趋势洞察：AI硬件算力、存力、运力三大核心赛道演进

每日经济新闻03-23

从算力层面来看，英伟达的芯片产品是行业发展的核心风向标。作为全球AI计算芯片领域占据绝对市场份额的龙头企业，英伟达的产品迭代节奏，对整个AI硬件行业具备极强的指引作用。　　从英伟达的产品迭代来看，芯片制程正在持续微缩。此前推动全球半导体产业制程持续迭代的核心动力是智能手机，比如此前台积电最先进的制程工艺，绝大部分产能都优先供应苹果公司。但现在，推动半导体制程迭代的核心主角已经发生变化，未来这一趋势...

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Capacity","themeId":null,"isJumpTheme":false,"ttsUrl":null,"symbols_score_info":{"3NVD.UK":0.6,"AIYY":0.6,"NVDY":0.6,"NVDD":0.6,"NVDW":0.6,"NVDU":0.6,"NVIW.SI":0.6,"NVD":0.6,"NVDS":0.6,"NVDB":0.6,"2NVD.UK":0.6,"NVDG":0.6,"NVD2.UK":0.6,"NVDA":1.96,"NVDQ":0.6,"SNVD.UK":0.6,"NVDS.UK":0.6,"NVII":0.6,"ANV":0.6,"NVD3.UK":0.6,"NVDX":0.6,"DIPS":0.6},"content_text":"从算力层面来看，英伟达的芯片产品是行业发展的核心风向标。作为全球AI计算芯片领域占据绝对市场份额的龙头企业，英伟达的产品迭代节奏，对整个AI硬件行业具备极强的指引作用。　　从英伟达的产品迭代来看，芯片制程正在持续微缩。此前推动全球半导体产业制程持续迭代的核心动力是智能手机，比如此前台积电最先进的制程工艺，绝大部分产能都优先供应苹果公司。但现在，推动半导体制程迭代的核心主角已经发生变化，未来这一趋势也将持续延续。　　为什么要持续推进制程升级？核心是让AI计算能够充分受益于摩尔定律带来的性能红利。但摩尔定律的迭代速度相对有限，通常需要两年多时间才能实现性能翻倍。当前半导体制程逐渐逼近1纳米物理极限，摩尔定律的迭代速度还在持续放缓。但与此同时，AI算力需求呈现爆发式增长，如何填补这一供需缺口，成为行业核心命题。　　在这样的行业背景下，行业选择通过扩大芯片部署规模来满足算力需求——当摩尔定律带来的单芯片性能提升跟不上算力需求增长时，就通过增加芯片投放量、扩建数据中心的方式，填补算力缺口。这也直接推动了算力芯片市场的高速增长。海外市场中，无论是英伟达的GPU，还是谷歌的TPU等AI算力芯片，均实现了极快的增长。以上就是算力层面的行业发展趋势。　　对于存储的情况，其最本质的需求就来自于AI的带动。因为我们知道，AI领域需要大量存储来存放数据，不管是GPU上面有很多的HBM——HBM就是一种高带宽内存。除了HBM以外，英伟达还在其机柜里面配置了大量DRAM、NAND等存储芯片，催生了对应的需求。现在随着agent的快速发展以及放量，尤其推理市场现在起量得这么快，我们知道很多用户，比如很多人可以看到自己的公司里面可能已经部署了一些大模型了。这种大模型在部署的时候，本身带有海量参数，每个大模型的参数规模都极为庞大，用来存储这些参数的存储需求也十分强劲。后续大模型还在不断地走强，那么从这一发展逻辑来看，后续存储的需求还会快速地增加。就目前来看，存储的涨价可能还会持续一段时间。所以存储的景气度，本质上还是来源于AI的计算需求在快速地增长，从而带来的价格上行效应。　　而运力，是我们今天想重点给大家分享的板块。其实对于运力而言，其组网逻辑主要分为两个部分。或者说我们可以想象，海外的云厂商要进行组网，是通过两个阶段实现的：第一个是scale up。　　这个scale up怎么理解？比如英伟达之前的A100以及H100的GPU，都是以八张GPU组成一个小型集群，再把这些服务器通过柜外连接，组成超大规模的数据中心。单台服务器内部的GPU互联，我们就可以看作是scale up，它原本就要把服务器里面的GPU连接成一个整体，服务器之内的互联就是scale up。我们从scale up的发展来看，单集群内的GPU数量在不断地增加。目前来看，单柜GPU数量已经增加到72卡，后续本次发布的576卡机柜将进一步刷新单机柜算力规模。这也是刚刚提到的，我们在GTC大会上看到的核心发布内容。　　Scale out是什么？就是要把这些服务器或者机柜组成一个超大规模的整体，只有组成完整的大规模集群、搭建起超大型数据中心之后，才能开展AI的训练以及推理工作。从目前来看，海外头部的云厂商，已经建成了十几万卡的GPU集群，整体发展速度非常快。　　那么我们在实现scale up和scale out的互联时，有哪些可用的技术方案？第一个是PCB，可能有的朋友知道PCB是什么。大家如果在家拆过闹钟或者其他电器，就会知道里面基本都会有一块PCB。PCB就是印刷电路板，被称作电子元器件之母，其核心作用就是让电路能够更高效、更稳定地运行。当然，不同PCB之间的规格差异极大，比如我们家里闹钟里面用的PCB，一平方米可能也就100多块钱。　　但是像英伟达这类企业，或是其他云厂商，用于GPU计算芯片的PCB，规格都非常高：基材目前基本都在M8级别，后续还会向M9甚至M10的材料迭代；层数也很高，普遍能达到二三十层，还带有一次DI的相关设计。所以AI场景所用的PCB单价极高，部分产品一平方米能卖到几万块钱，甚至更高。　　所以我们可以看到，相关PCB企业的业绩成长得也非常迅猛，从市场规模来看，也在快速地扩大，目前整个行业产能都处于供不应求的状态。大家可以看到，很多厂商目前都在快速地扩产，后续产能释放之后，对相关厂商的业绩提振是比较乐观的。当然这里只是给大家做行业情况的分析列示，不做任何个股推荐。　　第二种备选的互联方式是铜连接。大家可以看到，我列示了英伟达机柜背面的实拍情况，机柜背面这些银白色、一捆一捆的部件，就是英伟达目前在NVL72机柜中所用的铜缆，整体用量非常大。 　　资料来源：Nvidia　　这些铜缆的作用是什么？大家可以看下方的示意图，英伟达的机柜内部是一个个独立的托盘，每个计算托盘里放置4个GPU，一个机柜内有18个计算托盘，所以总共有72张GPU。计算托盘内部的GPU，主要通过PCB来连接。从内部结构来看，这个结构就相当于一个托盘，每个托盘里有两个超级芯片，每个超级芯片上搭载两个GPU，下方蓝色的部分就是两块PCB，也就是我们刚刚提到的高规格PCB。这里我们可以看到，PCB将4个GPU组成了一个完整的计算托盘。 　　下一步，就是要把所有的计算托盘，组成一个完整的机柜整体。而机柜内部的跨托盘互联，就是靠铜缆来实现的。基本就是通过铜缆连接NV Switch芯片，从而把机柜内所有72张GPU组成一个完整的互联整体。 　　资料来源：Nvidia　　但是铜缆也有自身的技术局限。比如目前单层网络下，用铜缆组成72卡集群已经接近物理极限，如果再叠加一层网络，最多也只能扩展到576卡，且这还只是技术层面的设想，未必能完全实现。因为铜本身存在诸多物理限制，我们知道铜是导体，而当下数据中心的功耗极高，功耗高的核心原因，是计算芯片的计算吞吐量极大，数据传输过程中的运算量极高，传输的电流更大、信号频率也更快。 　　资料来源：Nvidia　　如果用铜缆承担数据传输任务，极有可能出现过度发热的问题。持续发热就意味着能量损耗，长时间工作还会引发数据丢失等问题，这在AI训练与推理场景中是绝对无法容忍的。为什么？我们以训练为例，AI模型需要通过一次次的迭代，不断寻找到最优的参数集合，如果每次迭代都丢失一部分数据，最终很可能导致整个训练过程完全无效。所以铜缆的核心问题就在这里：它只能支持短距离的连接，传输距离过长就无法满足需求。从目前来看，铜缆最多只能支持几米的传输距离，而英伟达的互联网络已经升级到800G通信规格，后续还会进一步向1.6T升级，铜缆的局限性会持续凸显。　　当然，在英伟达机柜规格持续提升的过程中，柜内互联的整体市场规模也在不断扩大。在这个扩容的过程中，我们认为无论是PCB还是铜缆，后续都能获得可观的市场份额。但如果要让它们承担柜内全部的互联任务，尤其是本次发布的576卡机柜的互联需求，仅靠铜和PCB在技术上存在不小的难度。后续随着Feynman芯片的量产落地，将正式引入CPO技术，同步推进柜内可插拔光模块等光互联方案的落地应用。　　柜外的互联要如何实现？从英伟达的方案来看，其目前所用的网络架构，主要是叶脊网络。什么叫叶脊网络？大家可以看下方的拓扑图，叶脊网络的架构其实很简单，看起来就像一棵树，从树根不断长出更多的树枝，树枝再进一步分化出更细的枝丫，最终连接到叶片，就是这样的架构。 　　注：以上为根据英伟达资料推论所得，具体应用网络会在客户间有一定差异　　叶脊网络的逻辑就是如此，比如我们假设有四个输入端口，中间设置两台交换机，从这个架构来看，最基本的要求，就是任意一个输入端口到任意一个输出端口之间，至少要有一条通信通路。　　而所有的交换机不仅承担输入功能，也承担输出功能，如果我们把上面的拓扑图沿中线对折，就形成了英伟达所用的网络架构。大家可以看到，最上层是脊交换机，把下层的叶交换机连接成一个整体，叶交换机再进一步连接大量的GPU，最终组成数据中心的完整集群。　　这个架构中需要用到大量的光模块。光模块的应用场景在哪里？大家可以看到，拓扑图里标注的这些线路，其实都是光缆，而交换机侧和服务器侧处理信号时，用的都是电信号。所以信号要从一台交换机传输到另一台交换机，或是从一台服务器传输到另一台服务器，传输过程中需要多次完成光电转换，而这个核心功能，就是由光模块来实现的。　　我在这里给大家做了相关测算：在三层网络架构下，GPU与光模块的配比大约是1:6。当然这里是用同规格的光模块进行测算的，比如在Hopper架构芯片下，1:6的配比对应的是400G光模块；在Blackwell架构芯片下，1:6的配比对应的是800G光模块；下一代Rubin架构，对应的则是1.6T光模块。当然大家也知道，光模块有很强的灵活适配性，两个800G的光模块可以合并当作一个1.6T的光模块使用，一个1.6T的光模块也可以拆分成两个800G的光模块使用。所以这个配比在实际产品应用中可能会有小幅波动，但整体对应的带宽需求是固定的，因此三层网络下的配比基本维持在1:6左右。　　由此可以看到，随着下游芯片的快速放量，光模块的整体市场规模会迎来快速增长。后续来看，“光入柜内”会是整个市场预期最高的发展方向之一。为什么会有光入柜内这样的预期？刚才我已经给大家介绍过，实现scale up和scale out互联的技术方案，目前只有三种：铜连接、PCB，以及光模块。目前在英伟达的技术体系中，scale up对应的柜内互联，全部通过电连接实现，也就是铜缆和PCB；而柜外的长距离互联，基本都通过光模块来实现。　　往后面去看，正是因为Scale Up在不断地扩张，我们可以看到从8卡到72卡再到576卡，甚至后续规模可能会越来越大、覆盖的卡量越来越多。随着GPU的数量在不断地增加，对应的部署空间也在不断地扩大，对吧？所以大家可以看到，比如本次发布的576卡的机柜落地后，所需的部署空间就已经非常可观了。　　对于这么大的部署空间而言，不管是PCB还是铜缆，都会存在自身的固有局限。比如说PCB无法制造得过大，不可能做出一间屋子那么大的PCB板，因为其内部尤其是多层板的压合工艺，会受到诸多机械条件的限制。而铜缆则受自身物理特性的限制，无法实现长距离的信号传输。所以其实在scale up市场规模持续扩大的过程中，光技术后续很有可能会成为核心选择。　　在scale up规模持续扩大的过程中，我再强调一下：scale up的市场规模在不断扩张的过程中，首先PCB的成长性是比较强的。不管是本次发布的Rubin Ultra采用的正交背板，还是其技术持续升级、层数不断增加，亦或是所用材料的性能持续提升，PCB的成长性都十分强劲。我只是想说明，在scale up规模持续增长的过程中，PCB与铜缆很难独自承接市场的全部分额。所以往后面去看，光技术会是非常重要的选择。　　那么scale up这个市场到底有多大？从目前来看，即便我们给大家做相关测算，结果也并不具备足够的参考性。但从博通CEO的表述来看，该市场规模有5到10倍的增长空间。虽然产业链上有5到10倍的增长预期，但各家公司可能都有自身的发展诉求。从我们目前的观察来看，该市场规模至少会超过柜外市场。　　对于当前的可插拔光模块厂商而言，对应的市场机会大概是怎样的？我们知道，当前的scale out市场，大致可以理解为柜外市场，已经是可插拔光模块的核心主战场。而后续的scale up场景中，采用的可能是可插拔光模块，也可能是CPO。但无论是可插拔光模块还是CPO，对于当前的行业龙头厂商而言，都有充足的订单机会可以承接。即便是CPO，也有诸多相关方案正在验证阶段，后续有望拿到大量订单，所以大家也不用过度担心。　　总体从产业层面来看，算力、存力、运力仍在快速地成长。在这样的发展过程中，相关厂商都将迎来更多的成长机会。（文章来源：每日经济新闻）","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":"","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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