vivant的个人主页 - 老虎社区

vivant

vivant

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IP属地：重庆

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0勋章

02-07

$阿里巴巴(BABA)$ 阿里真的已经很努力了又卖投资又大股东、创始人回购又努力提前发财报但是越努力却感觉越无力一声叹息。作为一个前员工还是祝好吧

2018-07-19

$好未来(TAL)$ 居然让我买到今天最低针尖价...

2023-12-21

有人说英伟达是卖铲子的，要到顶了说这话的人根本就不了解英伟达。现在越来越深刻地了解到什么叫做一家公司的基因。基因就是人性。你是一个怎样的人决定了你的公司会是什么样（当然需要直接管理的那种，退位的曾经创始人不算）黄老师在一开始还在英特尔时期就通过英特尔和cxle知道了建立一个环境有多么重要，所以他早就建好了运动场和准备好了矿泉水，来服务来这跑步的人。你看到他卖水赚钱，就去卖水当你没看到人家之所以必须要来这里买水，除了水好以外，还因为这方圆十里，就这么一个好的运动场。你说你是去呢还是去呢 #英伟达$英伟达(NVDA)$ $博通(AVGO)$ $英特尔(INTC)$

2020-03-07

为什么bili的put这么坚挺…

02-20

$英伟达(NVDA)$ 感觉就是700 上下了情绪不高

2023-12-19

写的太好了笑死了

NVLink，英伟达的另一张王牌

02-23

今年一定是国内新能源车企加速破产的一年

02-23

今天超微的成交量不大感觉还有一波下跌谨慎观察👀

02-08

$逸仙电商(YSG)$ 中国的新消费品牌就是个笑话

02-06

超微电脑真的是要疯这还得了

02-05

股市要赚钱，只有两个点 1. 尽可能多地拿到底部的筹码 2. 上涨时要拿得住，不要轻易交出有的人拿到了筹码拿不住可偏偏很多人拿得住顶部的筹码真奇怪...

02-03

十倍的nvda 不辜负！比上班强多了呜呜呜缅a还在泥地里哎

02-03

今天把我的650 call 的期权单子卖飞了不然赚好多以为他在650上不去了不过胆小落袋为安

01-26

不想$微软(MSFT)$ 他的copilot 根本登不上去 bug 很大感觉

01-26

不看好amd的财报$美国超微公司(AMD)$ 原因很简单他的芯片够好又便宜的话大家干嘛自己造？ sam 又不傻

01-26

英伟达必上650 $英伟达(NVDA)$ 英伟达(NVDA)$ 看多

01-24

我发现老虎的延时好严重同时开老虎和ib 价格不一样... 怀疑用老虎会被夹...

01-24

英伟达真的tmd吓死人了昨天卖了一个covered call 结果今天跳空高开... 真的绝了赶紧平仓...

01-16

英伟达牛逼！！上600 妥妥的

去老虎APP查看更多动态

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现在越来越深刻地了解到什么叫做一家公司的基因。 基因就是人性。 你是一个怎样的人决定了你的公司会是什么样（当然需要直接管理的那种，退位的曾经创始人不算） 黄老师在一开始还在英特尔时期就通过英特尔和cxle知道了建立一个环境有多么重要，所以他早就建好了运动场和准备好了矿泉水，来服务来这跑步的人。 你看到他卖水赚钱，就去卖水 当你没看到人家之所以必须要来这里买水，除了水好以外，还因为这方圆十里，就这么一个好的运动场。 你说你是去呢还是去呢 #英伟达<a href=\"https://laohu8.com/S/NVDA\">$英伟达(NVDA)$&nbsp;</a><a href=\"https://laohu8.com/S/AVGO\">$博通(AVGO)$&nbsp;</a><a href=\"https://laohu8.com/S/INTC\">$英特尔(INTC)$&nbsp;</a>","listText":"有人说英伟达是卖铲子的，要到顶了 说这话的人根本就不了解英伟达。 现在越来越深刻地了解到什么叫做一家公司的基因。 基因就是人性。 你是一个怎样的人决定了你的公司会是什么样（当然需要直接管理的那种，退位的曾经创始人不算） 黄老师在一开始还在英特尔时期就通过英特尔和cxle知道了建立一个环境有多么重要，所以他早就建好了运动场和准备好了矿泉水，来服务来这跑步的人。 你看到他卖水赚钱，就去卖水 当你没看到人家之所以必须要来这里买水，除了水好以外，还因为这方圆十里，就这么一个好的运动场。 你说你是去呢还是去呢 #英伟达<a href=\"https://laohu8.com/S/NVDA\">$英伟达(NVDA)$&nbsp;</a><a href=\"https://laohu8.com/S/AVGO\">$博通(AVGO)$&nbsp;</a><a href=\"https://laohu8.com/S/INTC\">$英特尔(INTC)$&nbsp;</a>","text":"有人说英伟达是卖铲子的，要到顶了 说这话的人根本就不了解英伟达。 现在越来越深刻地了解到什么叫做一家公司的基因。 基因就是人性。 你是一个怎样的人决定了你的公司会是什么样（当然需要直接管理的那种，退位的曾经创始人不算） 黄老师在一开始还在英特尔时期就通过英特尔和cxle知道了建立一个环境有多么重要，所以他早就建好了运动场和准备好了矿泉水，来服务来这跑步的人。 你看到他卖水赚钱，就去卖水 当你没看到人家之所以必须要来这里买水，除了水好以外，还因为这方圆十里，就这么一个好的运动场。 你说你是去呢还是去呢 #英伟达$英伟达(NVDA)$ $博通(AVGO)$ $英特尔(INTC)$","images":[],"top":1,"highlighted":1,"essential":1,"paper":1,"likeSize":2,"commentSize":0,"repostSize":0,"link":"https://laohu8.com/post/254200425595040","isVote":1,"tweetType":1,"viewCount":4739,"authorTweetTopStatus":1,"verified":2,"comments":[],"imageCount":0,"langContent":"CN","totalScore":0},{"id":981355367,"gmtCreate":1583514416059,"gmtModify":1704350193684,"author":{"id":"3480645275647101","authorId":"3480645275647101","name":"vivant","avatar":"https://static.tigerbbs.com/3ab12bba42752e93c1e14c1dbda2b938","crmLevel":5,"crmLevelSwitch":0,"followedFlag":false,"idStr":"3480645275647101","authorIdStr":"3480645275647101"},"themes":[],"htmlText":"为什么bili的put这么坚挺…","listText":"为什么bili的put这么坚挺…","text":"为什么bili的put这么坚挺…","images":[],"top":1,"highlighted":1,"essential":1,"paper":1,"likeSize":0,"commentSize":1,"repostSize":0,"link":"https://laohu8.com/post/981355367","isVote":1,"tweetType":1,"viewCount":1306,"authorTweetTopStatus":1,"verified":2,"comments":[],"imageCount":0,"langContent":"CN","totalScore":0},{"id":276069052707080,"gmtCreate":1708429794972,"gmtModify":1708429796214,"author":{"id":"3480645275647101","authorId":"3480645275647101","name":"vivant","avatar":"https://static.tigerbbs.com/3ab12bba42752e93c1e14c1dbda2b938","crmLevel":5,"crmLevelSwitch":0,"followedFlag":false,"idStr":"3480645275647101","authorIdStr":"3480645275647101"},"themes":[],"htmlText":"<a href=\"https://laohu8.com/S/NVDA\">$英伟达(NVDA)$&nbsp;</a> 感觉就是700 上下了 &nbsp;情绪不高","listText":"<a href=\"https://laohu8.com/S/NVDA\">$英伟达(NVDA)$&nbsp;</a> 感觉就是700 上下了 &nbsp;情绪不高","text":"$英伟达(NVDA)$ 感觉就是700 上下了 情绪不高","images":[],"top":1,"highlighted":1,"essential":1,"paper":1,"likeSize":1,"commentSize":0,"repostSize":0,"link":"https://laohu8.com/post/276069052707080","isVote":1,"tweetType":1,"viewCount":1924,"authorTweetTopStatus":1,"verified":2,"comments":[],"imageCount":0,"langContent":"CN","totalScore":0},{"id":253765977497848,"gmtCreate":1702990986496,"gmtModify":1702990988154,"author":{"id":"3480645275647101","authorId":"3480645275647101","name":"vivant","avatar":"https://static.tigerbbs.com/3ab12bba42752e93c1e14c1dbda2b938","crmLevel":5,"crmLevelSwitch":0,"followedFlag":false,"idStr":"3480645275647101","authorIdStr":"3480645275647101"},"themes":[],"htmlText":"写的太好了 &nbsp;笑死了","listText":"写的太好了 &nbsp;笑死了","text":"写的太好了 笑死了","images":[],"top":1,"highlighted":1,"essential":1,"paper":1,"likeSize":1,"commentSize":0,"repostSize":0,"link":"https://laohu8.com/post/253765977497848","repostId":"2392998471","repostType":2,"repost":{"id":"2392998471","kind":"news","pubTimestamp":1702945800,"share":"https://www.laohu8.com/m/news/2392998471?lang=&edition=full","pubTime":"2023-12-19 08:30","market":"us","language":"zh","title":"NVLink，英伟达的另一张王牌","url":"https://stock-news.laohu8.com/highlight/detail?id=2392998471","media":"远川科技评论","summary":"互联，AI芯片的下半场。","content":"<html><head></head><body><p>美国商务部的口风越来越紧，逼得黄式刀法重出江湖：多方证实，英伟达即将推出三款特供版GPU，由于出口管制，性能最强的H20，相较H100算力也大幅缩水80%。</p><p style=\"text-align: justify;\">算力被限制死，英伟达也只能在其他地方做文章。H20的最大亮点落在带宽：</p><p style=\"text-align: justify;\">带宽达到与H100持平的900G/s，为英伟达所有产品中最高。较A100的600G/s，和另外两款特供芯片A800和H800的400G/s大幅提高。</p><p style=\"text-align: justify;\"><strong>阉割算力，提升带宽。看似割韭菜，实则含金量不低。</strong></p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/50002a5e03fedb756fec9a8b42314602\" alt=\"H20踩着红线免受制裁\" title=\"H20踩着红线免受制裁\" tg-width=\"1080\" tg-height=\"1051\"/><span>H20踩着红线免受制裁</span></p><p style=\"text-align: justify;\">简单来说，带宽的大小决定了单位时间向GPU传输的数据总量。<strong>考虑到人工智能对数据吞吐能力病态的要求，如今衡量GPU的质量，带宽已经成为算力之外最重要的指标。</strong></p><p style=\"text-align: justify;\">另一方面，云服务公司和大模型厂商不是只买几颗芯片，而是一次性采购几百上千张组成集群，芯片之间的数据传输效率也成了迫在眉睫的问题。</p><p style=\"text-align: justify;\">GPU和GPU之间的数据传输问题，让英伟达在<strong>芯片算力</strong>、<strong>CUDA生态</strong>之外的另一张王牌浮出了水面：<strong>NVLink</strong>。</p><p style=\"text-align: justify;\"><strong>数据传输，算力的紧箍咒</strong></p><p style=\"text-align: justify;\">要理解NVLink的重要性，首先要了解数据中心的工作原理。</p><p style=\"text-align: justify;\">我们平时玩游戏，一般只需要一块CPU加一块GPU。但训练大模型，需要的是成百上千个GPU组成的“集群”。</p><p style=\"text-align: justify;\">Inflection曾宣称他们正在构建的AI集群，包含多达22000张 NVIDIA H100。按照马斯克的说法，GPT-5的训练可能需要3万到5万张H100，虽然被Altman否认，但也可以窥见大模型对GPU的消耗。</p><p style=\"text-align: justify;\">特斯拉自家的超算Dojo ExaPod，就是由多个Cabinet机柜组成，每个机柜里有多个训练单元，每个训练单元都封装了25个D1芯片。一整台ExaPod包含3000个D1芯片。</p><p style=\"text-align: justify;\">但在这种计算集群中，会遇到一个严峻的问题：<strong>芯片彼此独立，如何解决芯片之间的数据传输问题？</strong></p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/185383e60753a0cb967d1525e25d00c9\" alt=\"特斯拉的超算ExaPOD\" title=\"特斯拉的超算ExaPOD\" tg-width=\"1080\" tg-height=\"563\"/><span>特斯拉的超算ExaPOD</span></p><p style=\"text-align: justify;\">计算集群执行任务，可以简单理解为CPU负责下达命令，GPU负责计算。这个过程可以大概概括为：</p><p style=\"text-align: justify;\"><strong>GPU先从CPU那里拿到数据——CPU发布命令，GPU进行计算——GPU计算完成，将数据回传给CPU。如此循环往复，直到CPU汇总所有计算结果。</strong></p><p style=\"text-align: justify;\">数据一来一回，传输效率就至关重要。如果有多个GPU，GPU之间还要分配任务，这又涉及到数据的传输。</p><p style=\"text-align: justify;\"><strong>所以，假设一家公司买下100颗H100芯片，它拥有的算力并不是100颗芯片的算力简单相加，还要考虑到数据传输带来的损耗。</strong></p><p style=\"text-align: justify;\">一直以来，数据传输的主流方案是的<strong>PCIe</strong>。2001年，英特尔提出以PCIe取代过去的总线协议，联手20多家业内公司起草技术规范，英伟达也是受益者。但时至今日，PCIe的缺点变得越来越明显。</p><p style=\"text-align: justify;\"><strong>一是数据传输效率被算力的提升远远甩在了后面。</strong></p><p style=\"text-align: justify;\">从2001年到2017年，运算设备的算力提高了5000倍。同期，PCIe迭代到4.0，带宽（单通道）只从250MB/s提高到2GB/s，提升只有8倍。</p><p style=\"text-align: justify;\">算力的传输之间的巨大落差，导致效率大幅降低。就像摆了一桌满汉全齐，餐具就给一个挖耳勺，怎么吃都不痛快。</p><p style=\"text-align: justify;\"><strong>二是人工智能暴露了PCIe的设计缺陷。</strong></p><p style=\"text-align: justify;\">在PCIe的设计思路里，GPU之间的数据传输都必须经过CPU。换句话说就是GPU1想和GPU2交换数据，都得由CPU来分发。</p><p style=\"text-align: justify;\">这在以前不是什么问题，但人工智能主打一个大力出奇迹，计算集群里GPU数量迅速膨胀。如果每个GPU都要靠CPU传话，效率就大大降低了。用大家很熟悉的话来形容，就是“你一个人耽误一分钟，全班同学就浪费了一个小时”。</p><p style=\"text-align: justify;\">大幅提高PCIe的带宽，不太符合英特尔挤牙膏上瘾的人设。大幅提高CPU的处理能力是个办法，但英特尔要是有这个本事，英伟达和AMD活不到今天。</p><p style=\"text-align: justify;\">于是，深感时不我待的英伟达动了另起炉灶的心思。</p><p style=\"text-align: justify;\">2010年，英伟达推出GPU Direct shared memory技术，通过减少一次复制的步骤，加快了GPU1-CPU-GPU2的数据传输速度。</p><p style=\"text-align: justify;\">次年，英伟达又推出GPU Direct P2P技术，直接去掉了数据在CPU中转的步骤，进一步加快传输速度。</p><p style=\"text-align: justify;\">只是这些小幅度的技术改良，都基于PCIe方案。</p><p style=\"text-align: justify;\">和CUDA一样，PCIe的竞争力在于生态。所谓“生态”，核心就是“大家都在用你凭什么搞特殊”。由于大多数设备都采用PCIe接口，就算英伟达想掀桌子，其他人也得掂量掂量兼容性问题。</p><p style=\"text-align: justify;\">转折点出现在2016年，AlphaGo 3:0战胜李世石，GPU一夜之间从荼毒青少年的游戏显卡变成了人工智能的科技明珠，英伟达终于可以光明正大的进村了。</p><p style=\"text-align: justify;\"><strong>NVLink，解开PCIe封印</strong></p><p style=\"text-align: justify;\">2016年9月，IBM发布Power 8服务器新版本，搭载英伟达GPU：</p><p style=\"text-align: justify;\">两颗Power 8 CPU连接了4颗英伟达P100 GPU，其中数据传输的纽带从PCIe换成了英伟达自研NVLink，带宽高达80G/s，通信速度提高了5倍，性能提升了14%。</p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/426133a492edbfb232476633f83eae59\" alt=\"Power8+P100架构\" title=\"Power8+P100架构\" tg-width=\"1080\" tg-height=\"840\"/><span>Power8+P100架构</span></p><p style=\"text-align: justify;\">同时，NVLink还实现了GPU-GPU之间的直接传输，不带PCIe玩了。</p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/3e184c3620eda553070a650c5770bf74\" alt=\"\" title=\"\" tg-width=\"1080\" tg-height=\"1226\"/></p><p style=\"text-align: justify;\">2017年，基于Power8+P100的模型在22K的ImageNet数据集上实操了一把，识别准确率达到33.8%，虽然准确率相比前一年只提高了4%，但训练时间从10天大幅缩短到了7小时。</p><p style=\"text-align: justify;\">小试牛刀效果不错，老黄也不准备再装了。</p><p style=\"text-align: justify;\">从2017年的Volta架构开始，英伟达给每一代GPU都搭配了基于NVLink方案的NVSwitch芯片，用来处理GPU之间的数据传输。</p><p style=\"text-align: justify;\">NVLink和NVSwitch的关系，可以简单理解为：<strong>NVLink是一种技术方案，NVSwitch和NVLink交换机都是这种方案的载体。</strong></p><p style=\"text-align: justify;\">目前最新的DGX H100服务器中，每台服务器拥有8个H100 GPU、4个NVSwitch芯片相互连接。</p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/3e043ccaab8fdcd7109bab93101df6fc\" alt=\"带有标注的NVSwitch芯片裸片\" title=\"带有标注的NVSwitch芯片裸片\" tg-width=\"1080\" tg-height=\"312\"/><span>带有标注的NVSwitch芯片裸片</span></p><p style=\"text-align: justify;\">在DGX H100服务器发布的同时，英伟达还发布了搭载两个NVSwitch芯片的NVLink交换机，用来处理DGX H100服务器之间的数据传输。</p><p style=\"text-align: justify;\">也就是说，NVLink不仅负责DGX服务器内部8个GPU的连通，也负责整个服务器之间每个GPU的数据传输。</p><p style=\"text-align: justify;\">按照英伟达的设计，一个H100 SuperPOD系统，会用到32台服务器总共256个H100 GPU，算力高达1EFlops。每套系统搭配18台NVlink交换机，加起来就是128个NVSwitch芯片。</p><p style=\"text-align: justify;\"><strong>如上文所说，一个集群的算力并不是每个GPU算力的简单相加，服务器间的数据传输效率是主要的制约因素。当集群的规模越来越大，NVLink的重要性也就越来越强。</strong></p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/894d54396bcf35135363ac5730923907\" alt=\"\" title=\"\" tg-width=\"1080\" tg-height=\"898\"/></p><p style=\"text-align: justify;\">NVLink渐成气候，老黄的野心也逐渐成型：和PCIe拉帮结派搞生态不同，NVLink必须绑定英伟达的芯片使用。当然，考虑到PCIe的既定生态，H100系列中也有多个支持PCIe的版本。</p><p style=\"text-align: justify;\">为了扩张自己的势力范围，英伟达还推出了基于Arm架构的Grace服务器CPU，用英伟达的CPU+英伟达的GPU+英伟达的互联方案，捆绑在一起，统一数据中心市场。</p><p style=\"text-align: justify;\">有了这一层铺垫，就不难理解H20的杀伤力。</p><p style=\"text-align: justify;\">虽然算力被砍了一大截，应付不了大参数的模型训练，但H20本身的高带宽和NVLink的加持，可以组成更大的集群，在一些小参数模型的训练和推理上，反而更具性价比。</p><p style=\"text-align: justify;\"><strong>在英伟达的示范下，AI的内卷也算力转向了互联技术。</strong></p><p style=\"text-align: justify;\"><strong>互联，AI芯片的下半场</strong></p><p style=\"text-align: justify;\">2023年11月，AMD发布预告已久的MI300系列，直接对标英伟达H100。</p><p style=\"text-align: justify;\">发布会上，除了例行的纸面算力比较外，Lisa Su重点强调了MI300带宽上的遥遥领先：MI300X带宽高达5.2TB/s，比H100还要高1.6倍。</p><p style=\"text-align: justify;\">这是实话，不过得先挤挤水分。</p><p style=\"text-align: justify;\">Lisa Su用来与MI300X比较的是H100 SXM版，但性能更高的H100 NVL版通过NVLink集成两颗GPU带宽达到7.8TB/s，仍高于MI300X的。</p><p style=\"text-align: justify;\">但这足见AMD对带宽的重视程度，以及AI芯片竞争的新焦点：<strong>互联技术</strong>。</p><p style=\"text-align: justify;\">英伟达发布NVLink的几个月后，AMD就推出了高速互联技术Infinity Fabric，提供CPU-CPU之间最高到512GB/s的带宽，后又扩展到GPU-GPU、CPU-GPU互联。</p><p style=\"text-align: justify;\">看着两大竞争对手甩开带宽的包袱放飞自我，英特尔作为PCIe的带头大哥，自然心情复杂。</p><p style=\"text-align: justify;\">2019年，英特尔联手戴尔、惠普等推出新的互联标准CXL，本质与NVLink和Inifinity Fabric一样，都是为了摆脱带宽掣肘，2.0标准最高带宽可达到32GT/s。</p><p style=\"text-align: justify;\">英特尔的心机在于，由于CXL是基于PCIe扩展的，因此和PCIe接口兼容。也就是说，过去用PCIe接口的设备可以“无痛”改用CXL，生态大法又立了大功。</p><p style=\"text-align: justify;\"><strong>芯片巨头围绕互联技术斗得正欢，转而自研芯片的AI大厂，也在解决互联问题。</strong></p><p style=\"text-align: justify;\">谷歌在自家TPU上采用了自研的光电路交换机技术（OCS），甚至还自研了光路开关芯片Palomar，只为了提高数据中心里几千颗TPU之间的通信速度。特斯拉也自己开发了通信协议，处理Dojo内部的数据传输。</p><p style=\"text-align: justify;\">回到本文开头，也正是这种差距，才让NVLink成为了英伟达的新“刀法”。</p><p style=\"text-align: justify;\">大模型所需的算力，并非国产AI芯片不可触及，但数据传输技术瘸腿依然会造成不可忽视的成本问题。</p><p style=\"text-align: justify;\">举一个不太严谨的例子，来帮助大家理解这个问题：</p><p style=\"text-align: justify;\">假设H20和国产AI芯片的单价都是1万元，一颗H20提供的算力是1，国产芯片提供的算力是2，但考虑到集群规模带来的算力损耗，由于NVLink的存在，H20的损耗是20%，国产芯片是50%，那么一个算力需求100的数据中心，需要125颗H20或是200颗国产芯片。</p><p style=\"text-align: justify;\"><strong>在成本上，就是125万和200万的差距。</strong></p><p style=\"text-align: justify;\">模型规模越大，数据中心所需的芯片越多，成本的差距就越大。要是黄仁勋狠狠心，刀法再犀利些，或许还可以卖出更低的价格。如果你是国内AIGC厂商的采购总监，你怎么选？</p><p style=\"text-align: justify;\"><strong>互联技术上的弱势，创造了英伟达的另一张王牌。</strong></p><p style=\"text-align: justify;\">按照当前的消息，原本11月发布的H20已经延后到明年第一季度，接受预定、出货时间也将顺势延后。延迟发布的原因并不确切，但在H20正式开售前，留给国产芯片的机会窗口，已经在倒计时了。</p><p style=\"text-align: justify;\">英伟达的伟大在于，它以高度的前瞻性，几乎以一己之力开辟了一条人工智能的高速公路。</p><p style=\"text-align: justify;\">而它的成功在于，黄仁勋在每一个你可能经过的车道，都提前修好了收费站。</p></body></html>","source":"lsy1571969218062","collect":0,"html":"<!DOCTYPE html>\n<html>\n<head>\n<meta http-equiv=\"Content-Type\" content=\"text/html; charset=utf-8\" />\n<meta name=\"viewport\" content=\"width=device-width,initial-scale=1.0,minimum-scale=1.0,maximum-scale=1.0,user-scalable=no\"/>\n<meta name=\"format-detection\" content=\"telephone=no,email=no,address=no\" />\n<title>NVLink，英伟达的另一张王牌</title>\n<style type=\"text/css\">\na,abbr,acronym,address,applet,article,aside,audio,b,big,blockquote,body,canvas,caption,center,cite,code,dd,del,details,dfn,div,dl,dt,\nem,embed,fieldset,figcaption,figure,footer,form,h1,h2,h3,h4,h5,h6,header,hgroup,html,i,iframe,img,ins,kbd,label,legend,li,mark,menu,nav,\nobject,ol,output,p,pre,q,ruby,s,samp,section,small,span,strike,strong,sub,summary,sup,table,tbody,td,tfoot,th,thead,time,tr,tt,u,ul,var,video{ 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}\n.symbol-link{font-weight:bold;}\n/* header{ border-bottom:1px solid #494756; } */\n.title{ margin:0 0 8px;line-height:1.3;color:#ddd; }\n.meta {color:#5e5c6d;font-size:13px;margin:0 0 .5em; }\na{text-decoration:none; color:#2a4b87;}\n.meta .head { display: inline-block; overflow: hidden}\n.head .h-thumb { width: 30px; height: 30px; margin: 0; padding: 0; border-radius: 50%; float: left;}\n.head .h-content { margin: 0; padding: 0 0 0 9px; float: left;}\n.head .h-name {font-size: 13px; color: #eee; margin: 0;}\n.head .h-time {font-size: 11px; color: #7E829C; margin: 0;line-height: 11px;}\n.small {font-size: 12.5px; display: inline-block; transform: scale(0.9); -webkit-transform: scale(0.9); transform-origin: left; -webkit-transform-origin: left;}\n.smaller {font-size: 12.5px; display: inline-block; transform: scale(0.8); -webkit-transform: scale(0.8); transform-origin: left; -webkit-transform-origin: left;}\n.bt-text {font-size: 12px;margin: 1.5em 0 0 0}\n.bt-text p {margin: 0}\n</style>\n</head>\n<body>\n<div class=\"wrapper\">\n<header>\n<h2 class=\"title\">\nNVLink，英伟达的另一张王牌\n</h2>\n\n<h4 class=\"meta\">\n\n\n2023-12-19 08:30 北京时间&nbsp;&nbsp;&nbsp;<a href=https://mp.weixin.qq.com/s/zpBM1WMzVsaP9z6_8BnXUA><strong>远川科技评论</strong></a>\n\n\n</h4>\n\n</header>\n<article>\n<div>\n<p>美国商务部的口风越来越紧，逼得黄式刀法重出江湖：多方证实，英伟达即将推出三款特供版GPU，由于出口管制，性能最强的H20，相较H100算力也大幅缩水80%。算力被限制死，英伟达也只能在其他地方做文章。H20的最大亮点落在带宽：带宽达到与H100持平的900G/s，为英伟达所有产品中最高。较A100的600G/s，和另外两款特供芯片A800和H800的400G/s大幅提高。阉割算力，提升带宽。看似割...</p>\n\n<a href=\"https://mp.weixin.qq.com/s/zpBM1WMzVsaP9z6_8BnXUA\">Web Link</a>\n\n</div>\n\n\n</article>\n</div>\n</body>\n</html>\n","type":0,"thumbnail":"https://static.tigerbbs.com/f51e0887617e5ccdb1f56ab9d6b7e088","relate_stocks":{"NVDA":"英伟达"},"source_url":"https://mp.weixin.qq.com/s/zpBM1WMzVsaP9z6_8BnXUA","is_english":false,"share_image_url":"https://static.laohu8.com/e9f99090a1c2ed51c021029395664489","article_id":"2392998471","content_text":"美国商务部的口风越来越紧，逼得黄式刀法重出江湖：多方证实，英伟达即将推出三款特供版GPU，由于出口管制，性能最强的H20，相较H100算力也大幅缩水80%。算力被限制死，英伟达也只能在其他地方做文章。H20的最大亮点落在带宽：带宽达到与H100持平的900G/s，为英伟达所有产品中最高。较A100的600G/s，和另外两款特供芯片A800和H800的400G/s大幅提高。阉割算力，提升带宽。看似割韭菜，实则含金量不低。H20踩着红线免受制裁简单来说，带宽的大小决定了单位时间向GPU传输的数据总量。考虑到人工智能对数据吞吐能力病态的要求，如今衡量GPU的质量，带宽已经成为算力之外最重要的指标。另一方面，云服务公司和大模型厂商不是只买几颗芯片，而是一次性采购几百上千张组成集群，芯片之间的数据传输效率也成了迫在眉睫的问题。GPU和GPU之间的数据传输问题，让英伟达在芯片算力、CUDA生态之外的另一张王牌浮出了水面：NVLink。数据传输，算力的紧箍咒要理解NVLink的重要性，首先要了解数据中心的工作原理。我们平时玩游戏，一般只需要一块CPU加一块GPU。但训练大模型，需要的是成百上千个GPU组成的“集群”。Inflection曾宣称他们正在构建的AI集群，包含多达22000张 NVIDIA H100。按照马斯克的说法，GPT-5的训练可能需要3万到5万张H100，虽然被Altman否认，但也可以窥见大模型对GPU的消耗。特斯拉自家的超算Dojo ExaPod，就是由多个Cabinet机柜组成，每个机柜里有多个训练单元，每个训练单元都封装了25个D1芯片。一整台ExaPod包含3000个D1芯片。但在这种计算集群中，会遇到一个严峻的问题：芯片彼此独立，如何解决芯片之间的数据传输问题？特斯拉的超算ExaPOD计算集群执行任务，可以简单理解为CPU负责下达命令，GPU负责计算。这个过程可以大概概括为：GPU先从CPU那里拿到数据——CPU发布命令，GPU进行计算——GPU计算完成，将数据回传给CPU。如此循环往复，直到CPU汇总所有计算结果。数据一来一回，传输效率就至关重要。如果有多个GPU，GPU之间还要分配任务，这又涉及到数据的传输。所以，假设一家公司买下100颗H100芯片，它拥有的算力并不是100颗芯片的算力简单相加，还要考虑到数据传输带来的损耗。一直以来，数据传输的主流方案是的PCIe。2001年，英特尔提出以PCIe取代过去的总线协议，联手20多家业内公司起草技术规范，英伟达也是受益者。但时至今日，PCIe的缺点变得越来越明显。一是数据传输效率被算力的提升远远甩在了后面。从2001年到2017年，运算设备的算力提高了5000倍。同期，PCIe迭代到4.0，带宽（单通道）只从250MB/s提高到2GB/s，提升只有8倍。算力的传输之间的巨大落差，导致效率大幅降低。就像摆了一桌满汉全齐，餐具就给一个挖耳勺，怎么吃都不痛快。二是人工智能暴露了PCIe的设计缺陷。在PCIe的设计思路里，GPU之间的数据传输都必须经过CPU。换句话说就是GPU1想和GPU2交换数据，都得由CPU来分发。这在以前不是什么问题，但人工智能主打一个大力出奇迹，计算集群里GPU数量迅速膨胀。如果每个GPU都要靠CPU传话，效率就大大降低了。用大家很熟悉的话来形容，就是“你一个人耽误一分钟，全班同学就浪费了一个小时”。大幅提高PCIe的带宽，不太符合英特尔挤牙膏上瘾的人设。大幅提高CPU的处理能力是个办法，但英特尔要是有这个本事，英伟达和AMD活不到今天。于是，深感时不我待的英伟达动了另起炉灶的心思。2010年，英伟达推出GPU Direct shared memory技术，通过减少一次复制的步骤，加快了GPU1-CPU-GPU2的数据传输速度。次年，英伟达又推出GPU Direct P2P技术，直接去掉了数据在CPU中转的步骤，进一步加快传输速度。只是这些小幅度的技术改良，都基于PCIe方案。和CUDA一样，PCIe的竞争力在于生态。所谓“生态”，核心就是“大家都在用你凭什么搞特殊”。由于大多数设备都采用PCIe接口，就算英伟达想掀桌子，其他人也得掂量掂量兼容性问题。转折点出现在2016年，AlphaGo 3:0战胜李世石，GPU一夜之间从荼毒青少年的游戏显卡变成了人工智能的科技明珠，英伟达终于可以光明正大的进村了。NVLink，解开PCIe封印2016年9月，IBM发布Power 8服务器新版本，搭载英伟达GPU：两颗Power 8 CPU连接了4颗英伟达P100 GPU，其中数据传输的纽带从PCIe换成了英伟达自研NVLink，带宽高达80G/s，通信速度提高了5倍，性能提升了14%。Power8+P100架构同时，NVLink还实现了GPU-GPU之间的直接传输，不带PCIe玩了。2017年，基于Power8+P100的模型在22K的ImageNet数据集上实操了一把，识别准确率达到33.8%，虽然准确率相比前一年只提高了4%，但训练时间从10天大幅缩短到了7小时。小试牛刀效果不错，老黄也不准备再装了。从2017年的Volta架构开始，英伟达给每一代GPU都搭配了基于NVLink方案的NVSwitch芯片，用来处理GPU之间的数据传输。NVLink和NVSwitch的关系，可以简单理解为：NVLink是一种技术方案，NVSwitch和NVLink交换机都是这种方案的载体。目前最新的DGX H100服务器中，每台服务器拥有8个H100 GPU、4个NVSwitch芯片相互连接。带有标注的NVSwitch芯片裸片在DGX H100服务器发布的同时，英伟达还发布了搭载两个NVSwitch芯片的NVLink交换机，用来处理DGX H100服务器之间的数据传输。也就是说，NVLink不仅负责DGX服务器内部8个GPU的连通，也负责整个服务器之间每个GPU的数据传输。按照英伟达的设计，一个H100 SuperPOD系统，会用到32台服务器总共256个H100 GPU，算力高达1EFlops。每套系统搭配18台NVlink交换机，加起来就是128个NVSwitch芯片。如上文所说，一个集群的算力并不是每个GPU算力的简单相加，服务器间的数据传输效率是主要的制约因素。当集群的规模越来越大，NVLink的重要性也就越来越强。NVLink渐成气候，老黄的野心也逐渐成型：和PCIe拉帮结派搞生态不同，NVLink必须绑定英伟达的芯片使用。当然，考虑到PCIe的既定生态，H100系列中也有多个支持PCIe的版本。为了扩张自己的势力范围，英伟达还推出了基于Arm架构的Grace服务器CPU，用英伟达的CPU+英伟达的GPU+英伟达的互联方案，捆绑在一起，统一数据中心市场。有了这一层铺垫，就不难理解H20的杀伤力。虽然算力被砍了一大截，应付不了大参数的模型训练，但H20本身的高带宽和NVLink的加持，可以组成更大的集群，在一些小参数模型的训练和推理上，反而更具性价比。在英伟达的示范下，AI的内卷也算力转向了互联技术。互联，AI芯片的下半场2023年11月，AMD发布预告已久的MI300系列，直接对标英伟达H100。发布会上，除了例行的纸面算力比较外，Lisa Su重点强调了MI300带宽上的遥遥领先：MI300X带宽高达5.2TB/s，比H100还要高1.6倍。这是实话，不过得先挤挤水分。Lisa Su用来与MI300X比较的是H100 SXM版，但性能更高的H100 NVL版通过NVLink集成两颗GPU带宽达到7.8TB/s，仍高于MI300X的。但这足见AMD对带宽的重视程度，以及AI芯片竞争的新焦点：互联技术。英伟达发布NVLink的几个月后，AMD就推出了高速互联技术Infinity Fabric，提供CPU-CPU之间最高到512GB/s的带宽，后又扩展到GPU-GPU、CPU-GPU互联。看着两大竞争对手甩开带宽的包袱放飞自我，英特尔作为PCIe的带头大哥，自然心情复杂。2019年，英特尔联手戴尔、惠普等推出新的互联标准CXL，本质与NVLink和Inifinity 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