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How a Shifting AI Chip Market Will Shape Nvidia's Future

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ariticle, would you like to share it?","listText":"Great ariticle, would you like to share it?","text":"Great ariticle, would you like to share it?","images":[],"top":1,"highlighted":1,"essential":1,"paper":1,"likeSize":0,"commentSize":0,"repostSize":0,"link":"https://ttm.financial/post/278042946449672","repostId":"2414236065","repostType":2,"repost":{"id":"2414236065","weMediaInfo":{"introduction":"Dow Jones publishes the world’s most trusted business news and financial information in a variety of media.","home_visible":0,"media_name":"Dow Jones","id":"106","head_image":"https://static.tigerbbs.com/150f88aa4d182df19190059f4a365e99"},"pubTimestamp":1708903810,"share":"https://www.laohu8.com/m/news/2414236065?lang=&edition=full","pubTime":"2024-02-26 07:30","market":"hk","language":"en","title":"How a Shifting AI Chip Market Will Shape Nvidia's Future","url":"https://stock-news.laohu8.com/highlight/detail?id=2414236065","media":"Dow Jones","summary":"The AI chip battle that Nvidia has dominated is already shifting to a new front -- one that will be much larger but also more competitive.Nvidia built itself into a $2 trillion company by supplying the chips essential for the incredibly complicated work of training artificial-intelligence models. As the industry rapidly evolves, the bigger opportunity will be selling chips that make those models run after they are trained, churning out text and images for the fast-growing population of companies and people actually using generative AI tools.Kress's comments allayed some concerns that the shift toward chips for deploying AI systems -- those that do what is called \"inference\" work -- threatens Nvidia's position because that work can be done with less-powerful and less-expensive chips than those that have made Nvidia the leader of the AI boom.The rate at which inference is growing may be faster than earlier expected. Ea","content":"<html><head></head><body><p>The AI chip battle that Nvidia has dominated is already shifting to a new front -- one that will be much larger but also more competitive.</p><p>Nvidia built itself into a $2 trillion company by supplying the chips essential for the incredibly complicated work of training artificial-intelligence models. As the industry rapidly evolves, the bigger opportunity will be selling chips that make those models run after they are trained, churning out text and images for the fast-growing population of companies and people actually using generative AI tools.</p><p>Right now, that shift is adding to Nvidia&#39;s blockbuster sales. Chief Financial Officer Colette Kress said this past week that more than 40% of Nvidia&#39;s data center business in the past year -- when revenue exceeded $47 billion -- was for deployment of AI systems and not training. That percentage was the first significant indication that the shift is under way.</p><p>Kress&#39;s comments allayed some concerns that the shift toward chips for deploying AI systems -- those that do what is called &#34;inference&#34; work -- threatens Nvidia&#39;s position because that work can be done with less-powerful and less-expensive chips than those that have made Nvidia the leader of the AI boom.</p><p>&#34;There is a perception that Nvidia&#39;s share will be lower in inferencing vs. training,&#34; Ben Reitzes, an analyst at Melius Research, said in a note to clients. &#34;This revelation helps shed light on its ability to benefit from the coming inferencing explosion.&#34;</p><p>Many rivals believe they have a better shot in the AI market as chips for inferencing become more important.</p><p>Intel, which makes central processing units that go into data centers, believes its chips will be increasingly appealing as customers focus on driving down the cost of operating AI models. The kinds of chips Intel specializes in are already widely used in inferencing, and it isn&#39;t as critical to have Nvidia&#39;s cutting-edge and more expensive H100 AI chips when doing that task.</p><p>&#34;The economics of inferencing are, I&#39;m not going to stand up $40,000 H100 environments that suck too much power and require new management and security models and new IT infrastructure,&#34; Intel Chief Executive Pat Gelsinger said in an interview in December. &#34;If I can run those models on standard [Intel chips], it&#39;s a no-brainer.&#34;</p><p>Vivek Arya, an analyst at Bank of America, said the shift toward inference was perhaps the most significant news to emerge Wednesday from Nvidia&#39;s quarterly earnings report, which beat Wall Street forecasts and led its stock to climb 8.5% for the week, pushing the company to a roughly $2 trillion valuation.</p><p>Arya said inference would rise as the focus shifts to generating revenue from AI models following a surge of investment in training them. That could be more competitive compared with AI training, where Nvidia dominates.</p><p>The rate at which inference is growing may be faster than earlier expected. Early this year, UBS analysts estimated 90% of chip demand stemmed from training, and that inference would drive just 20% of the market by next year. Nvidia deriving about 40% of its data center revenue from inference was &#34;a bigger number than we would expect,&#34; the analysts said in a note.</p><p>Indeed, Nvidia&#39;s financial results Wednesday suggest its market share in AI chips of more than 80% isn&#39;t yet being challenged in a serious way. Nvidia&#39;s chips used for training AI systems are expected to remain in high demand for the foreseeable future.</p><p>In training AI systems, companies run vast oceans of data through their models to teach them to predict language in a way that enables human-sounding expression. The work requires enormous computing ability that is well suited to Nvidia&#39;s graphics processing units, or GPUs.</p><p>Inference work is when those models are asked to process new bits of information and respond -- a lighter lift.</p><p>In addition to Nvidia&#39;s established competitors like Intel and Advanced Micro Devices, a number of AI-chip startups may also gain steam as inference takes center stage.</p><p>&#34;We&#39;re seeing our inference use case exploding,&#34; said Rodrigo Liang, chief executive of SambaNova, a startup that makes a combination of AI chips and software that can do both inferencing and training. &#34;People are starting to realize that 80%-plus of the cost is going to be in inferencing, and I need to look for alternate solutions,&#34; he said.</p><p>Groq, a startup founded by former Google AI-chip engineer Jonathan Ross, also has seen a surge of interest in recent months after a demo on the company&#39;s home page showed how quickly its inference chips could generate responses from a large language model. The company is on track to deploy 42,000 of its chips this year and one million next year, but is exploring increasing those totals to 220,000 this year and 1.5 million next year, Ross said.</p><p>One factor driving the shift, he said, was that some of the most advanced AI systems were being tuned to produce better responses without retraining them, pushing more of the computational work into inference. And Groq&#39;s specialist chips, he said, were significantly quicker and cheaper to run than Nvidia&#39;s or other chip companies&#39; offerings.</p><p>&#34;For inference, what you can deploy depends on cost,&#34; he said. &#34;There are a bunch of models that would get trained at Google that worked but about 80% of them didn&#39;t get deployed because they were too expensive to put into production.&#34;</p><p>Big tech companies -- including Meta, Microsoft, Alphabet&#39;s Google and Amazon.com -- have been working to develop inference chips in-house, recognizing the coming shift and the benefits of being able to do inference more cheaply.</p><p>Amazon, for example, has had inference chips since 2018, and inference represents 40% of computing costs for its Alexa smart assistant, Swami Sivasubramanian, a vice president of data and machine learning at the company&#39;s cloud-computing arm, said last year.</p><p>Nvidia, for its part, is seeking to stay on top as the transition toward inference proceeds. A coming chip posted industry-leading results last year in a key AI inference benchmark, extending the company&#39;s yearslong dominance in the competition.</p><p>In December, after AMD unveiled new AI chips that it said were better than Nvidia&#39;s at inference, Nvidia fired back in a blog post disputing the claims. AMD didn&#39;t use optimized software in making its performance claims, Nvidia said, and if it did, Nvidia&#39;s chips would be twice as fast.</p></body></html>","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>How a Shifting AI Chip Market Will Shape Nvidia's Future</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: 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\">\nHow a Shifting AI Chip Market Will Shape Nvidia's Future\n</h2>\n\n<h4 class=\"meta\">\n\n\n<div class=\"head\" \">\n\n\n<div class=\"h-thumb\" style=\"background-image:url(https://static.tigerbbs.com/150f88aa4d182df19190059f4a365e99);background-size:cover;\"></div>\n\n<div class=\"h-content\">\n<p class=\"h-name\">Dow Jones </p>\n<p class=\"h-time\">2024-02-26 07:30</p>\n</div>\n\n</div>\n\n\n</h4>\n\n</header>\n<article>\n<html><head></head><body><p>The AI chip battle that Nvidia has dominated is already shifting to a new front -- one that will be much larger but also more competitive.</p><p>Nvidia built itself into a $2 trillion company by supplying the chips essential for the incredibly complicated work of training artificial-intelligence models. As the industry rapidly evolves, the bigger opportunity will be selling chips that make those models run after they are trained, churning out text and images for the fast-growing population of companies and people actually using generative AI tools.</p><p>Right now, that shift is adding to Nvidia&#39;s blockbuster sales. Chief Financial Officer Colette Kress said this past week that more than 40% of Nvidia&#39;s data center business in the past year -- when revenue exceeded $47 billion -- was for deployment of AI systems and not training. That percentage was the first significant indication that the shift is under way.</p><p>Kress&#39;s comments allayed some concerns that the shift toward chips for deploying AI systems -- those that do what is called &#34;inference&#34; work -- threatens Nvidia&#39;s position because that work can be done with less-powerful and less-expensive chips than those that have made Nvidia the leader of the AI boom.</p><p>&#34;There is a perception that Nvidia&#39;s share will be lower in inferencing vs. training,&#34; Ben Reitzes, an analyst at Melius Research, said in a note to clients. &#34;This revelation helps shed light on its ability to benefit from the coming inferencing explosion.&#34;</p><p>Many rivals believe they have a better shot in the AI market as chips for inferencing become more important.</p><p>Intel, which makes central processing units that go into data centers, believes its chips will be increasingly appealing as customers focus on driving down the cost of operating AI models. The kinds of chips Intel specializes in are already widely used in inferencing, and it isn&#39;t as critical to have Nvidia&#39;s cutting-edge and more expensive H100 AI chips when doing that task.</p><p>&#34;The economics of inferencing are, I&#39;m not going to stand up $40,000 H100 environments that suck too much power and require new management and security models and new IT infrastructure,&#34; Intel Chief Executive Pat Gelsinger said in an interview in December. &#34;If I can run those models on standard [Intel chips], it&#39;s a no-brainer.&#34;</p><p>Vivek Arya, an analyst at Bank of America, said the shift toward inference was perhaps the most significant news to emerge Wednesday from Nvidia&#39;s quarterly earnings report, which beat Wall Street forecasts and led its stock to climb 8.5% for the week, pushing the company to a roughly $2 trillion valuation.</p><p>Arya said inference would rise as the focus shifts to generating revenue from AI models following a surge of investment in training them. That could be more competitive compared with AI training, where Nvidia dominates.</p><p>The rate at which inference is growing may be faster than earlier expected. Early this year, UBS analysts estimated 90% of chip demand stemmed from training, and that inference would drive just 20% of the market by next year. Nvidia deriving about 40% of its data center revenue from inference was &#34;a bigger number than we would expect,&#34; the analysts said in a note.</p><p>Indeed, Nvidia&#39;s financial results Wednesday suggest its market share in AI chips of more than 80% isn&#39;t yet being challenged in a serious way. Nvidia&#39;s chips used for training AI systems are expected to remain in high demand for the foreseeable future.</p><p>In training AI systems, companies run vast oceans of data through their models to teach them to predict language in a way that enables human-sounding expression. The work requires enormous computing ability that is well suited to Nvidia&#39;s graphics processing units, or GPUs.</p><p>Inference work is when those models are asked to process new bits of information and respond -- a lighter lift.</p><p>In addition to Nvidia&#39;s established competitors like Intel and Advanced Micro Devices, a number of AI-chip startups may also gain steam as inference takes center stage.</p><p>&#34;We&#39;re seeing our inference use case exploding,&#34; said Rodrigo Liang, chief executive of SambaNova, a startup that makes a combination of AI chips and software that can do both inferencing and training. &#34;People are starting to realize that 80%-plus of the cost is going to be in inferencing, and I need to look for alternate solutions,&#34; he said.</p><p>Groq, a startup founded by former Google AI-chip engineer Jonathan Ross, also has seen a surge of interest in recent months after a demo on the company&#39;s home page showed how quickly its inference chips could generate responses from a large language model. The company is on track to deploy 42,000 of its chips this year and one million next year, but is exploring increasing those totals to 220,000 this year and 1.5 million next year, Ross said.</p><p>One factor driving the shift, he said, was that some of the most advanced AI systems were being tuned to produce better responses without retraining them, pushing more of the computational work into inference. And Groq&#39;s specialist chips, he said, were significantly quicker and cheaper to run than Nvidia&#39;s or other chip companies&#39; offerings.</p><p>&#34;For inference, what you can deploy depends on cost,&#34; he said. &#34;There are a bunch of models that would get trained at Google that worked but about 80% of them didn&#39;t get deployed because they were too expensive to put into production.&#34;</p><p>Big tech companies -- including Meta, Microsoft, Alphabet&#39;s Google and Amazon.com -- have been working to develop inference chips in-house, recognizing the coming shift and the benefits of being able to do inference more cheaply.</p><p>Amazon, for example, has had inference chips since 2018, and inference represents 40% of computing costs for its Alexa smart assistant, Swami Sivasubramanian, a vice president of data and machine learning at the company&#39;s cloud-computing arm, said last year.</p><p>Nvidia, for its part, is seeking to stay on top as the transition toward inference proceeds. A coming chip posted industry-leading results last year in a key AI inference benchmark, extending the company&#39;s yearslong dominance in the competition.</p><p>In December, after AMD unveiled new AI chips that it said were better than Nvidia&#39;s at inference, Nvidia fired back in a blog post disputing the claims. AMD didn&#39;t use optimized software in making its performance claims, Nvidia said, and if it did, Nvidia&#39;s chips would be twice as fast.</p></body></html>\n\n</article>\n</div>\n</body>\n</html>\n","type":0,"thumbnail":"","relate_stocks":{"BK4534":"瑞士信贷持仓","BK4567":"ESG概念","BK4533":"AQR资本管理(全球第二大对冲基金)","IE00B1XK9C88.USD":"PINEBRIDGE US LARGE CAP RESEARCH ENHANCED \"A\" (USD) ACC","BK4525":"远程办公概念","GOOGL":"谷歌A","IE00BMPRXR70.SGD":"Neuberger Berman 5G Connectivity A Acc SGD-H","IE00BSNM7G36.USD":"NEUBERGER BERMAN SYSTEMATIC GLOBAL SUSTAINABLE VALUE \"A\" (USD) ACC","LU0494093205.USD":"贝莱德ESG灵活多元资产A2 USD-H","LU0128525689.USD":"TEMPLETON GLOBAL BALANCED \"A\"(USD) ACC","BK4535":"淡马锡持仓","GOOG":"谷歌","LU0310799852.SGD":"FTIF - Templeton Global Equity Income A MDIS SGD","BK4527":"明星科技股","BK4538":"云计算","BK4543":"AI","IE00BZ1G4Q59.USD":"LEGG MASON CLEARBRIDGE US EQUITY SUSTAINABILITY LEADER \"A\"(USD) INC (A)","BK4550":"红杉资本持仓","NVDA":"英伟达","IE00BWXC8680.SGD":"PINEBRIDGE US LARGE CAP RESEARCH ENHANCED \"A5\" (SGD) ACC","LU0979878070.USD":"FULLERTON LUX FUNDS - ASIA ABSOLUTE ALPHA \"A\" (USD) ACC","LU0158827948.USD":"ALLIANZ GLOBAL SUSTAINABILITY \"A\" (USD) INC","LU0320765059.SGD":"FTIF - Franklin US Opportunities A Acc SGD","LU1814569148.SGD":"WELLINGTON GLOBAL QUALITY GROWTH \"D\" (SGDHDG) ACC","BK4574":"无人驾驶","LU1852331112.SGD":"Blackrock World Technology Fund A2 SGD-H","AMD":"美国超微公司","BK4573":"虚拟现实","INTC":"英特尔","LU1691799644.USD":"Amundi Funds Polen Capital Global Growth A2 (C) USD","LU0672654240.SGD":"FTIF - Franklin US Opportunities A Acc SGD-H1","IE00B1BXHZ80.USD":"Legg Mason ClearBridge - US Appreciation A Acc USD","LU0965509283.SGD":"AB LOW VOLATILITY EQUITY PORTFOLIO \"AD\" (SGDHDG) INC","BK4581":"高盛持仓","LU0354030511.USD":"ALLSPRING  U.S. LARGE CAP GROWTH \"I\" (USD) ACC","BK4549":"软银资本持仓","IE00BJTD4V19.USD":"NEUBERGER BERMAN US LONG SHORT EQUITY \"A1\" (USD) ACC","LU0354030438.USD":"富国美国大盘成长基金Cl A Acc","IE0009356076.USD":"JANUS HENDERSON GLOBAL TECHNOLOGY AND INNOVATION \"A2\" (USD) ACC","BK4548":"巴美列捷福持仓","LU0256863811.USD":"ALLIANZ US EQUITY \"A\" INC","LU0348723411.USD":"ALLIANZ GLOBAL HI-TECH GROWTH \"A\" (USD) INC","LU0029864427.USD":"TEMPLETON GLOBAL \"A\" (USD) INC","IE00BFSS7M15.SGD":"Janus Henderson Balanced A Acc SGD-H","BK4554":"元宇宙及AR概念","BK4532":"文艺复兴科技持仓","IE00B3S45H60.SGD":"Neuberger Berman US Multicap Opportunities A Acc SGD-H","LU1988902786.USD":"FULLERTON LUX FUNDS GLOBAL ABSOLUTE ALPHA \"I\" (USD) ACC","LU0417517546.SGD":"Allianz US Equity Cl AT Acc SGD","BK4553":"喜马拉雅资本持仓"},"source_url":"https://dowjonesnews.com/newdjn/logon.aspx?AL=N","is_english":true,"share_image_url":"https://static.laohu8.com/e9f99090a1c2ed51c021029395664489","article_id":"2414236065","content_text":"The AI chip battle that Nvidia has dominated is already shifting to a new front -- one that will be much larger but also more competitive.Nvidia built itself into a $2 trillion company by supplying the chips essential for the incredibly complicated work of training artificial-intelligence models. As the industry rapidly evolves, the bigger opportunity will be selling chips that make those models run after they are trained, churning out text and images for the fast-growing population of companies and people actually using generative AI tools.Right now, that shift is adding to Nvidia's blockbuster sales. Chief Financial Officer Colette Kress said this past week that more than 40% of Nvidia's data center business in the past year -- when revenue exceeded $47 billion -- was for deployment of AI systems and not training. That percentage was the first significant indication that the shift is under way.Kress's comments allayed some concerns that the shift toward chips for deploying AI systems -- those that do what is called \"inference\" work -- threatens Nvidia's position because that work can be done with less-powerful and less-expensive chips than those that have made Nvidia the leader of the AI boom.\"There is a perception that Nvidia's share will be lower in inferencing vs. training,\" Ben Reitzes, an analyst at Melius Research, said in a note to clients. \"This revelation helps shed light on its ability to benefit from the coming inferencing explosion.\"Many rivals believe they have a better shot in the AI market as chips for inferencing become more important.Intel, which makes central processing units that go into data centers, believes its chips will be increasingly appealing as customers focus on driving down the cost of operating AI models. The kinds of chips Intel specializes in are already widely used in inferencing, and it isn't as critical to have Nvidia's cutting-edge and more expensive H100 AI chips when doing that task.\"The economics of inferencing are, I'm not going to stand up $40,000 H100 environments that suck too much power and require new management and security models and new IT infrastructure,\" Intel Chief Executive Pat Gelsinger said in an interview in December. \"If I can run those models on standard [Intel chips], it's a no-brainer.\"Vivek Arya, an analyst at Bank of America, said the shift toward inference was perhaps the most significant news to emerge Wednesday from Nvidia's quarterly earnings report, which beat Wall Street forecasts and led its stock to climb 8.5% for the week, pushing the company to a roughly $2 trillion valuation.Arya said inference would rise as the focus shifts to generating revenue from AI models following a surge of investment in training them. That could be more competitive compared with AI training, where Nvidia dominates.The rate at which inference is growing may be faster than earlier expected. Early this year, UBS analysts estimated 90% of chip demand stemmed from training, and that inference would drive just 20% of the market by next year. Nvidia deriving about 40% of its data center revenue from inference was \"a bigger number than we would expect,\" the analysts said in a note.Indeed, Nvidia's financial results Wednesday suggest its market share in AI chips of more than 80% isn't yet being challenged in a serious way. Nvidia's chips used for training AI systems are expected to remain in high demand for the foreseeable future.In training AI systems, companies run vast oceans of data through their models to teach them to predict language in a way that enables human-sounding expression. The work requires enormous computing ability that is well suited to Nvidia's graphics processing units, or GPUs.Inference work is when those models are asked to process new bits of information and respond -- a lighter lift.In addition to Nvidia's established competitors like Intel and Advanced Micro Devices, a number of AI-chip startups may also gain steam as inference takes center stage.\"We're seeing our inference use case exploding,\" said Rodrigo Liang, chief executive of SambaNova, a startup that makes a combination of AI chips and software that can do both inferencing and training. \"People are starting to realize that 80%-plus of the cost is going to be in inferencing, and I need to look for alternate solutions,\" he said.Groq, a startup founded by former Google AI-chip engineer Jonathan Ross, also has seen a surge of interest in recent months after a demo on the company's home page showed how quickly its inference chips could generate responses from a large language model. The company is on track to deploy 42,000 of its chips this year and one million next year, but is exploring increasing those totals to 220,000 this year and 1.5 million next year, Ross said.One factor driving the shift, he said, was that some of the most advanced AI systems were being tuned to produce better responses without retraining them, pushing more of the computational work into inference. And Groq's specialist chips, he said, were significantly quicker and cheaper to run than Nvidia's or other chip companies' offerings.\"For inference, what you can deploy depends on cost,\" he said. \"There are a bunch of models that would get trained at Google that worked but about 80% of them didn't get deployed because they were too expensive to put into production.\"Big tech companies -- including Meta, Microsoft, Alphabet's Google and Amazon.com -- have been working to develop inference chips in-house, recognizing the coming shift and the benefits of being able to do inference more cheaply.Amazon, for example, has had inference chips since 2018, and inference represents 40% of computing costs for its Alexa smart assistant, Swami Sivasubramanian, a vice president of data and machine learning at the company's cloud-computing arm, said last year.Nvidia, for its part, is seeking to stay on top as the transition toward inference proceeds. A coming chip posted industry-leading results last year in a key AI inference benchmark, extending the company's yearslong dominance in the competition.In December, after AMD unveiled new AI chips that it said were better than Nvidia's at inference, Nvidia fired back in a blog post disputing the claims. AMD didn't use optimized software in making its performance claims, Nvidia said, and if it did, Nvidia's chips would be twice as fast.","news_type":1},"isVote":1,"tweetType":1,"viewCount":77,"authorTweetTopStatus":1,"verified":2,"comments":[],"imageCount":0,"langContent":"EN","totalScore":0},{"id":259712913551376,"gmtCreate":1704416985716,"gmtModify":1704416990713,"author":{"id":"4117707853567942","authorId":"4117707853567942","name":"Mumusc","avatar":"https://community-static.tradeup.com/news/88a5db2adac3101c48883843b3a38ddf","crmLevel":8,"crmLevelSwitch":1},"themes":[],"htmlText":"May the force be with you all in 2024","listText":"May the force be with you all in 2024","text":"May the force be with you all in 2024","images":[],"top":1,"highlighted":1,"essential":1,"paper":1,"likeSize":0,"commentSize":0,"repostSize":0,"link":"https://ttm.financial/post/259712913551376","isVote":1,"tweetType":1,"viewCount":123,"authorTweetTopStatus":1,"verified":2,"comments":[],"imageCount":0,"langContent":"EN","totalScore":0},{"id":9970603124,"gmtCreate":1684331440669,"gmtModify":1684332473046,"author":{"id":"4117707853567942","authorId":"4117707853567942","name":"Mumusc","avatar":"https://community-static.tradeup.com/news/88a5db2adac3101c48883843b3a38ddf","crmLevel":8,"crmLevelSwitch":1},"themes":[],"htmlText":"What's the view on Vix index?","listText":"What's the view on Vix index?","text":"What's the view on Vix index?","images":[],"top":1,"highlighted":1,"essential":1,"paper":1,"likeSize":0,"commentSize":0,"repostSize":0,"link":"https://ttm.financial/post/9970603124","isVote":1,"tweetType":1,"viewCount":116,"authorTweetTopStatus":1,"verified":2,"comments":[],"imageCount":0,"langContent":"EN","totalScore":0},{"id":9942203169,"gmtCreate":1681223451552,"gmtModify":1681225343830,"author":{"id":"4117707853567942","authorId":"4117707853567942","name":"Mumusc","avatar":"https://community-static.tradeup.com/news/88a5db2adac3101c48883843b3a38ddf","crmLevel":8,"crmLevelSwitch":1},"themes":[],"htmlText":"How to make tiger jump faster","listText":"How to make tiger jump faster","text":"How to make tiger jump faster","images":[],"top":1,"highlighted":1,"essential":1,"paper":1,"likeSize":1,"commentSize":0,"repostSize":0,"link":"https://ttm.financial/post/9942203169","repostId":"9943960936","repostType":1,"repost":{"id":9943960936,"gmtCreate":1679046534725,"gmtModify":1680580626622,"author":{"id":"3527667667103859","authorId":"3527667667103859","name":"TigerEvents","avatar":"https://community-static.tradeup.com/news/61ed9b39c6cbcdce6372edc1c0b48a2d","crmLevel":1,"crmLevelSwitch":0},"themes":[],"title":"【Game】Easter Egg Hunting with Tiger, Win Disney Shares and USD 120 Voucher","htmlText":"🐰🌷 Hop into the Easter spirit and join our \"Tiger's Egg Hunting\" game! 🎉Stand to win free Disney stocks and a USD 120 cash voucher!🎁🌟Our interactive Easter game is open to Tigers, and it's so easy to play! Simply jump and catch the egg, and you could be a lucky winner. 🐇That's not all. You can also invite your friends to join in the fun to earn more points. Plus, you can challenge your friends for a race up the leaderboard. Let's fly to the moon together!Don't miss out on this egg-citing opportunity to win BIG! Join the game now and hop on your way to victory. 🥳🐣<a href=\"https://www.tigerbrokers.com.sg/activity/market/2023/easter/?adcode=20230316162207#/\" target=\"_blank\">Join our Easter campaign now</a>","listText":"🐰🌷 Hop into the Easter spirit and join our \"Tiger's Egg Hunting\" game! 🎉Stand to win free Disney stocks and a USD 120 cash voucher!🎁🌟Our interactive Easter game is open to Tigers, and it's so easy to play! Simply jump and catch the egg, and you could be a lucky winner. 🐇That's not all. You can also invite your friends to join in the fun to earn more points. Plus, you can challenge your friends for a race up the leaderboard. Let's fly to the moon together!Don't miss out on this egg-citing opportunity to win BIG! Join the game now and hop on your way to victory. 🥳🐣<a href=\"https://www.tigerbrokers.com.sg/activity/market/2023/easter/?adcode=20230316162207#/\" target=\"_blank\">Join our Easter campaign now</a>","text":"🐰🌷 Hop into the Easter spirit and join our \"Tiger's Egg Hunting\" game! 🎉Stand to win free Disney stocks and a USD 120 cash voucher!🎁🌟Our interactive Easter game is open to Tigers, and it's so easy to play! Simply jump and catch the egg, and you could be a lucky winner. 🐇That's not all. You can also invite your friends to join in the fun to earn more points. Plus, you can challenge your friends for a race up the leaderboard. Let's fly to the moon together!Don't miss out on this egg-citing opportunity to win BIG! Join the game now and hop on your way to victory. 🥳🐣Join our Easter campaign now","images":[{"img":"https://community-static.tradeup.com/news/c90a7371a3bcd1e6c552d2aa23f72c33","width":"1200","height":"630"}],"top":1,"highlighted":1,"essential":2,"paper":2,"likeSize":0,"commentSize":0,"repostSize":0,"link":"https://ttm.financial/post/9943960936","isVote":1,"tweetType":1,"viewCount":0,"authorTweetTopStatus":1,"verified":2,"comments":[],"imageCount":1,"langContent":"EN","totalScore":0},"isVote":1,"tweetType":1,"viewCount":21,"authorTweetTopStatus":1,"verified":2,"comments":[],"imageCount":0,"langContent":"EN","totalScore":0},{"id":9933296120,"gmtCreate":1662289091260,"gmtModify":1676537031526,"author":{"id":"4117707853567942","authorId":"4117707853567942","name":"Mumusc","avatar":"https://community-static.tradeup.com/news/88a5db2adac3101c48883843b3a38ddf","crmLevel":8,"crmLevelSwitch":1},"themes":[],"htmlText":"Let's go","listText":"Let's go","text":"Let's go","images":[],"top":1,"highlighted":1,"essential":1,"paper":1,"likeSize":3,"commentSize":0,"repostSize":0,"link":"https://ttm.financial/post/9933296120","repostId":"2264541477","repostType":4,"isVote":1,"tweetType":1,"viewCount":189,"authorTweetTopStatus":1,"verified":2,"comments":[],"imageCount":0,"langContent":"EN","totalScore":0},{"id":9994874806,"gmtCreate":1661612705110,"gmtModify":1676536549590,"author":{"id":"4117707853567942","authorId":"4117707853567942","name":"Mumusc","avatar":"https://community-static.tradeup.com/news/88a5db2adac3101c48883843b3a38ddf","crmLevel":8,"crmLevelSwitch":1},"themes":[],"htmlText":"Hello world","listText":"Hello world","text":"Hello world","images":[],"top":1,"highlighted":1,"essential":1,"paper":1,"likeSize":1,"commentSize":0,"repostSize":0,"link":"https://ttm.financial/post/9994874806","isVote":1,"tweetType":1,"viewCount":50,"authorTweetTopStatus":1,"verified":2,"comments":[],"imageCount":0,"langContent":"EN","totalScore":0}],"hots":[{"id":9933296120,"gmtCreate":1662289091260,"gmtModify":1676537031526,"author":{"id":"4117707853567942","authorId":"4117707853567942","name":"Mumusc","avatar":"https://community-static.tradeup.com/news/88a5db2adac3101c48883843b3a38ddf","crmLevel":8,"crmLevelSwitch":1},"themes":[],"htmlText":"Let's go","listText":"Let's go","text":"Let's go","images":[],"top":1,"highlighted":1,"essential":1,"paper":1,"likeSize":3,"commentSize":0,"repostSize":0,"link":"https://ttm.financial/post/9933296120","repostId":"2264541477","repostType":4,"repost":{"id":"2264541477","pubTimestamp":1662257511,"share":"https://www.laohu8.com/m/news/2264541477?lang=&edition=full","pubTime":"2022-09-04 10:11","market":"us","language":"en","title":"1 Big Reason Why Nvidia's Second-Quarter Earnings Results Underwhelmed Investors","url":"https://stock-news.laohu8.com/highlight/detail?id=2264541477","media":"Motley Fool","summary":"Although this graphics processing unit manufacturer faces strong short-term headwinds, its long-term future remains bright.","content":"<html><head></head><body><p>Semiconductor company<b> Nvidia</b> has benefited significantly over the past five years from manufacturing products that enable virtually every major multiyear trend in the tech industry. Consequently, its outstanding revenue growth over the last several years at one point seemed unstoppable. However, its recently reported fiscal 2023 quarter ended in June proved to be a big disappointment for investors.</p><p>Here is one big reason why Nvidia&#39;s second-quarter earnings results underwhelmed investors.</p><h2>Gaming revenue growth ran into a wall</h2><p>Nvidia&#39;s second-quarter fiscal 2023 total revenues of $6.7 billion were down 19% sequentially and only up 3% year on year. This number was far below the $8.1 billion outlook management provided on the first-quarter fiscal 2023 earnings call. So what was the culprit for this growth slowdown?</p><p>You can point to gaming revenue, a significant part of the total revenue, being down 44% sequentially and 33% year over year. And while management expected a slowdown in gaming due to weak European demand related to the war in Ukraine, COVID-19 lockdowns in China, and the slowing U.S. economy, it did not expect the drop-off to be this drastic.</p><p>Although the company likes to dance around the topic, an unknown but significant amount of crypto sales are included in its gaming segment. The sales channels Nvidia uses for its gaming customers are the same channels that crypto miners use to buy its products. So management claims never to know the extent of crypto sales. Still, they seem to strongly suspect how much crypto demand there actually is; some people have previously accused the company of keeping hidden from investors. For instance, the Securities and Exchange Commission sued and recently settled charges against Nvidia in May 2022 for downplaying the impact of crypto mining on its results in 2018. </p><h2>A nasty cryptocurrency hangover</h2><p>Crypto miners find graphics processing units (GPUs) faster than central processing units, or CPUs, for calculating the math problems involved in mining -- the main reason for high crypto miner demand for Nvidia&#39;s gaming GPUs.</p><p>These GPU sales to crypto miners were a massive benefit for Nvidia in 2021. Every crypto mining farm worldwide bought up vast amounts of Nvidia&#39;s GPUs during the crypto industry&#39;s bull market run last year. Demand for its GPUs went so high that it exceeded Nvidia&#39;s ability to supply the market, creating a GPU shortage. Moreover, it became difficult for true gamers at one point to buy a new graphics card. The company significantly ramped up its GPU production to satisfy this &#34;gaming&#34; demand. The artificial boost to gaming from crypto mining demand made the company inebriated from rapid revenue growth.</p><p><img src=\"https://static.tigerbbs.com/272ff35265ad31a8019f82b563b3aabd\" referrerpolicy=\"no-referrer\"/></p><p>NVDA Revenue (TTM) data by YCharts.</p><p>Unfortunately for this GPU manufacturer, cryptocurrency markets appear negatively impacted by the Federal Reserve raising interest rates. With crypto miner GPU demand diving as the crypto industry experiences a bear market, Nvidia&#39;s projected 2022 GPU demand failed to materialize. As a result, good times have now turned into a hangover for the company.  </p><p>Nvidia is holding onto $1.32 billion of inventory that it wants to sell rapidly at a discount before releasing its next-generation &#34;Lovelace&#34; gaming GPUs. Sadly for current Nvidia investors, it could take some time for this headache to go away. If you invest in this stock, you should not expect a quick rebound in results.</p><h2>Gaming will eventually rebound</h2><p>Fortunately for investors, cryptocurrency is not part of Nvidia&#39;s long-term investing thesis. While crypto has been lucrative in the past, the crypto market has a history of being so volatile that this business presents the company with far more problems than benefits. Management has built measures into its GPUs to make them less effective for crypto mining. It seems that the company wants to minimize the impact of crypto on its business.</p><p>Management believes in the gaming business, excluding crypto, over the long term. Chief Financial Officer Colette Kress said on the second-quarter 2023 earnings call that although gaming is navigating significant short-term macroeconomic challenges, the company believes the long-term fundamentals of gaming remain strong. In addition to gaming, Nvidia has several other solid long-term growth drivers across areas, like the data center, automotive, and vision systems that enable the metaverse.</p><p>So, if you are a long-term investor looking for a solid growth stock and can wait out near-term headwinds to its performance, this could be an excellent time to pick up a few shares.</p></body></html>","source":"fool_stock","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>1 Big Reason Why Nvidia's Second-Quarter Earnings Results Underwhelmed Investors</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: 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\">\n1 Big Reason Why Nvidia's Second-Quarter Earnings Results Underwhelmed Investors\n</h2>\n\n<h4 class=\"meta\">\n\n\n2022-09-04 10:11 GMT+8&nbsp;&nbsp;&nbsp;<a href=https://www.fool.com/investing/2022/09/03/1-big-reason-why-nvidias-second-quarter-earnings-r/><strong>Motley Fool</strong></a>\n\n\n</h4>\n\n</header>\n<article>\n<div>\n<p>Semiconductor company Nvidia has benefited significantly over the past five years from manufacturing products that enable virtually every major multiyear trend in the tech industry. Consequently, its ...</p>\n\n<a href=\"https://www.fool.com/investing/2022/09/03/1-big-reason-why-nvidias-second-quarter-earnings-r/\">Web Link</a>\n\n</div>\n\n\n</article>\n</div>\n</body>\n</html>\n","type":0,"thumbnail":"","relate_stocks":{"NVDA":"英伟达"},"source_url":"https://www.fool.com/investing/2022/09/03/1-big-reason-why-nvidias-second-quarter-earnings-r/","is_english":true,"share_image_url":"https://static.laohu8.com/e9f99090a1c2ed51c021029395664489","article_id":"2264541477","content_text":"Semiconductor company Nvidia has benefited significantly over the past five years from manufacturing products that enable virtually every major multiyear trend in the tech industry. Consequently, its outstanding revenue growth over the last several years at one point seemed unstoppable. However, its recently reported fiscal 2023 quarter ended in June proved to be a big disappointment for investors.Here is one big reason why Nvidia's second-quarter earnings results underwhelmed investors.Gaming revenue growth ran into a wallNvidia's second-quarter fiscal 2023 total revenues of $6.7 billion were down 19% sequentially and only up 3% year on year. This number was far below the $8.1 billion outlook management provided on the first-quarter fiscal 2023 earnings call. So what was the culprit for this growth slowdown?You can point to gaming revenue, a significant part of the total revenue, being down 44% sequentially and 33% year over year. And while management expected a slowdown in gaming due to weak European demand related to the war in Ukraine, COVID-19 lockdowns in China, and the slowing U.S. economy, it did not expect the drop-off to be this drastic.Although the company likes to dance around the topic, an unknown but significant amount of crypto sales are included in its gaming segment. The sales channels Nvidia uses for its gaming customers are the same channels that crypto miners use to buy its products. So management claims never to know the extent of crypto sales. Still, they seem to strongly suspect how much crypto demand there actually is; some people have previously accused the company of keeping hidden from investors. For instance, the Securities and Exchange Commission sued and recently settled charges against Nvidia in May 2022 for downplaying the impact of crypto mining on its results in 2018. A nasty cryptocurrency hangoverCrypto miners find graphics processing units (GPUs) faster than central processing units, or CPUs, for calculating the math problems involved in mining -- the main reason for high crypto miner demand for Nvidia's gaming GPUs.These GPU sales to crypto miners were a massive benefit for Nvidia in 2021. Every crypto mining farm worldwide bought up vast amounts of Nvidia's GPUs during the crypto industry's bull market run last year. Demand for its GPUs went so high that it exceeded Nvidia's ability to supply the market, creating a GPU shortage. Moreover, it became difficult for true gamers at one point to buy a new graphics card. The company significantly ramped up its GPU production to satisfy this \"gaming\" demand. The artificial boost to gaming from crypto mining demand made the company inebriated from rapid revenue growth.NVDA Revenue (TTM) data by YCharts.Unfortunately for this GPU manufacturer, cryptocurrency markets appear negatively impacted by the Federal Reserve raising interest rates. With crypto miner GPU demand diving as the crypto industry experiences a bear market, Nvidia's projected 2022 GPU demand failed to materialize. As a result, good times have now turned into a hangover for the company.  Nvidia is holding onto $1.32 billion of inventory that it wants to sell rapidly at a discount before releasing its next-generation \"Lovelace\" gaming GPUs. Sadly for current Nvidia investors, it could take some time for this headache to go away. If you invest in this stock, you should not expect a quick rebound in results.Gaming will eventually reboundFortunately for investors, cryptocurrency is not part of Nvidia's long-term investing thesis. While crypto has been lucrative in the past, the crypto market has a history of being so volatile that this business presents the company with far more problems than benefits. Management has built measures into its GPUs to make them less effective for crypto mining. It seems that the company wants to minimize the impact of crypto on its business.Management believes in the gaming business, excluding crypto, over the long term. Chief Financial Officer Colette Kress said on the second-quarter 2023 earnings call that although gaming is navigating significant short-term macroeconomic challenges, the company believes the long-term fundamentals of gaming remain strong. In addition to gaming, Nvidia has several other solid long-term growth drivers across areas, like the data center, automotive, and vision systems that enable the metaverse.So, if you are a long-term investor looking for a solid growth stock and can wait out near-term headwinds to its performance, this could be an excellent time to pick up a few shares.","news_type":1},"isVote":1,"tweetType":1,"viewCount":189,"authorTweetTopStatus":1,"verified":2,"comments":[],"imageCount":0,"langContent":"EN","totalScore":0},{"id":9942203169,"gmtCreate":1681223451552,"gmtModify":1681225343830,"author":{"id":"4117707853567942","authorId":"4117707853567942","name":"Mumusc","avatar":"https://community-static.tradeup.com/news/88a5db2adac3101c48883843b3a38ddf","crmLevel":8,"crmLevelSwitch":1},"themes":[],"htmlText":"How to make tiger jump faster","listText":"How to make tiger jump faster","text":"How to make tiger jump faster","images":[],"top":1,"highlighted":1,"essential":1,"paper":1,"likeSize":1,"commentSize":0,"repostSize":0,"link":"https://ttm.financial/post/9942203169","repostId":"9943960936","repostType":1,"repost":{"id":9943960936,"gmtCreate":1679046534725,"gmtModify":1680580626622,"author":{"id":"3527667667103859","authorId":"3527667667103859","name":"TigerEvents","avatar":"https://community-static.tradeup.com/news/61ed9b39c6cbcdce6372edc1c0b48a2d","crmLevel":1,"crmLevelSwitch":0},"themes":[],"title":"【Game】Easter Egg Hunting with Tiger, Win Disney Shares and USD 120 Voucher","htmlText":"🐰🌷 Hop into the Easter spirit and join our \"Tiger's Egg Hunting\" game! 🎉Stand to win free Disney stocks and a USD 120 cash voucher!🎁🌟Our interactive Easter game is open to Tigers, and it's so easy to play! Simply jump and catch the egg, and you could be a lucky winner. 🐇That's not all. You can also invite your friends to join in the fun to earn more points. Plus, you can challenge your friends for a race up the leaderboard. Let's fly to the moon together!Don't miss out on this egg-citing opportunity to win BIG! Join the game now and hop on your way to victory. 🥳🐣<a href=\"https://www.tigerbrokers.com.sg/activity/market/2023/easter/?adcode=20230316162207#/\" target=\"_blank\">Join our Easter campaign now</a>","listText":"🐰🌷 Hop into the Easter spirit and join our \"Tiger's Egg Hunting\" game! 🎉Stand to win free Disney stocks and a USD 120 cash voucher!🎁🌟Our interactive Easter game is open to Tigers, and it's so easy to play! Simply jump and catch the egg, and you could be a lucky winner. 🐇That's not all. You can also invite your friends to join in the fun to earn more points. Plus, you can challenge your friends for a race up the leaderboard. Let's fly to the moon together!Don't miss out on this egg-citing opportunity to win BIG! Join the game now and hop on your way to victory. 🥳🐣<a href=\"https://www.tigerbrokers.com.sg/activity/market/2023/easter/?adcode=20230316162207#/\" target=\"_blank\">Join our Easter campaign now</a>","text":"🐰🌷 Hop into the Easter spirit and join our \"Tiger's Egg Hunting\" game! 🎉Stand to win free Disney stocks and a USD 120 cash voucher!🎁🌟Our interactive Easter game is open to Tigers, and it's so easy to play! Simply jump and catch the egg, and you could be a lucky winner. 🐇That's not all. You can also invite your friends to join in the fun to earn more points. Plus, you can challenge your friends for a race up the leaderboard. Let's fly to the moon together!Don't miss out on this egg-citing opportunity to win BIG! Join the game now and hop on your way to victory. 🥳🐣Join our Easter campaign now","images":[{"img":"https://community-static.tradeup.com/news/c90a7371a3bcd1e6c552d2aa23f72c33","width":"1200","height":"630"}],"top":1,"highlighted":1,"essential":2,"paper":2,"likeSize":0,"commentSize":0,"repostSize":0,"link":"https://ttm.financial/post/9943960936","isVote":1,"tweetType":1,"viewCount":0,"authorTweetTopStatus":1,"verified":2,"comments":[],"imageCount":1,"langContent":"EN","totalScore":0},"isVote":1,"tweetType":1,"viewCount":21,"authorTweetTopStatus":1,"verified":2,"comments":[],"imageCount":0,"langContent":"EN","totalScore":0},{"id":9994874806,"gmtCreate":1661612705110,"gmtModify":1676536549590,"author":{"id":"4117707853567942","authorId":"4117707853567942","name":"Mumusc","avatar":"https://community-static.tradeup.com/news/88a5db2adac3101c48883843b3a38ddf","crmLevel":8,"crmLevelSwitch":1},"themes":[],"htmlText":"Hello world","listText":"Hello world","text":"Hello world","images":[],"top":1,"highlighted":1,"essential":1,"paper":1,"likeSize":1,"commentSize":0,"repostSize":0,"link":"https://ttm.financial/post/9994874806","isVote":1,"tweetType":1,"viewCount":50,"authorTweetTopStatus":1,"verified":2,"comments":[],"imageCount":0,"langContent":"EN","totalScore":0},{"id":278042946449672,"gmtCreate":1708907592190,"gmtModify":1708907595634,"author":{"id":"4117707853567942","authorId":"4117707853567942","name":"Mumusc","avatar":"https://community-static.tradeup.com/news/88a5db2adac3101c48883843b3a38ddf","crmLevel":8,"crmLevelSwitch":1},"themes":[],"htmlText":"Great ariticle, would you like to share it?","listText":"Great ariticle, would you like to share it?","text":"Great ariticle, would you like to share it?","images":[],"top":1,"highlighted":1,"essential":1,"paper":1,"likeSize":0,"commentSize":0,"repostSize":0,"link":"https://ttm.financial/post/278042946449672","repostId":"2414236065","repostType":2,"repost":{"id":"2414236065","weMediaInfo":{"introduction":"Dow Jones publishes the world’s most trusted business news and financial information in a variety of media.","home_visible":0,"media_name":"Dow Jones","id":"106","head_image":"https://static.tigerbbs.com/150f88aa4d182df19190059f4a365e99"},"pubTimestamp":1708903810,"share":"https://www.laohu8.com/m/news/2414236065?lang=&edition=full","pubTime":"2024-02-26 07:30","market":"hk","language":"en","title":"How a Shifting AI Chip Market Will Shape Nvidia's Future","url":"https://stock-news.laohu8.com/highlight/detail?id=2414236065","media":"Dow Jones","summary":"The AI chip battle that Nvidia has dominated is already shifting to a new front -- one that will be much larger but also more competitive.Nvidia built itself into a $2 trillion company by supplying the chips essential for the incredibly complicated work of training artificial-intelligence models. As the industry rapidly evolves, the bigger opportunity will be selling chips that make those models run after they are trained, churning out text and images for the fast-growing population of companies and people actually using generative AI tools.Kress's comments allayed some concerns that the shift toward chips for deploying AI systems -- those that do what is called \"inference\" work -- threatens Nvidia's position because that work can be done with less-powerful and less-expensive chips than those that have made Nvidia the leader of the AI boom.The rate at which inference is growing may be faster than earlier expected. Ea","content":"<html><head></head><body><p>The AI chip battle that Nvidia has dominated is already shifting to a new front -- one that will be much larger but also more competitive.</p><p>Nvidia built itself into a $2 trillion company by supplying the chips essential for the incredibly complicated work of training artificial-intelligence models. As the industry rapidly evolves, the bigger opportunity will be selling chips that make those models run after they are trained, churning out text and images for the fast-growing population of companies and people actually using generative AI tools.</p><p>Right now, that shift is adding to Nvidia&#39;s blockbuster sales. Chief Financial Officer Colette Kress said this past week that more than 40% of Nvidia&#39;s data center business in the past year -- when revenue exceeded $47 billion -- was for deployment of AI systems and not training. That percentage was the first significant indication that the shift is under way.</p><p>Kress&#39;s comments allayed some concerns that the shift toward chips for deploying AI systems -- those that do what is called &#34;inference&#34; work -- threatens Nvidia&#39;s position because that work can be done with less-powerful and less-expensive chips than those that have made Nvidia the leader of the AI boom.</p><p>&#34;There is a perception that Nvidia&#39;s share will be lower in inferencing vs. training,&#34; Ben Reitzes, an analyst at Melius Research, said in a note to clients. &#34;This revelation helps shed light on its ability to benefit from the coming inferencing explosion.&#34;</p><p>Many rivals believe they have a better shot in the AI market as chips for inferencing become more important.</p><p>Intel, which makes central processing units that go into data centers, believes its chips will be increasingly appealing as customers focus on driving down the cost of operating AI models. The kinds of chips Intel specializes in are already widely used in inferencing, and it isn&#39;t as critical to have Nvidia&#39;s cutting-edge and more expensive H100 AI chips when doing that task.</p><p>&#34;The economics of inferencing are, I&#39;m not going to stand up $40,000 H100 environments that suck too much power and require new management and security models and new IT infrastructure,&#34; Intel Chief Executive Pat Gelsinger said in an interview in December. &#34;If I can run those models on standard [Intel chips], it&#39;s a no-brainer.&#34;</p><p>Vivek Arya, an analyst at Bank of America, said the shift toward inference was perhaps the most significant news to emerge Wednesday from Nvidia&#39;s quarterly earnings report, which beat Wall Street forecasts and led its stock to climb 8.5% for the week, pushing the company to a roughly $2 trillion valuation.</p><p>Arya said inference would rise as the focus shifts to generating revenue from AI models following a surge of investment in training them. That could be more competitive compared with AI training, where Nvidia dominates.</p><p>The rate at which inference is growing may be faster than earlier expected. Early this year, UBS analysts estimated 90% of chip demand stemmed from training, and that inference would drive just 20% of the market by next year. Nvidia deriving about 40% of its data center revenue from inference was &#34;a bigger number than we would expect,&#34; the analysts said in a note.</p><p>Indeed, Nvidia&#39;s financial results Wednesday suggest its market share in AI chips of more than 80% isn&#39;t yet being challenged in a serious way. Nvidia&#39;s chips used for training AI systems are expected to remain in high demand for the foreseeable future.</p><p>In training AI systems, companies run vast oceans of data through their models to teach them to predict language in a way that enables human-sounding expression. The work requires enormous computing ability that is well suited to Nvidia&#39;s graphics processing units, or GPUs.</p><p>Inference work is when those models are asked to process new bits of information and respond -- a lighter lift.</p><p>In addition to Nvidia&#39;s established competitors like Intel and Advanced Micro Devices, a number of AI-chip startups may also gain steam as inference takes center stage.</p><p>&#34;We&#39;re seeing our inference use case exploding,&#34; said Rodrigo Liang, chief executive of SambaNova, a startup that makes a combination of AI chips and software that can do both inferencing and training. &#34;People are starting to realize that 80%-plus of the cost is going to be in inferencing, and I need to look for alternate solutions,&#34; he said.</p><p>Groq, a startup founded by former Google AI-chip engineer Jonathan Ross, also has seen a surge of interest in recent months after a demo on the company&#39;s home page showed how quickly its inference chips could generate responses from a large language model. The company is on track to deploy 42,000 of its chips this year and one million next year, but is exploring increasing those totals to 220,000 this year and 1.5 million next year, Ross said.</p><p>One factor driving the shift, he said, was that some of the most advanced AI systems were being tuned to produce better responses without retraining them, pushing more of the computational work into inference. And Groq&#39;s specialist chips, he said, were significantly quicker and cheaper to run than Nvidia&#39;s or other chip companies&#39; offerings.</p><p>&#34;For inference, what you can deploy depends on cost,&#34; he said. &#34;There are a bunch of models that would get trained at Google that worked but about 80% of them didn&#39;t get deployed because they were too expensive to put into production.&#34;</p><p>Big tech companies -- including Meta, Microsoft, Alphabet&#39;s Google and Amazon.com -- have been working to develop inference chips in-house, recognizing the coming shift and the benefits of being able to do inference more cheaply.</p><p>Amazon, for example, has had inference chips since 2018, and inference represents 40% of computing costs for its Alexa smart assistant, Swami Sivasubramanian, a vice president of data and machine learning at the company&#39;s cloud-computing arm, said last year.</p><p>Nvidia, for its part, is seeking to stay on top as the transition toward inference proceeds. A coming chip posted industry-leading results last year in a key AI inference benchmark, extending the company&#39;s yearslong dominance in the competition.</p><p>In December, after AMD unveiled new AI chips that it said were better than Nvidia&#39;s at inference, Nvidia fired back in a blog post disputing the claims. AMD didn&#39;t use optimized software in making its performance claims, Nvidia said, and if it did, Nvidia&#39;s chips would be twice as fast.</p></body></html>","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>How a Shifting AI Chip Market Will Shape Nvidia's Future</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: 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\">\nHow a Shifting AI Chip Market Will Shape Nvidia's Future\n</h2>\n\n<h4 class=\"meta\">\n\n\n<div class=\"head\" \">\n\n\n<div class=\"h-thumb\" style=\"background-image:url(https://static.tigerbbs.com/150f88aa4d182df19190059f4a365e99);background-size:cover;\"></div>\n\n<div class=\"h-content\">\n<p class=\"h-name\">Dow Jones </p>\n<p class=\"h-time\">2024-02-26 07:30</p>\n</div>\n\n</div>\n\n\n</h4>\n\n</header>\n<article>\n<html><head></head><body><p>The AI chip battle that Nvidia has dominated is already shifting to a new front -- one that will be much larger but also more competitive.</p><p>Nvidia built itself into a $2 trillion company by supplying the chips essential for the incredibly complicated work of training artificial-intelligence models. As the industry rapidly evolves, the bigger opportunity will be selling chips that make those models run after they are trained, churning out text and images for the fast-growing population of companies and people actually using generative AI tools.</p><p>Right now, that shift is adding to Nvidia&#39;s blockbuster sales. Chief Financial Officer Colette Kress said this past week that more than 40% of Nvidia&#39;s data center business in the past year -- when revenue exceeded $47 billion -- was for deployment of AI systems and not training. That percentage was the first significant indication that the shift is under way.</p><p>Kress&#39;s comments allayed some concerns that the shift toward chips for deploying AI systems -- those that do what is called &#34;inference&#34; work -- threatens Nvidia&#39;s position because that work can be done with less-powerful and less-expensive chips than those that have made Nvidia the leader of the AI boom.</p><p>&#34;There is a perception that Nvidia&#39;s share will be lower in inferencing vs. training,&#34; Ben Reitzes, an analyst at Melius Research, said in a note to clients. &#34;This revelation helps shed light on its ability to benefit from the coming inferencing explosion.&#34;</p><p>Many rivals believe they have a better shot in the AI market as chips for inferencing become more important.</p><p>Intel, which makes central processing units that go into data centers, believes its chips will be increasingly appealing as customers focus on driving down the cost of operating AI models. The kinds of chips Intel specializes in are already widely used in inferencing, and it isn&#39;t as critical to have Nvidia&#39;s cutting-edge and more expensive H100 AI chips when doing that task.</p><p>&#34;The economics of inferencing are, I&#39;m not going to stand up $40,000 H100 environments that suck too much power and require new management and security models and new IT infrastructure,&#34; Intel Chief Executive Pat Gelsinger said in an interview in December. &#34;If I can run those models on standard [Intel chips], it&#39;s a no-brainer.&#34;</p><p>Vivek Arya, an analyst at Bank of America, said the shift toward inference was perhaps the most significant news to emerge Wednesday from Nvidia&#39;s quarterly earnings report, which beat Wall Street forecasts and led its stock to climb 8.5% for the week, pushing the company to a roughly $2 trillion valuation.</p><p>Arya said inference would rise as the focus shifts to generating revenue from AI models following a surge of investment in training them. That could be more competitive compared with AI training, where Nvidia dominates.</p><p>The rate at which inference is growing may be faster than earlier expected. Early this year, UBS analysts estimated 90% of chip demand stemmed from training, and that inference would drive just 20% of the market by next year. Nvidia deriving about 40% of its data center revenue from inference was &#34;a bigger number than we would expect,&#34; the analysts said in a note.</p><p>Indeed, Nvidia&#39;s financial results Wednesday suggest its market share in AI chips of more than 80% isn&#39;t yet being challenged in a serious way. Nvidia&#39;s chips used for training AI systems are expected to remain in high demand for the foreseeable future.</p><p>In training AI systems, companies run vast oceans of data through their models to teach them to predict language in a way that enables human-sounding expression. The work requires enormous computing ability that is well suited to Nvidia&#39;s graphics processing units, or GPUs.</p><p>Inference work is when those models are asked to process new bits of information and respond -- a lighter lift.</p><p>In addition to Nvidia&#39;s established competitors like Intel and Advanced Micro Devices, a number of AI-chip startups may also gain steam as inference takes center stage.</p><p>&#34;We&#39;re seeing our inference use case exploding,&#34; said Rodrigo Liang, chief executive of SambaNova, a startup that makes a combination of AI chips and software that can do both inferencing and training. &#34;People are starting to realize that 80%-plus of the cost is going to be in inferencing, and I need to look for alternate solutions,&#34; he said.</p><p>Groq, a startup founded by former Google AI-chip engineer Jonathan Ross, also has seen a surge of interest in recent months after a demo on the company&#39;s home page showed how quickly its inference chips could generate responses from a large language model. The company is on track to deploy 42,000 of its chips this year and one million next year, but is exploring increasing those totals to 220,000 this year and 1.5 million next year, Ross said.</p><p>One factor driving the shift, he said, was that some of the most advanced AI systems were being tuned to produce better responses without retraining them, pushing more of the computational work into inference. And Groq&#39;s specialist chips, he said, were significantly quicker and cheaper to run than Nvidia&#39;s or other chip companies&#39; offerings.</p><p>&#34;For inference, what you can deploy depends on cost,&#34; he said. &#34;There are a bunch of models that would get trained at Google that worked but about 80% of them didn&#39;t get deployed because they were too expensive to put into production.&#34;</p><p>Big tech companies -- including Meta, Microsoft, Alphabet&#39;s Google and Amazon.com -- have been working to develop inference chips in-house, recognizing the coming shift and the benefits of being able to do inference more cheaply.</p><p>Amazon, for example, has had inference chips since 2018, and inference represents 40% of computing costs for its Alexa smart assistant, Swami Sivasubramanian, a vice president of data and machine learning at the company&#39;s cloud-computing arm, said last year.</p><p>Nvidia, for its part, is seeking to stay on top as the transition toward inference proceeds. A coming chip posted industry-leading results last year in a key AI inference benchmark, extending the company&#39;s yearslong dominance in the competition.</p><p>In December, after AMD unveiled new AI chips that it said were better than Nvidia&#39;s at inference, Nvidia fired back in a blog post disputing the claims. AMD didn&#39;t use optimized software in making its performance claims, Nvidia said, and if it did, Nvidia&#39;s chips would be twice as fast.</p></body></html>\n\n</article>\n</div>\n</body>\n</html>\n","type":0,"thumbnail":"","relate_stocks":{"BK4534":"瑞士信贷持仓","BK4567":"ESG概念","BK4533":"AQR资本管理(全球第二大对冲基金)","IE00B1XK9C88.USD":"PINEBRIDGE US LARGE CAP RESEARCH ENHANCED \"A\" (USD) ACC","BK4525":"远程办公概念","GOOGL":"谷歌A","IE00BMPRXR70.SGD":"Neuberger Berman 5G Connectivity A Acc SGD-H","IE00BSNM7G36.USD":"NEUBERGER BERMAN SYSTEMATIC GLOBAL SUSTAINABLE VALUE \"A\" (USD) ACC","LU0494093205.USD":"贝莱德ESG灵活多元资产A2 USD-H","LU0128525689.USD":"TEMPLETON GLOBAL BALANCED \"A\"(USD) ACC","BK4535":"淡马锡持仓","GOOG":"谷歌","LU0310799852.SGD":"FTIF - Templeton Global Equity Income A MDIS SGD","BK4527":"明星科技股","BK4538":"云计算","BK4543":"AI","IE00BZ1G4Q59.USD":"LEGG MASON CLEARBRIDGE US EQUITY SUSTAINABILITY LEADER \"A\"(USD) INC (A)","BK4550":"红杉资本持仓","NVDA":"英伟达","IE00BWXC8680.SGD":"PINEBRIDGE US LARGE CAP RESEARCH ENHANCED \"A5\" (SGD) ACC","LU0979878070.USD":"FULLERTON LUX FUNDS - ASIA ABSOLUTE ALPHA \"A\" (USD) ACC","LU0158827948.USD":"ALLIANZ GLOBAL SUSTAINABILITY \"A\" (USD) INC","LU0320765059.SGD":"FTIF - Franklin US Opportunities A Acc SGD","LU1814569148.SGD":"WELLINGTON GLOBAL QUALITY GROWTH \"D\" (SGDHDG) ACC","BK4574":"无人驾驶","LU1852331112.SGD":"Blackrock World Technology Fund A2 SGD-H","AMD":"美国超微公司","BK4573":"虚拟现实","INTC":"英特尔","LU1691799644.USD":"Amundi Funds Polen Capital Global Growth A2 (C) USD","LU0672654240.SGD":"FTIF - Franklin US Opportunities A Acc SGD-H1","IE00B1BXHZ80.USD":"Legg Mason ClearBridge - US Appreciation A Acc USD","LU0965509283.SGD":"AB LOW VOLATILITY EQUITY PORTFOLIO \"AD\" (SGDHDG) INC","BK4581":"高盛持仓","LU0354030511.USD":"ALLSPRING  U.S. LARGE CAP GROWTH \"I\" (USD) ACC","BK4549":"软银资本持仓","IE00BJTD4V19.USD":"NEUBERGER BERMAN US LONG SHORT EQUITY \"A1\" (USD) ACC","LU0354030438.USD":"富国美国大盘成长基金Cl A Acc","IE0009356076.USD":"JANUS HENDERSON GLOBAL TECHNOLOGY AND INNOVATION \"A2\" (USD) ACC","BK4548":"巴美列捷福持仓","LU0256863811.USD":"ALLIANZ US EQUITY \"A\" INC","LU0348723411.USD":"ALLIANZ GLOBAL HI-TECH GROWTH \"A\" (USD) INC","LU0029864427.USD":"TEMPLETON GLOBAL \"A\" (USD) INC","IE00BFSS7M15.SGD":"Janus Henderson Balanced A Acc SGD-H","BK4554":"元宇宙及AR概念","BK4532":"文艺复兴科技持仓","IE00B3S45H60.SGD":"Neuberger Berman US Multicap Opportunities A Acc SGD-H","LU1988902786.USD":"FULLERTON LUX FUNDS GLOBAL ABSOLUTE ALPHA \"I\" (USD) ACC","LU0417517546.SGD":"Allianz US Equity Cl AT Acc SGD","BK4553":"喜马拉雅资本持仓"},"source_url":"https://dowjonesnews.com/newdjn/logon.aspx?AL=N","is_english":true,"share_image_url":"https://static.laohu8.com/e9f99090a1c2ed51c021029395664489","article_id":"2414236065","content_text":"The AI chip battle that Nvidia has dominated is already shifting to a new front -- one that will be much larger but also more competitive.Nvidia built itself into a $2 trillion company by supplying the chips essential for the incredibly complicated work of training artificial-intelligence models. As the industry rapidly evolves, the bigger opportunity will be selling chips that make those models run after they are trained, churning out text and images for the fast-growing population of companies and people actually using generative AI tools.Right now, that shift is adding to Nvidia's blockbuster sales. Chief Financial Officer Colette Kress said this past week that more than 40% of Nvidia's data center business in the past year -- when revenue exceeded $47 billion -- was for deployment of AI systems and not training. That percentage was the first significant indication that the shift is under way.Kress's comments allayed some concerns that the shift toward chips for deploying AI systems -- those that do what is called \"inference\" work -- threatens Nvidia's position because that work can be done with less-powerful and less-expensive chips than those that have made Nvidia the leader of the AI boom.\"There is a perception that Nvidia's share will be lower in inferencing vs. training,\" Ben Reitzes, an analyst at Melius Research, said in a note to clients. \"This revelation helps shed light on its ability to benefit from the coming inferencing explosion.\"Many rivals believe they have a better shot in the AI market as chips for inferencing become more important.Intel, which makes central processing units that go into data centers, believes its chips will be increasingly appealing as customers focus on driving down the cost of operating AI models. The kinds of chips Intel specializes in are already widely used in inferencing, and it isn't as critical to have Nvidia's cutting-edge and more expensive H100 AI chips when doing that task.\"The economics of inferencing are, I'm not going to stand up $40,000 H100 environments that suck too much power and require new management and security models and new IT infrastructure,\" Intel Chief Executive Pat Gelsinger said in an interview in December. \"If I can run those models on standard [Intel chips], it's a no-brainer.\"Vivek Arya, an analyst at Bank of America, said the shift toward inference was perhaps the most significant news to emerge Wednesday from Nvidia's quarterly earnings report, which beat Wall Street forecasts and led its stock to climb 8.5% for the week, pushing the company to a roughly $2 trillion valuation.Arya said inference would rise as the focus shifts to generating revenue from AI models following a surge of investment in training them. That could be more competitive compared with AI training, where Nvidia dominates.The rate at which inference is growing may be faster than earlier expected. Early this year, UBS analysts estimated 90% of chip demand stemmed from training, and that inference would drive just 20% of the market by next year. Nvidia deriving about 40% of its data center revenue from inference was \"a bigger number than we would expect,\" the analysts said in a note.Indeed, Nvidia's financial results Wednesday suggest its market share in AI chips of more than 80% isn't yet being challenged in a serious way. Nvidia's chips used for training AI systems are expected to remain in high demand for the foreseeable future.In training AI systems, companies run vast oceans of data through their models to teach them to predict language in a way that enables human-sounding expression. The work requires enormous computing ability that is well suited to Nvidia's graphics processing units, or GPUs.Inference work is when those models are asked to process new bits of information and respond -- a lighter lift.In addition to Nvidia's established competitors like Intel and Advanced Micro Devices, a number of AI-chip startups may also gain steam as inference takes center stage.\"We're seeing our inference use case exploding,\" said Rodrigo Liang, chief executive of SambaNova, a startup that makes a combination of AI chips and software that can do both inferencing and training. \"People are starting to realize that 80%-plus of the cost is going to be in inferencing, and I need to look for alternate solutions,\" he said.Groq, a startup founded by former Google AI-chip engineer Jonathan Ross, also has seen a surge of interest in recent months after a demo on the company's home page showed how quickly its inference chips could generate responses from a large language model. The company is on track to deploy 42,000 of its chips this year and one million next year, but is exploring increasing those totals to 220,000 this year and 1.5 million next year, Ross said.One factor driving the shift, he said, was that some of the most advanced AI systems were being tuned to produce better responses without retraining them, pushing more of the computational work into inference. And Groq's specialist chips, he said, were significantly quicker and cheaper to run than Nvidia's or other chip companies' offerings.\"For inference, what you can deploy depends on cost,\" he said. \"There are a bunch of models that would get trained at Google that worked but about 80% of them didn't get deployed because they were too expensive to put into production.\"Big tech companies -- including Meta, Microsoft, Alphabet's Google and Amazon.com -- have been working to develop inference chips in-house, recognizing the coming shift and the benefits of being able to do inference more cheaply.Amazon, for example, has had inference chips since 2018, and inference represents 40% of computing costs for its Alexa smart assistant, Swami Sivasubramanian, a vice president of data and machine learning at the company's cloud-computing arm, said last year.Nvidia, for its part, is seeking to stay on top as the transition toward inference proceeds. A coming chip posted industry-leading results last year in a key AI inference benchmark, extending the company's yearslong dominance in the competition.In December, after AMD unveiled new AI chips that it said were better than Nvidia's at inference, Nvidia fired back in a blog post disputing the claims. AMD didn't use optimized software in making its performance claims, Nvidia said, and if it did, Nvidia's chips would be twice as fast.","news_type":1},"isVote":1,"tweetType":1,"viewCount":77,"authorTweetTopStatus":1,"verified":2,"comments":[],"imageCount":0,"langContent":"EN","totalScore":0},{"id":259712913551376,"gmtCreate":1704416985716,"gmtModify":1704416990713,"author":{"id":"4117707853567942","authorId":"4117707853567942","name":"Mumusc","avatar":"https://community-static.tradeup.com/news/88a5db2adac3101c48883843b3a38ddf","crmLevel":8,"crmLevelSwitch":1},"themes":[],"htmlText":"May the force be with you all in 2024","listText":"May the force be with you all in 2024","text":"May the force be with you all in 2024","images":[],"top":1,"highlighted":1,"essential":1,"paper":1,"likeSize":0,"commentSize":0,"repostSize":0,"link":"https://ttm.financial/post/259712913551376","isVote":1,"tweetType":1,"viewCount":123,"authorTweetTopStatus":1,"verified":2,"comments":[],"imageCount":0,"langContent":"EN","totalScore":0},{"id":9970603124,"gmtCreate":1684331440669,"gmtModify":1684332473046,"author":{"id":"4117707853567942","authorId":"4117707853567942","name":"Mumusc","avatar":"https://community-static.tradeup.com/news/88a5db2adac3101c48883843b3a38ddf","crmLevel":8,"crmLevelSwitch":1},"themes":[],"htmlText":"What's the view on Vix index?","listText":"What's the view on Vix index?","text":"What's the view on Vix index?","images":[],"top":1,"highlighted":1,"essential":1,"paper":1,"likeSize":0,"commentSize":0,"repostSize":0,"link":"https://ttm.financial/post/9970603124","isVote":1,"tweetType":1,"viewCount":116,"authorTweetTopStatus":1,"verified":2,"comments":[],"imageCount":0,"langContent":"EN","totalScore":0}],"lives":[]}