模块。一个模块有6个面的光路链接，每个面有16个链接，单模块共有96个光路连接到OCS。为了提供3D环面的环绕链接，对面的链接必须连接到同一个OCS。每个模块连接48个OCS，最终实现所需的4096个TPU v4芯片互联。Ironwood将一个Pod内的芯片数量相比TPU v4增加了1倍，实现9216个Ironwood TPU芯片互联。OCS支持将Pod配置成不同大小的矩形棱柱体，可以丢弃失效节点，通过从检查点恢复，重新配置切片以使用其他机架。从Gemini 2.0开始，TPU的训练和推理生态被逐步验证。如按8个芯片计算，H100的A3 High实例On-demand价格是单芯片88.49美元/小时，TPU v5p的价格为单芯片33.6美元/小时（4.2美元/小时×8），TPU v5p的价格约为H100的38%。对客户而言，TPU拥有显著的推理成本优势。2.3.2 Meta：助力MTIA架构演进与推理优化Meta 在2023年部署了MTIA 1，主要用于推荐系统（DLRM）。2024年发布的MTIA 2采用台积电5nm工艺，INT8精度下的稠密算力达354 TFLOPS，是上一代算力的3.5倍，配备128GB的LPDDR5内存，内存带宽205GB/s，设计最大功耗90W TDP，MTIA 2专门优化了Meta的广告排名和推荐算法。下一代 MTIA 3能够处理生成式AI的训练任务，Meta计划在2026-2027年实现对GPU训练集群的部分替代。芯片架构方面，MTIA 2内部包含加速器、片上和片外存储以及互联结构。AI加速器由8x8的处理单元网格（PE，processing element）组成，PE基于RISC-V内核，PE彼此互联，可作为一个整体运行任务，也可以独立处理任务。片上内存SRAM容量256MB，SRAM带宽为2.7TB/s，每个PE内存容量为384KB，PE带宽为1 TB/s。每个加速器使用PCIe Gen5 x8主机接口。MTIA 2加速器模块在配置方面，每张卡包含了2个MTIA芯片，每个MTIA都可以使用PCIe Gen5 x8接口，单模块共x16接口（2 PCIe Gen5 x16）。MTIA一个机架系统包含(2×MTIA芯片)×(12×模组)×(3×机箱)，相当于每个机架系统搭载了72颗MTIA芯片。MTIA 2软件堆栈与PyTorch 2.0、TorchDynamo、TorchInductor完全集成，致力于提高开发者编程效率。MTIA v2的低级编译器从前端获取输出，生成高效且特定于设备的代码。下方是运行时堆栈，负责与驱动程序/固件接口，最后，运行时与驱动程序交互。Meta创建了Triton-MTIA编译器后端为芯片硬件生成高性能代码，Triton用于编写ML计算内核，极大提高了开发人员效率。基于MTIA平台加速后的Meta推荐模型的效率得到提升，在大型片上SRAM的加持下，低复杂度（LC）模型具有开箱即用的优势，而高复杂度（HC）模型在4-6个月内性能提高了2倍以上。2.3.3 OpenAI：从算法巅峰向硬核制造的战略跨越为了实现 AGI，OpenAI对算力规模的需求远超现有供应链的承载能力。2025年，OpenAI 与博通确立了战略合作关系，将共同开发包含Scale-up和Scale-out的加速器和博通以太网解决方案的系统。通过设计自己的芯片和系统，OpenAI可以将开发前沿模型和产品中所学的知识经验直接嵌入硬件中，解锁新的能力和智能层次。这一项目规模宏大，博通计划于2026年下半年开始部署AI加速器和网络系统机架，目标在2029年前部署10 GW的定制算力集群。3 网络技术全栈布局，构建端到端网络解决方案如果说GPU/ASIC是AI的大脑，那么网络就是AI的神经系统。随着模型参数的扩大，必须依靠成千上万个芯片协同工作才能更高效地完成模型的训练和推理。此时，网络的带宽和延迟决定了整个集群的效率。博通在网络芯片领域拥有近乎垄断的地位，其产品线覆盖了AI网络的所有层级。 Tomahawk系列（交换芯片）：专为高带宽、低延迟的数据中心主干网络设计。 Tomahawk 5：提供51.2 Tbps带宽，支持800G端口。 Tomahawk 6：2025年发布，基于3nm工艺，带宽翻倍至102.4 Tbps，支持1.6T光模块接口，是连接下一代GPU的关键。 Jericho系列（路由/Fabric芯片）：专为大规模AI集群的Scale-Up网络设计，具有深缓冲区（Deep Buffer）和虚拟输出队列（VOQ）技术。 Jericho3-AI：博通的杀手锏。针对AI流量特征（突发性、长流）进行了优化，在负载均衡和作业完成时间（JCT）上比英伟达的InfiniBand快10%。 Thor系列（网卡/NIC）：位于服务器端的网络接口芯片，负责将数据从CPU/GPU搬运到网络中，Thor 2支持400G带宽和RDMA卸载。随着集群规模扩大到数万张卡，博通主导的超以太网联盟（Ultra Ethernet Consortium）推出了基于RoCEv2 (RDMA over Converged Ethernet) 的新一代以太网技术，或重塑大规模AI集群的通信标准。Meta、Oracle等选择了基于博通芯片的以太网方案，证明了经过优化的以太网完全能够胜任最苛刻的AI训练任务。4 盈利预测与投资建议关键假设：假设1：谷歌、OpenAI、Meta等客户自研芯片需求提升，带动ASIC和配套的网络产品出货显著增长，非AI半导体逐步走出周期底部，半导体业务未来三年收入复合增速达57%。假设2：基础设施软件客户粘性高，随着对VMware整合的深入，基础设施软件业务稳步增长，未来三年收入复合增速8%。假设3：ASIC业务占比增加，VMware整合深入带动软件业务毛利率小幅提升，公司整体毛利率稳定在67%左右。基于以上假设，我们预测公司2026-2028财年分业务收入如下表：我们选取了半导体产业链的4家可比公司，2025-2027年平均估值为126倍PE、42倍、27倍PE。公司是全球AI ASIC龙头企业，AI推理带动ASIC需求爆发，预计公司未来三年Non-GAAP净利润年复合增速为42.8%，当前股价下公司2027财年（对应自然年2026年11月~2027年10月）对应估值为22倍PE（Non-GAAP），PEG为0.5，相比同业平均水平，公司显著低估。结合业绩增速，给予公司2027财年30倍PE（Non-GAAP），目标价469.95美元，对应市值2.2万亿美元。首次覆盖，给予“买入”评级。","kind":"news","is_publish_news":true,"is_publish_highlight":false,"is_publish_live":false,"is_publish_wemedia":null,"editions":null,"column":"","sentiment":"2","news_tag":"dataReport","news_rank":0,"symbols":[],"gpt_button":0,"need_auth":false,"code":"91000000","status":"200"},"commentList":[],"hasMoreComments":false,"newsSizeData":{"likeSize":0,"commentSize":0,"repostSize":0,"favoriteSize":1,"likeStatus":false,"favoriteStatus":false},"isCrawlerRequest":true}