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$Hims & Hers Health Inc.(HIMS)$
X_Angel
2024-01-24
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","text":"","images":[],"top":1,"highlighted":1,"essential":1,"paper":1,"likeSize":0,"commentSize":0,"repostSize":0,"link":"https://ttm.financial/post/266491726684400","repostId":"1194806166","repostType":2,"repost":{"id":"1194806166","kind":"news","weMediaInfo":{"introduction":"提供即時金融資訊、行情、數據，旨在幫助投資者理解世界，做投資決策。","home_visible":1,"media_name":"老虎資訊","id":"1059071526","head_image":"https://community-static.tradeup.com/news/8274c5b9d4c2852bfb1c4d6ce16c68ba"},"pubTimestamp":1706092646,"share":"https://ttm.financial/m/news/1194806166?lang=en_US&edition=fundamental","pubTime":"2024-01-24 18:37","market":"hk","language":"zh","title":"Nvidia dominates the AI market but don't ignore these 3 challengers","url":"https://stock-news.laohu8.com/highlight/detail?id=1194806166","media":"老虎資訊","summary":"編譯自 The Motley Fool《Nvidia Dominates the AI Market, but Don't Ignore These 3 Challengers》，作者：Leo Sun重點 $英特尔$計劃將其新的面向AI的GPU與其數據中心GPU捆綁在一起。在最新一季，Nvidia的80％收入來自其數據中心芯片，而其遊戲業務僅為其收入的16％。Nvidia當前的客戶名單支持這種樂觀的觀點。但從長遠來看，其他芯片製造商可能會減輕Nvidia對AI市場的鐵腕。這些科技巨頭仍在購買大量 Nvidia 的 A100 GPU 來處理他們的人工智慧任務，但他們可以逐漸用他們的第一方晶片取代這些晶片以降低成本。","content":"<p><html><head></head><body><em>Nvidia Dominates the AI Market, but Don't Ignore These 3 Challengers, by The Motley Fool</em><strong><em>Leo Sun</em></strong></p><p>Focus</p><p><ul style=\"\"><li><a href=\"https://laohu8.com/S/INTC\">Intel</a>Plans to bundle its new AI-oriented GPU with its data center GPU.</p><p></li><li>AMD (<a href=\"https://laohu8.com/S/AMD\">Supermicro America</a>) is targeting the AI market with its latest Instinct GPU.</p><p></li><li>Nvidia (<a href=\"https://laohu8.com/S/NVDA\">Nvidia</a>) 's top customers are developing their own first-party AI chips.</p><p></li></ul>Investors should not assume that this hot chipmaker is invincible.</p><p>In the past five years, the price of Nvidia (NVDA) shares has soared by 1420%. Its growth is largely driven by the expansion of the artificial intelligence (AI) market, which has prompted more companies to install their high-end GPUs in their data centers.</p><p>In the latest quarter, 80% of Nvidia's revenue came from its data center chips, while its gaming business (which was its original growth engine before the surge in AI) accounted for only 16% of its revenue. Optimists believe that Nvidia's data center business will continue to grow as the company develops more advanced generative AI platforms and large language models, and its GPUs will maintain industry standards for handling these complex tasks.</p><p>Nvidia's current customer list supports this optimistic view. ChatGPT creators OpenAI, Microsoft (MSFT), Alphabet's (GOOG) (GOOGL) Google, Amazon,<a href=\"https://laohu8.com/S/META\">Meta Platforms</a>(META) and other tech giants use its chips to power their AI services. But in the long run, other chipmakers may ease Nvidia's iron fist on the AI market. Let's take a closer look at the three most likely challengers.</p><p><h2 id=\"id_4212173666\">1、<a href=\"https://laohu8.com/S/INTC\">Intel</a></h2></p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/2e1574dc9806dcc57a4c52d21632f121\" tg-width=\"560\" tg-height=\"240\"/></p><p>Nvidia's GPUs typically work alongside INTC's high-end Xeon CPUs in the data center to accelerate machine learning and AI tasks. For Intel, which still controls about 94% of the server CPU market, Nvidia's expansion into the data center market is an unwelcome intrusion, meaning its CPUs can't effectively handle AI tasks on their own.</p><p>CPUs still process one piece of data at a time, while GPUs process a large number of integer and floating-point numbers at the same time, making them more suitable for graphics and AI applications. To narrow this gap, Intel launched its own Xe GPU series in September 2020, with Xe-HP (High Performance) and Xe-HPC (High Performance Computing) directly targeting Nvidia's data center GPUs. This follows the launch of the new Max data center GPU last year.</p><p>Unlike the first-party foundry model, Intel outsourced GPU production to rival TSMC, which also makes Nvidia's high-end GPUs. These chips haven't gained much momentum yet, but Intel's continued expansion of the business-and its ability to bundle GPUs with Xeon CPUs-could ultimately hurt Nvidia.</p><p><h2 id=\"id_3599411369\">2、<a href=\"https://laohu8.com/S/AMD\">Supermicro America</a></h2></p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/75de36a77586f00d0966660d965e5656\" tg-width=\"560\" tg-height=\"240\"/></p><p>Nvidia's expansion into the data center market has enabled it to<a href=\"https://laohu8.com/S/AMD\">Supermicro America</a>(AMD), which controls a small portion of the discrete GPU market. However, AMD has been fighting back with its new Instinct data center chip for AI tasks.</p><p>It launched its first three Instinct GPUs (MI6, MI8, and MI25) in 2017, followed by more 7nm and 6nm MI chips over the next four years. In December last year, the company launched its latest MI300 Instinct chip, which is based on TSMC's 5nm and 6nm node architecture. In the latest base test, AMD's top-level MI300X defeated Nvidia's H100 in terms of processing capabilities and memory bandwidth, but Nvidia claims that the H100 is still superior to the MI300X when operating on optimized software.</p><p>AMD's AI ambitions could pose problems for Nvidia, especially if it continues its tradition of selling chips at lower prices. Companies such as Microsoft, Meta, Oracle, Dell, and Hewlett-Packard Enterprise are already testing or running Instinct GPUs, and with cost-conscious data center operators than three, the customer list may be longer.</p><p><h2 id=\"id_1237529206\">3. Independently developed chips</h2>Last but not least, Nvidia's dominance in the data center GPU market is driving many of its top customers (including OpenAI,<a href=\"https://laohu8.com/S/MSFT\">Microsoft</a>, Google,<a href=\"https://laohu8.com/S/AMZN\">Amazon</a>And Meta) to develop their own AI accelerator chips. These projects may take years to bear fruit, but the development of these chips has brought a dangerous signal to Nvidia's long-term growth.</p><p>Last year, Google published a research paper showing that its own fourth-generation TPU (tensor processing unit) is faster and more efficient than Nvidia's A100 chip. Meta also recently poached an entire research team from Graphcore, an artificial intelligence startup that claims its graph processing method can handle artificial intelligence tasks more efficiently than Nvidia's GPUs.</p><p>These tech giants are still buying a lot of Nvidia's A100 GPUs to handle their AI tasks, but they can gradually replace these chips with their first-party chips to reduce costs. If this happens, Nvidia's largest growth engine may stall.</p><p><strong>Investors shouldn't think Nvidia is invincible</strong></p><p>Nvidia's future looks bright, but investors should expect it to face some unpredictable challenges in the coming years. Intel, AMD, and first-party chips won't inhibit their growth anytime soon, but they may eventually evolve into major threats. In short, investors should not consider NVIDIA an invincible leader in the artificial intelligence market.</p><p></body></html></p>","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>Nvidia dominates the AI market but don't ignore these 3 challengers</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{ font:inherit;margin:0;padding:0;vertical-align:baseline;border:0 }\nbody{ font-size:16px; line-height:1.5; color:#999; background:transparent; }\n.wrapper{ overflow:hidden;word-break:break-all;padding:10px; }\nh1,h2{ font-weight:normal; line-height:1.35; margin-bottom:.6em; }\nh3,h4,h5,h6{ line-height:1.35; margin-bottom:1em; }\nh1{ font-size:24px; }\nh2{ font-size:20px; }\nh3{ font-size:18px; }\nh4{ font-size:16px; }\nh5{ font-size:14px; }\nh6{ font-size:12px; }\np,ul,ol,blockquote,dl,table{ margin:1.2em 0; }\nul,ol{ margin-left:2em; }\nul{ list-style:disc; }\nol{ list-style:decimal; }\nli,li p{ margin:10px 0;}\nimg{ max-width:100%;display:block;margin:0 auto 1em; }\nblockquote{ color:#B5B2B1; border-left:3px solid #aaa; padding:1em; }\nstrong,b{font-weight:bold;}\nem,i{font-style:italic;}\ntable{ width:100%;border-collapse:collapse;border-spacing:1px;margin:1em 0;font-size:.9em; }\nth,td{ padding:5px;text-align:left;border:1px solid #aaa; }\nth{ font-weight:bold;background:#5d5d5d; }\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: 12.5px; color: #7E829C; margin: 0;}\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\">\nNvidia dominates the AI market but don't ignore these 3 challengers\n</h2>\n<h4 class=\"meta\">\n<a class=\"head\" href=\"https://laohu8.com/wemedia/1059071526\">\n\n<div class=\"h-thumb\" style=\"background-image:url(https://community-static.tradeup.com/news/8274c5b9d4c2852bfb1c4d6ce16c68ba);background-size:cover;\"></div>\n\n<div class=\"h-content\">\n<p class=\"h-name\">老虎資訊 </p>\n<p class=\"h-time smaller\">2024-01-24 18:37</p>\n</div>\n</a>\n</h4>\n</header>\n<article>\n<p><html><head></head><body><em>Nvidia Dominates the AI Market, but Don't Ignore These 3 Challengers, by The Motley Fool</em><strong><em>Leo Sun</em></strong></p><p>Focus</p><p><ul style=\"\"><li><a href=\"https://laohu8.com/S/INTC\">Intel</a>Plans to bundle its new AI-oriented GPU with its data center GPU.</p><p></li><li>AMD (<a href=\"https://laohu8.com/S/AMD\">Supermicro America</a>) is targeting the AI market with its latest Instinct GPU.</p><p></li><li>Nvidia (<a href=\"https://laohu8.com/S/NVDA\">Nvidia</a>) 's top customers are developing their own first-party AI chips.</p><p></li></ul>Investors should not assume that this hot chipmaker is invincible.</p><p>In the past five years, the price of Nvidia (NVDA) shares has soared by 1420%. Its growth is largely driven by the expansion of the artificial intelligence (AI) market, which has prompted more companies to install their high-end GPUs in their data centers.</p><p>In the latest quarter, 80% of Nvidia's revenue came from its data center chips, while its gaming business (which was its original growth engine before the surge in AI) accounted for only 16% of its revenue. Optimists believe that Nvidia's data center business will continue to grow as the company develops more advanced generative AI platforms and large language models, and its GPUs will maintain industry standards for handling these complex tasks.</p><p>Nvidia's current customer list supports this optimistic view. ChatGPT creators OpenAI, Microsoft (MSFT), Alphabet's (GOOG) (GOOGL) Google, Amazon,<a href=\"https://laohu8.com/S/META\">Meta Platforms</a>(META) and other tech giants use its chips to power their AI services. But in the long run, other chipmakers may ease Nvidia's iron fist on the AI market. Let's take a closer look at the three most likely challengers.</p><p><h2 id=\"id_4212173666\">1、<a href=\"https://laohu8.com/S/INTC\">Intel</a></h2></p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/2e1574dc9806dcc57a4c52d21632f121\" tg-width=\"560\" tg-height=\"240\"/></p><p>Nvidia's GPUs typically work alongside INTC's high-end Xeon CPUs in the data center to accelerate machine learning and AI tasks. For Intel, which still controls about 94% of the server CPU market, Nvidia's expansion into the data center market is an unwelcome intrusion, meaning its CPUs can't effectively handle AI tasks on their own.</p><p>CPUs still process one piece of data at a time, while GPUs process a large number of integer and floating-point numbers at the same time, making them more suitable for graphics and AI applications. To narrow this gap, Intel launched its own Xe GPU series in September 2020, with Xe-HP (High Performance) and Xe-HPC (High Performance Computing) directly targeting Nvidia's data center GPUs. This follows the launch of the new Max data center GPU last year.</p><p>Unlike the first-party foundry model, Intel outsourced GPU production to rival TSMC, which also makes Nvidia's high-end GPUs. These chips haven't gained much momentum yet, but Intel's continued expansion of the business-and its ability to bundle GPUs with Xeon CPUs-could ultimately hurt Nvidia.</p><p><h2 id=\"id_3599411369\">2、<a href=\"https://laohu8.com/S/AMD\">Supermicro America</a></h2></p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/75de36a77586f00d0966660d965e5656\" tg-width=\"560\" tg-height=\"240\"/></p><p>Nvidia's expansion into the data center market has enabled it to<a href=\"https://laohu8.com/S/AMD\">Supermicro America</a>(AMD), which controls a small portion of the discrete GPU market. However, AMD has been fighting back with its new Instinct data center chip for AI tasks.</p><p>It launched its first three Instinct GPUs (MI6, MI8, and MI25) in 2017, followed by more 7nm and 6nm MI chips over the next four years. In December last year, the company launched its latest MI300 Instinct chip, which is based on TSMC's 5nm and 6nm node architecture. In the latest base test, AMD's top-level MI300X defeated Nvidia's H100 in terms of processing capabilities and memory bandwidth, but Nvidia claims that the H100 is still superior to the MI300X when operating on optimized software.</p><p>AMD's AI ambitions could pose problems for Nvidia, especially if it continues its tradition of selling chips at lower prices. Companies such as Microsoft, Meta, Oracle, Dell, and Hewlett-Packard Enterprise are already testing or running Instinct GPUs, and with cost-conscious data center operators than three, the customer list may be longer.</p><p><h2 id=\"id_1237529206\">3. Independently developed chips</h2>Last but not least, Nvidia's dominance in the data center GPU market is driving many of its top customers (including OpenAI,<a href=\"https://laohu8.com/S/MSFT\">Microsoft</a>, Google,<a href=\"https://laohu8.com/S/AMZN\">Amazon</a>And Meta) to develop their own AI accelerator chips. These projects may take years to bear fruit, but the development of these chips has brought a dangerous signal to Nvidia's long-term growth.</p><p>Last year, Google published a research paper showing that its own fourth-generation TPU (tensor processing unit) is faster and more efficient than Nvidia's A100 chip. Meta also recently poached an entire research team from Graphcore, an artificial intelligence startup that claims its graph processing method can handle artificial intelligence tasks more efficiently than Nvidia's GPUs.</p><p>These tech giants are still buying a lot of Nvidia's A100 GPUs to handle their AI tasks, but they can gradually replace these chips with their first-party chips to reduce costs. If this happens, Nvidia's largest growth engine may stall.</p><p><strong>Investors shouldn't think Nvidia is invincible</strong></p><p>Nvidia's future looks bright, but investors should expect it to face some unpredictable challenges in the coming years. Intel, AMD, and first-party chips won't inhibit their growth anytime soon, but they may eventually evolve into major threats. In short, investors should not consider NVIDIA an invincible leader in the artificial intelligence market.</p><p></body></html></p>\n</article>\n</div>\n</body>\n</html>\n","type":0,"thumbnail":"https://static.tigerbbs.com/03e38a3a3c63bd5a9d8d8ca2d384d1c5","relate_stocks":{"MSFT":"微软","AMD":"美国超微公司","NVDA":"英伟达","AMZN":"亚马逊","INTC":"英特尔","META":"Meta Platforms, Inc."},"source_url":"","is_english":false,"share_image_url":"https://static.laohu8.com/e9f99090a1c2ed51c021029395664489","article_id":"1194806166","content_text":"編譯自 The Motley Fool《Nvidia Dominates the AI Market, but Don't Ignore These 3 Challengers》，作者：Leo Sun重點 英特尔計劃將其新的面向AI的GPU與其數據中心GPU捆綁在一起。AMD（ 美国超微公司 ）正在以其最新的Instinct GPU瞄準AI市場。Nvidia（ 英伟达 ）的頂級客戶正在開發自己的第一方AI芯片。投資者不應假設這家火熱的芯片製造商是不可戰勝的。在過去的五年中，Nvidia（NVDA）的股價飆漲了1420％。其增長主要是由人工智能（AI）市場的擴大推動的，這促使更多公司在其數據中心安裝其高端GPU。在最新一季，Nvidia的80％收入來自其數據中心芯片，而其遊戲業務（在AI激增之前是其原始增長引擎）僅為其收入的16％。樂觀者認為，隨著公司開發更先進的生成式AI平台和大型語言模型，Nvidia的數據中心業務將繼續增長，其GPU將保持處理這些複雜任務的行業標準。Nvidia當前的客戶名單支持這種樂觀的觀點。 ChatGPT的創造者OpenAI，Microsoft（MSFT ），Alphabet的（GOOG ）（GOOGL ）Google，Amazon， Meta Platforms（META ）和其他科技巨頭都使用其芯片來驅動其AI服務。但從長遠來看，其他芯片製造商可能會減輕Nvidia對AI市場的鐵腕。讓我們仔細看看三位最有可能的挑戰者。1、 英特尔 Nvidia 的 GPU 通常在資料中心與英特爾（INTC ）高階 Xeon CPU 一起工作，以加速機器學習和AI 任務。對於仍控制約94%伺服器CPU市場的英特爾來說，英偉達向資料中心市場的擴張是一種不受歡迎的入侵，這意味著其CPU無法自行有效處理人工智慧任務。CPU 仍然一次處理一份數據，而 GPU 則同時處理大量整數和浮點數，這使得它們更適合圖形和人工智慧應用。為了縮小這一差距，英特爾於 2020 年 9 月推出了自己的 Xe GPU 系列，其 Xe-HP（高效能）和 Xe-HPC（高效能運算）直接針對 Nvidia 的資料中心 GPU。隨後，該公司於去年推出了新的「Max」資料中心 GPU。與第一方代工模式不同尋常的是，英特爾將 GPU 的生產外包給競爭對手台積電，該公司也生產 Nvidia 的高階 GPU。這些晶片尚未獲得太大動力，但英特爾對該業務的持續擴張——以及將 GPU 與 Xeon CPU 捆綁在一起的能力——最終可能會損害 Nvidia。2、 美国超微公司 Nvidia 向資料中心市場的擴張使其在與 美国超微公司 ( AMD )的競爭中佔據了優勢，後者控制著獨立 GPU 市場的一小部分。然而，AMD 一直在利用其用於 AI 任務的新型 Instinct 資料中心晶片進行反擊。它於 2017 年推出了首批三款 Instinct GPU（MI6、MI8 和 MI25），隨後在接下來的四年中又推出了更多 7 奈米和 6 奈米 MI 晶片。去年 12 月，該公司推出了最新的 MI300 Instinct 晶片，該晶片基於台積電 5 奈米和 6 奈米節點構建。在最新的基準測試中，AMD的頂級MI300X在處理能力和記憶體頻寬方面擊敗了Nvidia的H100，但Nvidia聲稱H100在優化軟體上運行時仍然優於MI300X。AMD 的人工智慧野心可能會給英偉達帶來問題，特別是如果它繼續以較低價格銷售晶片的傳統的話。微軟、Meta、甲骨文、戴爾和惠普企業等公司已經在測試或運行 Instinct GPU，而且隨著注重成本的資料中心運營商貨比三家，客戶名單可能會更長。3、自主研發晶片最後但並非最不重要的一點是，Nvidia 在資料中心 GPU 市場的主導地位正在推動其許多頂級客戶（包括 OpenAI、 微软 、Google、 亚马逊 和 Meta）開發自己的 AI 加速器晶片。這些項目可能需要數年時間才能取得成果，但這些晶片的開發為英偉達的長期成長帶來了危險信號。去年，Google發表了一篇研究論文，顯示其自己的第四代 TPU（張量處理單元）比 Nvidia 的 A100 晶片更快、更有效率。Meta 最近也從 Graphcore 挖角了整個研究團隊，這家人工智慧新創公司聲稱其「圖形處理」方法可以比 Nvidia 的 GPU 更有效地處理人工智慧任務。這些科技巨頭仍在購買大量 Nvidia 的 A100 GPU 來處理他們的人工智慧任務，但他們可以逐漸用他們的第一方晶片取代這些晶片以降低成本。如果這種情況發生，英偉達最大的成長引擎可能就會熄火。投資者不應認為Nvidia是無敵的英偉達的未來看起來很光明，但投資者應該預計它在未來幾年將面臨一些不可預測的挑戰。英特爾、AMD 和第一方晶片不會很快抑制其成長，但它們最終可能演變成主要威脅。簡言之，投資人不應認為英偉達是人工智慧市場無敵的領導者。","news_type":1,"symbols_score_info":{"AMD":1.1,"META":1.1,"AMZN":1.1,"MSFT":1.1,"INTC":1.1,"NVDA":1.1}},"isVote":1,"tweetType":1,"viewCount":563,"authorTweetTopStatus":1,"verified":2,"comments":[],"imageCount":0,"langContent":"EN","totalScore":0}],"lives":[]}
