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2023-05-10

@空军大队长

Is the era of Nvidia's dominance over? ChatGPT ignites a chip war between Google and Microsoft, with Amazon also entering the fray.

2020-04-25

$Tilray Inc.(TLRY)$$特斯拉(TSLA)$$波音(BA)$$亚马逊(AMZN)$$Zoom(ZM)$$苹果(AAPL)$Are u OK?

2020-03-24

$GLD 20200331 161.0 CALL(GLD)$Thank you, Fed

2019-12-30

$蔚来(NIO)$ 4.35

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href=\"https://laohu8.com/U/74125836878304\">@空军大队长 &nbsp;</a>","listText":"<a href=\"https://laohu8.com/U/74125836878304\">@空军大队长 &nbsp;</a>","text":"@空军大队长","images":[],"top":1,"highlighted":1,"essential":1,"paper":1,"likeSize":0,"commentSize":0,"repostSize":0,"link":"https://ttm.financial/post/656682686","repostId":"2334722223","repostType":2,"repost":{"id":"2334722223","kind":"news","pubTimestamp":1683641719,"share":"https://ttm.financial/m/news/2334722223?lang=en_US&edition=fundamental","pubTime":"2023-05-09 22:15","market":"us","language":"zh","title":"Is the era of Nvidia's dominance over? ChatGPT ignites a chip war between Google and Microsoft, with Amazon also entering the fray.","url":"https://stock-news.laohu8.com/highlight/detail?id=2334722223","media":"新智元","summary":"ChatGPT引爆了芯片界「百家争鸣」，谷歌、微软、亚马逊纷纷入局芯片大战，英伟达恐怕不再一家独大。ChatGPT爆火之后，谷歌和微软两巨头的AI大战战火，已经烧到了新的领域——服务器芯片。如今，AI","content":"<p><html><head></head><body>ChatGPT has ignited a \"battle of wits\" in the chip industry, with Google, Microsoft, and Amazon all entering the chip war, and Nvidia may no longer have a monopoly. After ChatGPT became a hit,<a href=\"https://laohu8.com/S/GOOG\">Google</a>and<a href=\"https://laohu8.com/S/MSFT\">Microsoft</a>The AI battle between the two giants has spread to a new field—server chips.</p><p>Today, AI and cloud computing have become fiercely contested areas, and chips have become key to reducing costs and winning over commercial customers.</p><p>Originally,<a href=\"https://laohu8.com/S/AMZN\">Amazon</a>Major companies like Microsoft and Google are known for their software, and now they are investing billions of dollars in chip development and production.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/d7e49f3bef3efc25793e385cfb981184\" alt=\"各大科技巨头研发的AI芯片\" title=\"各大科技巨头研发的AI芯片\" tg-width=\"1080\" tg-height=\"607\"/><span>AI chips developed by major tech giants</span></p><p><strong>ChatGPT becomes a sensation, and major manufacturers launch a chip competition.</strong></p><p>According to reports from foreign media outlet The Information and other sources, these three major manufacturers have now launched or plan to launch eight servers and AI chips for internal product development, cloud server rental, or both.</p><p>“If you can create silicon optimized for AI, then a huge victory awaits you,” said Glenn O’Donnell, a director at research firm Forrester.</p><p>Will these tremendous efforts definitely be rewarded?</p><p>The answer is, not necessarily.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/92c58bb9a500fa1734275375f5acd50f\" alt=\"\" title=\"\" tg-width=\"500\" tg-height=\"224\"/></p><p><a href=\"https://laohu8.com/S/INTC\">Intel</a>, AMD and<a href=\"https://laohu8.com/S/NVDA\">NVIDIA</a>They can benefit from economies of scale, but this is far from the case for large technology companies.</p><p>They also face many tricky challenges, such as hiring chip designers and convincing developers to build applications using their custom chips.</p><p>However, major companies have already made remarkable progress in this area.</p><p>According to published performance data, Amazon's Graviton server chips, as well as AI-specific chips released by Amazon and Google, are comparable in performance to traditional chip manufacturers.</p><p>The chips developed by Amazon, Microsoft, and Google for their data centers mainly fall into two categories: standard computing chips and dedicated chips used to train and run machine learning models. It is the latter that powers large language models like ChatGPT.</p><p>Prior to this,<a href=\"https://laohu8.com/S/AAPL\">Apple</a>Chips have been successfully developed for iPhones, iPads, and Macs, improving the handling of some AI tasks. These large manufacturers may have learned inspiration from Apple.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/18da5c3fe29b36b77e2a0c06f1a66858\" alt=\"\" title=\"\" tg-width=\"728\" tg-height=\"382\"/></p><p>Of the three major companies, Amazon is the only cloud service provider that offers two types of chips in its servers. Its acquisition of Israeli chip designer Annapurna Labs in 2015 laid the foundation for this work.</p><p>Google launched a chip for AI workloads in 2015 and is developing a standard server chip to improve server performance in Google Cloud.</p><p>In contrast, Microsoft's chip development started later, in 2019, and recently, Microsoft has accelerated the timeline for launching AI chips specifically designed for LLMs.</p><p>The explosive popularity of ChatGPT has ignited excitement about AI among users worldwide. This further promoted the strategic transformation of the three major manufacturers.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/ca3cd473a03b6e5160c3681d1b2eff89\" alt=\"\" title=\"\" tg-width=\"800\" tg-height=\"534\"/></p><p>ChatGPT runs on Microsoft's Azure cloud and uses tens of thousands of NVIDIA A100 chips. Both ChatGPT and other OpenAI software integrated into Bing and various programs require so much computing power that Microsoft has already allocated server hardware to its internal team developing AI.</p><p>At Amazon, Chief Financial Officer Brian Olsavsky told investors on last week's earnings call that Amazon plans to shift spending from its retail business to AWS, partly due to investments in the infrastructure needed to support ChatGPT.</p><p>At Google, the engineering team responsible for manufacturing tensor processing units has moved to Google Cloud. It is understood that cloud organizations can now develop a roadmap for TPUs and the software running on them, hoping to get cloud customers to rent more TPU-powered servers.</p><p><strong>Google: TPU V4 specially tuned for AI</strong></p><p>Back in 2020, Google deployed the most powerful AI chip at the time—TPU v4—in its own data centers.</p><p>However, it wasn't until April 4th of this year that Google first released the technical details of this AI supercomputer.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/0690ee25b0f2aaecc65aeb54a8db428b\" alt=\"\" title=\"\" tg-width=\"1080\" tg-height=\"593\"/></p><p>Compared to the TPU v3, the TPU v4 has 2.1 times higher performance, and after integrating 4,096 chips, the supercomputer's performance is improved by 10 times.</p><p>Meanwhile, Google also claims that its chips are faster and more energy-efficient than the Nvidia A100. For a system of similar size, the TPU v4 can provide 1.7 times better performance than the NVIDIA A100, while also improving energy efficiency by 1.9 times.</p><p>For a similarly sized system, the TPU v4 is 1.15 times faster than the A100 on BERT and approximately 4.3 times faster than the IPU. For ResNet, TPU v4 is 1.67 times faster and about 4.5 times faster, respectively.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/101223a8a12806cfc2fa2a826f048f98\" alt=\"\" title=\"\" tg-width=\"780\" tg-height=\"452\"/></p><p>In addition, Google has hinted that it is developing a new TPU to compete with the Nvidia H100. Google researcher Jouppi told Reuters that Google has \"the production line for future chips.\"</p><p><strong>Microsoft: Secret Weapon Athena</strong></p><p>Regardless, Microsoft remains eager to try its luck in this chip dispute.</p><p>Previously, it was reported that a 300-person team secretly assembled by Microsoft began developing a custom chip called \"Athena\" in 2019.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/ff32948451fc5fb506c50218077142e3\" alt=\"\" title=\"\" tg-width=\"1080\" tg-height=\"608\"/></p><p>According to the original plan, \"Athena\" would use<a href=\"https://laohu8.com/S/TSM\">TSMC</a>Built using a 5nm process, it is expected to reduce the cost of each chip by one-third.</p><p>If it can be implemented on a large scale next year, Microsoft and OpenAI teams will be able to use \"Athena\" to simultaneously complete model training and inference.</p><p>In this way, the problem of shortage of dedicated computers can be greatly alleviated.</p><p>Bloomberg reported last week that Microsoft's chip division had partnered with AMD to develop the Athena chip, which also caused AMD's stock price to rise 6.5% on Thursday.</p><p>However, an insider said that AMD is not involved, but is developing its own GPUs to compete with Nvidia, and that AMD has been discussing chip design with Microsoft, which is expected to purchase the GPU.</p><p><strong>Amazon: Already a step ahead</strong></p><p>In the chip race against Microsoft and Google, Amazon seems to have taken a lead.</p><p>Over the past decade, Amazon has maintained a competitive advantage over Microsoft and Google in cloud computing services by offering more advanced technology and lower prices.</p><p>Over the next decade, Amazon is expected to continue to maintain its competitive advantage through its internally developed server chip, Graviton.</p><p>As the latest generation of processors, AWS Graviton3 offers up to 25% improvement in computing performance over its predecessor, and up to 2x improvement in floating-point performance. It also supports DDR5 memory, which increases bandwidth by 50% compared to DDR4 memory.</p><p>For machine learning workloads, AWS Graviton3 delivers up to three times the performance of its predecessor and supports bfloat16.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/0d19af72c9e6355e76551f19aa45cb48\" alt=\"\" title=\"\" tg-width=\"1024\" tg-height=\"471\"/></p><p>Cloud services based on Graviton 3 chips are very popular in some regions, even reaching a point where supply falls short of demand.</p><p>Another advantage of Amazon is that it is currently the only cloud provider that offers standard computing chips (Graviton) and AI-specific chips (Inferentia and Trainium) on its servers.</p><p>Back in 2019, Amazon launched its own AI inference chip, Inferentia.</p><p>It enables customers to run large-scale machine learning inference applications such as image recognition, speech recognition, natural language processing, personalization, and fraud detection in the cloud at low cost.</p><p>The latest Inferentia 2 boasts a threefold increase in computing performance, a fourfold increase in total accelerator memory, a fourfold increase in throughput, and a latency reduction of 1/10.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/10067987011c905d7adb326f7e2f0f2c\" alt=\"\" title=\"\" tg-width=\"1080\" tg-height=\"614\"/></p><p>Following the launch of the original Inferentia, Amazon released Trainium, a custom chip it designed primarily for AI training.</p><p>It optimizes deep learning training workloads, including image classification, semantic search, translation, speech recognition, natural language processing, and recommendation engines.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/f3ba57317936b14e104f278577aac690\" alt=\"\" title=\"\" tg-width=\"865\" tg-height=\"382\"/></p><p>In some cases, chip customization can not only reduce costs by an order of magnitude and energy consumption by 1/10, but these customized solutions can also provide customers with better service with lower latency.</p><p><strong>Shaking Nvidia's monopoly won't be easy.</strong></p><p>However, so far, most AI workloads still run on GPUs, and Nvidia produces most of the chips.</p><p>As previously reported, NVIDIA has an 80% market share in discrete GPUs and a 90% market share in high-end GPUs.</p><p>In 20 years, 80.6% of the world's cloud computing and data centers running on AI were powered by NVIDIA GPUs. In 2021, Nvidia stated that approximately 70% of the world's top 500 supercomputers were powered by its own chips.</p><p>Now, even the Microsoft data center that runs ChatGPT uses tens of thousands of NVIDIA A100 GPUs.</p><p>For a long time, whether it's the top-rated ChatGPT or models like Bard and Stable Diffusion, the computing power behind them has always been provided by NVIDIA A100 chips, each worth approximately $10,000.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/c33f78dfa5cc8233ff32625a5924fe86\" alt=\"\" title=\"\" tg-width=\"740\" tg-height=\"416\"/></p><p>Moreover, the A100 has now become a \"mainstay\" for artificial intelligence professionals. The 2022 State of Artificial Intelligence Report also lists some companies using the A100 supercomputer.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/9d220e98d16ada92a6fd5d48e05fb5cf\" alt=\"\" title=\"\" tg-width=\"920\" tg-height=\"582\"/></p><p>It is obvious that Nvidia has monopolized global computing power, dominating the market with its own chips.</p><p>According to industry insiders, compared to general-purpose chips, application-specific integrated circuit (ASIC) chips, which Amazon, Google, and Microsoft have been developing, perform machine learning tasks faster and consume less power.</p><p>When comparing GPUs and ASICs, Director O'Donnell used the following comparison: \"For normal driving, you can use a Prius, but if you have to use four-wheel drive in the mountains, a Jeep Wrangler would be more suitable.\"</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/90829da8a6f5a2d8163bf3716f607e95\" alt=\"\" title=\"\" tg-width=\"1080\" tg-height=\"608\"/></p><p>However, despite all their efforts, Amazon, Google, and Microsoft all face challenges—how to persuade developers to use these AI chips?</p><p>Currently, Nvidia's GPUs dominate, and developers are already familiar with its proprietary programming language CUDA, which is used to create GPU-driven applications.</p><p>If they switched to custom chips from Amazon, Google, or Microsoft, they would have to learn a completely new software language. Would they be willing?</p><p></body></html></p>","source":"lsy1569730104218","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>Is the era of Nvidia's dominance over? ChatGPT ignites a chip war between Google and Microsoft, with Amazon also entering the fray.</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\">\nIs the era of Nvidia's dominance over? ChatGPT ignites a chip war between Google and Microsoft, with Amazon also entering the fray.\n</h2>\n<h4 class=\"meta\">\n<p class=\"head\">\n<strong class=\"h-name small\">新智元</strong><span class=\"h-time small\">2023-05-09 22:15</span>\n</p>\n</h4>\n</header>\n<article>\n<p><html><head></head><body>ChatGPT has ignited a \"battle of wits\" in the chip industry, with Google, Microsoft, and Amazon all entering the chip war, and Nvidia may no longer have a monopoly. After ChatGPT became a hit,<a href=\"https://laohu8.com/S/GOOG\">Google</a>and<a href=\"https://laohu8.com/S/MSFT\">Microsoft</a>The AI battle between the two giants has spread to a new field—server chips.</p><p>Today, AI and cloud computing have become fiercely contested areas, and chips have become key to reducing costs and winning over commercial customers.</p><p>Originally,<a href=\"https://laohu8.com/S/AMZN\">Amazon</a>Major companies like Microsoft and Google are known for their software, and now they are investing billions of dollars in chip development and production.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/d7e49f3bef3efc25793e385cfb981184\" alt=\"各大科技巨头研发的AI芯片\" title=\"各大科技巨头研发的AI芯片\" tg-width=\"1080\" tg-height=\"607\"/><span>AI chips developed by major tech giants</span></p><p><strong>ChatGPT becomes a sensation, and major manufacturers launch a chip competition.</strong></p><p>According to reports from foreign media outlet The Information and other sources, these three major manufacturers have now launched or plan to launch eight servers and AI chips for internal product development, cloud server rental, or both.</p><p>“If you can create silicon optimized for AI, then a huge victory awaits you,” said Glenn O’Donnell, a director at research firm Forrester.</p><p>Will these tremendous efforts definitely be rewarded?</p><p>The answer is, not necessarily.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/92c58bb9a500fa1734275375f5acd50f\" alt=\"\" title=\"\" tg-width=\"500\" tg-height=\"224\"/></p><p><a href=\"https://laohu8.com/S/INTC\">Intel</a>, AMD and<a href=\"https://laohu8.com/S/NVDA\">NVIDIA</a>They can benefit from economies of scale, but this is far from the case for large technology companies.</p><p>They also face many tricky challenges, such as hiring chip designers and convincing developers to build applications using their custom chips.</p><p>However, major companies have already made remarkable progress in this area.</p><p>According to published performance data, Amazon's Graviton server chips, as well as AI-specific chips released by Amazon and Google, are comparable in performance to traditional chip manufacturers.</p><p>The chips developed by Amazon, Microsoft, and Google for their data centers mainly fall into two categories: standard computing chips and dedicated chips used to train and run machine learning models. It is the latter that powers large language models like ChatGPT.</p><p>Prior to this,<a href=\"https://laohu8.com/S/AAPL\">Apple</a>Chips have been successfully developed for iPhones, iPads, and Macs, improving the handling of some AI tasks. These large manufacturers may have learned inspiration from Apple.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/18da5c3fe29b36b77e2a0c06f1a66858\" alt=\"\" title=\"\" tg-width=\"728\" tg-height=\"382\"/></p><p>Of the three major companies, Amazon is the only cloud service provider that offers two types of chips in its servers. Its acquisition of Israeli chip designer Annapurna Labs in 2015 laid the foundation for this work.</p><p>Google launched a chip for AI workloads in 2015 and is developing a standard server chip to improve server performance in Google Cloud.</p><p>In contrast, Microsoft's chip development started later, in 2019, and recently, Microsoft has accelerated the timeline for launching AI chips specifically designed for LLMs.</p><p>The explosive popularity of ChatGPT has ignited excitement about AI among users worldwide. This further promoted the strategic transformation of the three major manufacturers.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/ca3cd473a03b6e5160c3681d1b2eff89\" alt=\"\" title=\"\" tg-width=\"800\" tg-height=\"534\"/></p><p>ChatGPT runs on Microsoft's Azure cloud and uses tens of thousands of NVIDIA A100 chips. Both ChatGPT and other OpenAI software integrated into Bing and various programs require so much computing power that Microsoft has already allocated server hardware to its internal team developing AI.</p><p>At Amazon, Chief Financial Officer Brian Olsavsky told investors on last week's earnings call that Amazon plans to shift spending from its retail business to AWS, partly due to investments in the infrastructure needed to support ChatGPT.</p><p>At Google, the engineering team responsible for manufacturing tensor processing units has moved to Google Cloud. It is understood that cloud organizations can now develop a roadmap for TPUs and the software running on them, hoping to get cloud customers to rent more TPU-powered servers.</p><p><strong>Google: TPU V4 specially tuned for AI</strong></p><p>Back in 2020, Google deployed the most powerful AI chip at the time—TPU v4—in its own data centers.</p><p>However, it wasn't until April 4th of this year that Google first released the technical details of this AI supercomputer.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/0690ee25b0f2aaecc65aeb54a8db428b\" alt=\"\" title=\"\" tg-width=\"1080\" tg-height=\"593\"/></p><p>Compared to the TPU v3, the TPU v4 has 2.1 times higher performance, and after integrating 4,096 chips, the supercomputer's performance is improved by 10 times.</p><p>Meanwhile, Google also claims that its chips are faster and more energy-efficient than the Nvidia A100. For a system of similar size, the TPU v4 can provide 1.7 times better performance than the NVIDIA A100, while also improving energy efficiency by 1.9 times.</p><p>For a similarly sized system, the TPU v4 is 1.15 times faster than the A100 on BERT and approximately 4.3 times faster than the IPU. For ResNet, TPU v4 is 1.67 times faster and about 4.5 times faster, respectively.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/101223a8a12806cfc2fa2a826f048f98\" alt=\"\" title=\"\" tg-width=\"780\" tg-height=\"452\"/></p><p>In addition, Google has hinted that it is developing a new TPU to compete with the Nvidia H100. Google researcher Jouppi told Reuters that Google has \"the production line for future chips.\"</p><p><strong>Microsoft: Secret Weapon Athena</strong></p><p>Regardless, Microsoft remains eager to try its luck in this chip dispute.</p><p>Previously, it was reported that a 300-person team secretly assembled by Microsoft began developing a custom chip called \"Athena\" in 2019.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/ff32948451fc5fb506c50218077142e3\" alt=\"\" title=\"\" tg-width=\"1080\" tg-height=\"608\"/></p><p>According to the original plan, \"Athena\" would use<a href=\"https://laohu8.com/S/TSM\">TSMC</a>Built using a 5nm process, it is expected to reduce the cost of each chip by one-third.</p><p>If it can be implemented on a large scale next year, Microsoft and OpenAI teams will be able to use \"Athena\" to simultaneously complete model training and inference.</p><p>In this way, the problem of shortage of dedicated computers can be greatly alleviated.</p><p>Bloomberg reported last week that Microsoft's chip division had partnered with AMD to develop the Athena chip, which also caused AMD's stock price to rise 6.5% on Thursday.</p><p>However, an insider said that AMD is not involved, but is developing its own GPUs to compete with Nvidia, and that AMD has been discussing chip design with Microsoft, which is expected to purchase the GPU.</p><p><strong>Amazon: Already a step ahead</strong></p><p>In the chip race against Microsoft and Google, Amazon seems to have taken a lead.</p><p>Over the past decade, Amazon has maintained a competitive advantage over Microsoft and Google in cloud computing services by offering more advanced technology and lower prices.</p><p>Over the next decade, Amazon is expected to continue to maintain its competitive advantage through its internally developed server chip, Graviton.</p><p>As the latest generation of processors, AWS Graviton3 offers up to 25% improvement in computing performance over its predecessor, and up to 2x improvement in floating-point performance. It also supports DDR5 memory, which increases bandwidth by 50% compared to DDR4 memory.</p><p>For machine learning workloads, AWS Graviton3 delivers up to three times the performance of its predecessor and supports bfloat16.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/0d19af72c9e6355e76551f19aa45cb48\" alt=\"\" title=\"\" tg-width=\"1024\" tg-height=\"471\"/></p><p>Cloud services based on Graviton 3 chips are very popular in some regions, even reaching a point where supply falls short of demand.</p><p>Another advantage of Amazon is that it is currently the only cloud provider that offers standard computing chips (Graviton) and AI-specific chips (Inferentia and Trainium) on its servers.</p><p>Back in 2019, Amazon launched its own AI inference chip, Inferentia.</p><p>It enables customers to run large-scale machine learning inference applications such as image recognition, speech recognition, natural language processing, personalization, and fraud detection in the cloud at low cost.</p><p>The latest Inferentia 2 boasts a threefold increase in computing performance, a fourfold increase in total accelerator memory, a fourfold increase in throughput, and a latency reduction of 1/10.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/10067987011c905d7adb326f7e2f0f2c\" alt=\"\" title=\"\" tg-width=\"1080\" tg-height=\"614\"/></p><p>Following the launch of the original Inferentia, Amazon released Trainium, a custom chip it designed primarily for AI training.</p><p>It optimizes deep learning training workloads, including image classification, semantic search, translation, speech recognition, natural language processing, and recommendation engines.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/f3ba57317936b14e104f278577aac690\" alt=\"\" title=\"\" tg-width=\"865\" tg-height=\"382\"/></p><p>In some cases, chip customization can not only reduce costs by an order of magnitude and energy consumption by 1/10, but these customized solutions can also provide customers with better service with lower latency.</p><p><strong>Shaking Nvidia's monopoly won't be easy.</strong></p><p>However, so far, most AI workloads still run on GPUs, and Nvidia produces most of the chips.</p><p>As previously reported, NVIDIA has an 80% market share in discrete GPUs and a 90% market share in high-end GPUs.</p><p>In 20 years, 80.6% of the world's cloud computing and data centers running on AI were powered by NVIDIA GPUs. In 2021, Nvidia stated that approximately 70% of the world's top 500 supercomputers were powered by its own chips.</p><p>Now, even the Microsoft data center that runs ChatGPT uses tens of thousands of NVIDIA A100 GPUs.</p><p>For a long time, whether it's the top-rated ChatGPT or models like Bard and Stable Diffusion, the computing power behind them has always been provided by NVIDIA A100 chips, each worth approximately $10,000.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/c33f78dfa5cc8233ff32625a5924fe86\" alt=\"\" title=\"\" tg-width=\"740\" tg-height=\"416\"/></p><p>Moreover, the A100 has now become a \"mainstay\" for artificial intelligence professionals. The 2022 State of Artificial Intelligence Report also lists some companies using the A100 supercomputer.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/9d220e98d16ada92a6fd5d48e05fb5cf\" alt=\"\" title=\"\" tg-width=\"920\" tg-height=\"582\"/></p><p>It is obvious that Nvidia has monopolized global computing power, dominating the market with its own chips.</p><p>According to industry insiders, compared to general-purpose chips, application-specific integrated circuit (ASIC) chips, which Amazon, Google, and Microsoft have been developing, perform machine learning tasks faster and consume less power.</p><p>When comparing GPUs and ASICs, Director O'Donnell used the following comparison: \"For normal driving, you can use a Prius, but if you have to use four-wheel drive in the mountains, a Jeep Wrangler would be more suitable.\"</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/90829da8a6f5a2d8163bf3716f607e95\" alt=\"\" title=\"\" tg-width=\"1080\" tg-height=\"608\"/></p><p>However, despite all their efforts, Amazon, Google, and Microsoft all face challenges—how to persuade developers to use these AI chips?</p><p>Currently, Nvidia's GPUs dominate, and developers are already familiar with its proprietary programming language CUDA, which is used to create GPU-driven applications.</p><p>If they switched to custom chips from Amazon, Google, or Microsoft, they would have to learn a completely new software language. Would they be willing?</p><p></body></html></p>\n<div class=\"bt-text\">\n\n\n<p> source：<a href=\"https://mp.weixin.qq.com/s/mDDh98MDwqq31IGc3xpj5A\">新智元</a></p>\n\n\n</div>\n</article>\n</div>\n</body>\n</html>\n","type":0,"thumbnail":"https://static.tigerbbs.com/3009f8f41e8235e7bbbf9b09a66a4539","relate_stocks":{"BK4553":"喜马拉雅资本持仓","LU0957791311.USD":"THREADNEEDLE (LUX) GLOBAL FOCUS \"ZU\" (USD) ACC","IE00B7KXQ091.USD":"Janus Henderson Balanced A Inc USD","LU0080751232.USD":"富达环球多元动力基金A","BK4585":"ETF&股票定投概念","LU1242518857.USD":"FULLERTON LUX FUNDS - ASIA ABSOLUTE ALPHA \"I\" (USD) ACC","BK4567":"ESG概念","MSFT":"微软","LU1852331112.SGD":"Blackrock World Technology Fund A2 SGD-H","BK4573":"虚拟现实","BK4533":"AQR资本管理(全球第二大对冲基金)","IE00B775SV38.USD":"NEUBERGER BERMAN US MULTICAP OPPORTUNITIES \"A\" (USD) ACC","IE00BSNM7G36.USD":"NEUBERGER BERMAN SYSTEMATIC GLOBAL SUSTAINABLE VALUE \"A\" (USD) ACC","BK4587":"ChatGPT概念","LU0786609619.USD":"高盛全球千禧一代股票组合Acc","LU1951198990.SGD":"Natixis Thematics 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ACC","BK4527":"明星科技股"},"source_url":"https://mp.weixin.qq.com/s/mDDh98MDwqq31IGc3xpj5A","is_english":false,"share_image_url":"https://static.laohu8.com/e9f99090a1c2ed51c021029395664489","article_id":"2334722223","content_text":"ChatGPT引爆了芯片界「百家争鸣」，谷歌、微软、亚马逊纷纷入局芯片大战，英伟达恐怕不再一家独大。ChatGPT爆火之后，谷歌和微软两巨头的AI大战战火，已经烧到了新的领域——服务器芯片。如今，AI和云计算都成了必争之地，而芯片，也成为降低成本、赢得商业客户的关键。原本，亚马逊、微软、谷歌这类大厂，都是以软件而闻名的，而现在，它们纷纷斥资数十亿美元，用于芯片开发和生产。各大科技巨头研发的AI芯片ChatGPT爆火，大厂开启芯片争霸赛根据外媒The Information的报道以及其他来源，这三家大厂现在已经推出或计划发布8款服务器和AI芯片，用于内部产品开发、云服务器租赁或者二者兼有。“如果你能制造出针对AI进行优化的硅，那前方等待你的将是巨大的胜利”，研究公司Forrester的董事Glenn O’Donnell这样说。付出这些巨大的努力，一定会得到回报吗？答案是，并不一定。英特尔、AMD和英伟达可以从规模经济中获益，但对大型科技公司来说，情况远非如此。它们还面临着许多棘手的挑战，比如需要聘请芯片设计师，还要说服开发者使用他们定制的芯片构建应用程序。不过，大厂们已经在这一领域取得了令人瞩目的进步。根据公布的性能数据，亚马逊的Graviton服务器芯片，以及亚马逊和谷歌发布的AI专用芯片，在性能上已经可以和传统的芯片厂商相媲美。亚马逊、微软和谷歌为其数据中心开发的芯片，主要有这两种：标准计算芯片和用于训练和运行机器学习模型的专用芯片。正是后者，为ChatGPT之类的大语言模型提供了动力。此前，苹果成功地为iPhone，iPad和Mac开发了芯片，改善了一些AI任务的处理。这些大厂，或许正是跟苹果学来的灵感。在三家大厂中，亚马逊是唯一一家在服务器中提供两种芯片的云服务商，2015年收购的以色列芯片设计商Annapurna Labs，为这些工作奠定了基础。谷歌在2015年推出了一款用于AI工作负载的芯片，并正在开发一款标准服务器芯片，以提高谷歌云的服务器性能。相比之下，微软的芯片研发开始得较晚，是在2019年启动的，而最近，微软更加快了推出专为LLM设计的AI芯片的时间轴。而ChatGPT的爆火，点燃了全世界用户对于AI的兴奋。这更促进了三家大厂的战略转型。ChatGPT运行在微软的Azure云上，使用了上万块英伟达A100。无论是ChatGPT，还是其他整合进Bing和各种程序的OpenAI软件，都需要如此多的算力，以至于微软已经为开发AI的内部团队分配了服务器硬件。在亚马逊，首席财务官Brian Olsavsky在上周的财报电话会议上告诉投资者，亚马逊计划将支出从零售业务转移到AWS，部分原因是投资于支持ChatGPT所需的基础设施。在谷歌，负责制造张量处理单元的工程团队已经转移到谷歌云。据悉，云组织现在可以为TPU和在其上运行的软件制定路线图，希望让云客户租用更多TPU驱动的服务器。谷歌：为AI特调的TPU V4早在2020年，谷歌就在自家的数据中心上部署了当时最强的AI芯片——TPU v4。不过直到今年的4月4日，谷歌才首次公布了这台AI超算的技术细节。相比于TPU v3，TPU v4的性能要高出2.1倍，而在整合4096个芯片之后，超算的性能更是提升了10倍。同时，谷歌还声称，自家芯片要比英伟达A100更快、更节能。对于规模相当的系统，TPU v4可以提供比英伟达A100强1.7倍的性能，同时在能效上也能提高1.9倍。对于相似规模的系统，TPU v4在BERT上比A100快1.15倍，比IPU快大约4.3倍。对于ResNet，TPU v4分别快1.67倍和大约4.5倍。另外，谷歌曾暗示，它正在研发一款与Nvidia H100竞争的新TPU。谷歌研究员Jouppi在接受路透社采访时表示，谷歌拥有“未来芯片的生产线”。微软：秘密武器雅典娜不管怎么说，微软在这场芯片纷争中，依旧跃跃欲试。此前有消息爆出，微软秘密组建的300人团队，在2019年时就开始研发一款名为“雅典娜”（Athena）的定制芯片。根据最初的计划，“雅典娜”会使用台积电的5nm工艺打造，预计可以将每颗芯片的成本降低1/3。如果在明年能够大面积实装，微软内部和OpenAI的团队便可以借助“雅典娜”同时完成模型的训练和推理。这样一来，就可以极大地缓解专用计算机紧缺的问题。彭博社在上周的报道中，称微软的芯片部门已与AMD合作开发雅典娜芯片，这也导致AMD的股价在周四上涨了6.5%。但一位知情者表示，AMD并未参与其中，而是在开发自己的GPU，与英伟达竞争，并且AMD一直在与微软讨论芯片的设计，因为微软预计要购买这款GPU。亚马逊：已抢跑一个身位而在与微软和谷歌的芯片竞赛中，亚马逊似乎已经领先了一个身位。在过去的十年中，亚马逊在云计算服务方面，通过提供更加先进的技术和更低的价格，一直保持了对微软和谷歌的竞争优势。而未来十年内，亚马逊也有望通过自己内部开发的服务器芯片——Graviton，继续在竞争中保持优势。作为最新一代的处理器，AWS Graviton3在计算性能上比上一代提高多达25%，浮点性能提高多达2倍。并支持DDR5内存，相比DDR4内存带宽增加了50%。针对机器学习工作负载，AWS Graviton3比上一代的性能高出多达3倍，并支持 bfloat16。基于Graviton 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Diffusion等模型，背后都是由每个大约价值1万美元的芯片英伟达A100提供算力。不仅如此，A100目前已成为人工智能专业人士的“主力”。2022人工智能现状报告还列出了使用A100超级计算机部分公司的名单。显而易见，英伟达已经垄断了全球算力，凭借自家的芯片，一统江湖。根据从业者的说法，相比于通用芯片，亚马逊、谷歌和微软一直在研发的专用集成电路（ASIC）芯片，在执行机器学习任务的速度更快，功耗更低。O’Donnell董事在比较GPU和ASIC时，用了这样一个比较：“平时开车，你可以用普锐斯，但如果你必须在山上用四轮驱动，用吉普牧马人就会更合适。”然而尽管已经做出了种种努力，但亚马逊、谷歌和微软都面临着挑战——如何说服开发者使用这些AI芯片呢？现在，英伟达的GPU是占主导地位的，开发者早已熟悉其专有的编程语言CUDA，用于制作GPU驱动的应用程序。如果换到亚马逊、谷歌或微软的定制芯片，就需要学习全新的软件语言了，他们会愿意吗？","news_type":1,"symbols_score_info":{"MSFT":1,"GOOGL":1,"AMZN":1,"NVDA":1}},"isVote":1,"tweetType":1,"viewCount":3121,"authorTweetTopStatus":1,"verified":2,"comments":[],"imageCount":0,"langContent":"EN","totalScore":0},{"id":925319167,"gmtCreate":1587769765459,"gmtModify":1705305286329,"author":{"id":"3433023691829222","authorId":"3433023691829222","name":"美股判官","avatar":"https://static.tigerbbs.com/42554bc1fcf5ab0f4dec98e8a1d32c23","crmLevel":2,"crmLevelSwitch":0,"followedFlag":false,"authorIdStr":"3433023691829222","idStr":"3433023691829222"},"themes":[],"title":"","htmlText":"<a 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OK?","images":[{"img":"https://static.tigerbbs.com/3cf2804174f5957d08bb9165f5633c7a","width":"750","height":"780"}],"top":1,"highlighted":1,"essential":1,"paper":1,"likeSize":1,"commentSize":3,"repostSize":1,"link":"https://ttm.financial/post/925319167","isVote":1,"tweetType":1,"viewCount":6410,"authorTweetTopStatus":1,"verified":2,"comments":[{"author":{"id":"3433023691829222","authorId":"3433023691829222","name":"美股判官","avatar":"https://static.tigerbbs.com/42554bc1fcf5ab0f4dec98e8a1d32c23","crmLevel":2,"crmLevelSwitch":0,"authorIdStr":"3433023691829222","idStr":"3433023691829222"},"content":"$Tilray Inc. (TLRY) $& nbsp; $Boeing (BA) $& nbsp; $Apple (AAPL) $& nbsp; $Tesla(TSLA)$ & nbsp; $AMZN) $& nbsp;I haven't completed the construction of the warehouse [money fan] & nbsp;","text":"$Tilray Inc. (TLRY) $& nbsp; $Boeing (BA) $& nbsp; $Apple (AAPL) $& nbsp; $Tesla(TSLA)$ & nbsp; $AMZN) $& nbsp;I haven't completed the construction of the warehouse [money fan] & nbsp;","html":"$Tilray Inc. 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(TLRY) $& nbsp; $Boeing (BA) $& nbsp; $Apple (AAPL) $& nbsp; $Tesla(TSLA)$ & nbsp; $AMZN) $& nbsp;I haven't completed the construction of the warehouse [money fan] & nbsp;","text":"$Tilray Inc. (TLRY) $& nbsp; $Boeing (BA) $& nbsp; $Apple (AAPL) $& nbsp; $Tesla(TSLA)$ & nbsp; $AMZN) $& nbsp;I haven't completed the construction of the warehouse [money fan] & nbsp;","html":"$Tilray Inc. (TLRY) $& nbsp; $Boeing (BA) $& nbsp; $Apple (AAPL) $& nbsp; $Tesla(TSLA)$ & nbsp; $AMZN) $& nbsp;I haven't completed the construction of the warehouse [money fan] & nbsp;"}],"imageCount":1,"langContent":"EN","totalScore":0},{"id":656682686,"gmtCreate":1683672533847,"gmtModify":1683682205514,"author":{"id":"3433023691829222","authorId":"3433023691829222","name":"美股判官","avatar":"https://static.tigerbbs.com/42554bc1fcf5ab0f4dec98e8a1d32c23","crmLevel":2,"crmLevelSwitch":0,"followedFlag":false,"idStr":"3433023691829222","authorIdStr":"3433023691829222"},"themes":[],"title":"","htmlText":"<a href=\"https://laohu8.com/U/74125836878304\">@空军大队长 &nbsp;</a>","listText":"<a href=\"https://laohu8.com/U/74125836878304\">@空军大队长 &nbsp;</a>","text":"@空军大队长","images":[],"top":1,"highlighted":1,"essential":1,"paper":1,"likeSize":0,"commentSize":0,"repostSize":0,"link":"https://ttm.financial/post/656682686","repostId":"2334722223","repostType":2,"repost":{"id":"2334722223","kind":"news","pubTimestamp":1683641719,"share":"https://ttm.financial/m/news/2334722223?lang=en_US&edition=fundamental","pubTime":"2023-05-09 22:15","market":"us","language":"zh","title":"Is the era of Nvidia's dominance over? ChatGPT ignites a chip war between Google and Microsoft, with Amazon also entering the fray.","url":"https://stock-news.laohu8.com/highlight/detail?id=2334722223","media":"新智元","summary":"ChatGPT引爆了芯片界「百家争鸣」，谷歌、微软、亚马逊纷纷入局芯片大战，英伟达恐怕不再一家独大。ChatGPT爆火之后，谷歌和微软两巨头的AI大战战火，已经烧到了新的领域——服务器芯片。如今，AI","content":"<p><html><head></head><body>ChatGPT has ignited a \"battle of wits\" in the chip industry, with Google, Microsoft, and Amazon all entering the chip war, and Nvidia may no longer have a monopoly. After ChatGPT became a hit,<a href=\"https://laohu8.com/S/GOOG\">Google</a>and<a href=\"https://laohu8.com/S/MSFT\">Microsoft</a>The AI battle between the two giants has spread to a new field—server chips.</p><p>Today, AI and cloud computing have become fiercely contested areas, and chips have become key to reducing costs and winning over commercial customers.</p><p>Originally,<a href=\"https://laohu8.com/S/AMZN\">Amazon</a>Major companies like Microsoft and Google are known for their software, and now they are investing billions of dollars in chip development and production.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/d7e49f3bef3efc25793e385cfb981184\" alt=\"各大科技巨头研发的AI芯片\" title=\"各大科技巨头研发的AI芯片\" tg-width=\"1080\" tg-height=\"607\"/><span>AI chips developed by major tech giants</span></p><p><strong>ChatGPT becomes a sensation, and major manufacturers launch a chip competition.</strong></p><p>According to reports from foreign media outlet The Information and other sources, these three major manufacturers have now launched or plan to launch eight servers and AI chips for internal product development, cloud server rental, or both.</p><p>“If you can create silicon optimized for AI, then a huge victory awaits you,” said Glenn O’Donnell, a director at research firm Forrester.</p><p>Will these tremendous efforts definitely be rewarded?</p><p>The answer is, not necessarily.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/92c58bb9a500fa1734275375f5acd50f\" alt=\"\" title=\"\" tg-width=\"500\" tg-height=\"224\"/></p><p><a href=\"https://laohu8.com/S/INTC\">Intel</a>, AMD and<a href=\"https://laohu8.com/S/NVDA\">NVIDIA</a>They can benefit from economies of scale, but this is far from the case for large technology companies.</p><p>They also face many tricky challenges, such as hiring chip designers and convincing developers to build applications using their custom chips.</p><p>However, major companies have already made remarkable progress in this area.</p><p>According to published performance data, Amazon's Graviton server chips, as well as AI-specific chips released by Amazon and Google, are comparable in performance to traditional chip manufacturers.</p><p>The chips developed by Amazon, Microsoft, and Google for their data centers mainly fall into two categories: standard computing chips and dedicated chips used to train and run machine learning models. It is the latter that powers large language models like ChatGPT.</p><p>Prior to this,<a href=\"https://laohu8.com/S/AAPL\">Apple</a>Chips have been successfully developed for iPhones, iPads, and Macs, improving the handling of some AI tasks. These large manufacturers may have learned inspiration from Apple.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/18da5c3fe29b36b77e2a0c06f1a66858\" alt=\"\" title=\"\" tg-width=\"728\" tg-height=\"382\"/></p><p>Of the three major companies, Amazon is the only cloud service provider that offers two types of chips in its servers. Its acquisition of Israeli chip designer Annapurna Labs in 2015 laid the foundation for this work.</p><p>Google launched a chip for AI workloads in 2015 and is developing a standard server chip to improve server performance in Google Cloud.</p><p>In contrast, Microsoft's chip development started later, in 2019, and recently, Microsoft has accelerated the timeline for launching AI chips specifically designed for LLMs.</p><p>The explosive popularity of ChatGPT has ignited excitement about AI among users worldwide. This further promoted the strategic transformation of the three major manufacturers.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/ca3cd473a03b6e5160c3681d1b2eff89\" alt=\"\" title=\"\" tg-width=\"800\" tg-height=\"534\"/></p><p>ChatGPT runs on Microsoft's Azure cloud and uses tens of thousands of NVIDIA A100 chips. Both ChatGPT and other OpenAI software integrated into Bing and various programs require so much computing power that Microsoft has already allocated server hardware to its internal team developing AI.</p><p>At Amazon, Chief Financial Officer Brian Olsavsky told investors on last week's earnings call that Amazon plans to shift spending from its retail business to AWS, partly due to investments in the infrastructure needed to support ChatGPT.</p><p>At Google, the engineering team responsible for manufacturing tensor processing units has moved to Google Cloud. It is understood that cloud organizations can now develop a roadmap for TPUs and the software running on them, hoping to get cloud customers to rent more TPU-powered servers.</p><p><strong>Google: TPU V4 specially tuned for AI</strong></p><p>Back in 2020, Google deployed the most powerful AI chip at the time—TPU v4—in its own data centers.</p><p>However, it wasn't until April 4th of this year that Google first released the technical details of this AI supercomputer.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/0690ee25b0f2aaecc65aeb54a8db428b\" alt=\"\" title=\"\" tg-width=\"1080\" tg-height=\"593\"/></p><p>Compared to the TPU v3, the TPU v4 has 2.1 times higher performance, and after integrating 4,096 chips, the supercomputer's performance is improved by 10 times.</p><p>Meanwhile, Google also claims that its chips are faster and more energy-efficient than the Nvidia A100. For a system of similar size, the TPU v4 can provide 1.7 times better performance than the NVIDIA A100, while also improving energy efficiency by 1.9 times.</p><p>For a similarly sized system, the TPU v4 is 1.15 times faster than the A100 on BERT and approximately 4.3 times faster than the IPU. For ResNet, TPU v4 is 1.67 times faster and about 4.5 times faster, respectively.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/101223a8a12806cfc2fa2a826f048f98\" alt=\"\" title=\"\" tg-width=\"780\" tg-height=\"452\"/></p><p>In addition, Google has hinted that it is developing a new TPU to compete with the Nvidia H100. Google researcher Jouppi told Reuters that Google has \"the production line for future chips.\"</p><p><strong>Microsoft: Secret Weapon Athena</strong></p><p>Regardless, Microsoft remains eager to try its luck in this chip dispute.</p><p>Previously, it was reported that a 300-person team secretly assembled by Microsoft began developing a custom chip called \"Athena\" in 2019.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/ff32948451fc5fb506c50218077142e3\" alt=\"\" title=\"\" tg-width=\"1080\" tg-height=\"608\"/></p><p>According to the original plan, \"Athena\" would use<a href=\"https://laohu8.com/S/TSM\">TSMC</a>Built using a 5nm process, it is expected to reduce the cost of each chip by one-third.</p><p>If it can be implemented on a large scale next year, Microsoft and OpenAI teams will be able to use \"Athena\" to simultaneously complete model training and inference.</p><p>In this way, the problem of shortage of dedicated computers can be greatly alleviated.</p><p>Bloomberg reported last week that Microsoft's chip division had partnered with AMD to develop the Athena chip, which also caused AMD's stock price to rise 6.5% on Thursday.</p><p>However, an insider said that AMD is not involved, but is developing its own GPUs to compete with Nvidia, and that AMD has been discussing chip design with Microsoft, which is expected to purchase the GPU.</p><p><strong>Amazon: Already a step ahead</strong></p><p>In the chip race against Microsoft and Google, Amazon seems to have taken a lead.</p><p>Over the past decade, Amazon has maintained a competitive advantage over Microsoft and Google in cloud computing services by offering more advanced technology and lower prices.</p><p>Over the next decade, Amazon is expected to continue to maintain its competitive advantage through its internally developed server chip, Graviton.</p><p>As the latest generation of processors, AWS Graviton3 offers up to 25% improvement in computing performance over its predecessor, and up to 2x improvement in floating-point performance. It also supports DDR5 memory, which increases bandwidth by 50% compared to DDR4 memory.</p><p>For machine learning workloads, AWS Graviton3 delivers up to three times the performance of its predecessor and supports bfloat16.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/0d19af72c9e6355e76551f19aa45cb48\" alt=\"\" title=\"\" tg-width=\"1024\" tg-height=\"471\"/></p><p>Cloud services based on Graviton 3 chips are very popular in some regions, even reaching a point where supply falls short of demand.</p><p>Another advantage of Amazon is that it is currently the only cloud provider that offers standard computing chips (Graviton) and AI-specific chips (Inferentia and Trainium) on its servers.</p><p>Back in 2019, Amazon launched its own AI inference chip, Inferentia.</p><p>It enables customers to run large-scale machine learning inference applications such as image recognition, speech recognition, natural language processing, personalization, and fraud detection in the cloud at low cost.</p><p>The latest Inferentia 2 boasts a threefold increase in computing performance, a fourfold increase in total accelerator memory, a fourfold increase in throughput, and a latency reduction of 1/10.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/10067987011c905d7adb326f7e2f0f2c\" alt=\"\" title=\"\" tg-width=\"1080\" tg-height=\"614\"/></p><p>Following the launch of the original Inferentia, Amazon released Trainium, a custom chip it designed primarily for AI training.</p><p>It optimizes deep learning training workloads, including image classification, semantic search, translation, speech recognition, natural language processing, and recommendation engines.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/f3ba57317936b14e104f278577aac690\" alt=\"\" title=\"\" tg-width=\"865\" tg-height=\"382\"/></p><p>In some cases, chip customization can not only reduce costs by an order of magnitude and energy consumption by 1/10, but these customized solutions can also provide customers with better service with lower latency.</p><p><strong>Shaking Nvidia's monopoly won't be easy.</strong></p><p>However, so far, most AI workloads still run on GPUs, and Nvidia produces most of the chips.</p><p>As previously reported, NVIDIA has an 80% market share in discrete GPUs and a 90% market share in high-end GPUs.</p><p>In 20 years, 80.6% of the world's cloud computing and data centers running on AI were powered by NVIDIA GPUs. In 2021, Nvidia stated that approximately 70% of the world's top 500 supercomputers were powered by its own chips.</p><p>Now, even the Microsoft data center that runs ChatGPT uses tens of thousands of NVIDIA A100 GPUs.</p><p>For a long time, whether it's the top-rated ChatGPT or models like Bard and Stable Diffusion, the computing power behind them has always been provided by NVIDIA A100 chips, each worth approximately $10,000.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/c33f78dfa5cc8233ff32625a5924fe86\" alt=\"\" title=\"\" tg-width=\"740\" tg-height=\"416\"/></p><p>Moreover, the A100 has now become a \"mainstay\" for artificial intelligence professionals. The 2022 State of Artificial Intelligence Report also lists some companies using the A100 supercomputer.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/9d220e98d16ada92a6fd5d48e05fb5cf\" alt=\"\" title=\"\" tg-width=\"920\" tg-height=\"582\"/></p><p>It is obvious that Nvidia has monopolized global computing power, dominating the market with its own chips.</p><p>According to industry insiders, compared to general-purpose chips, application-specific integrated circuit (ASIC) chips, which Amazon, Google, and Microsoft have been developing, perform machine learning tasks faster and consume less power.</p><p>When comparing GPUs and ASICs, Director O'Donnell used the following comparison: \"For normal driving, you can use a Prius, but if you have to use four-wheel drive in the mountains, a Jeep Wrangler would be more suitable.\"</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/90829da8a6f5a2d8163bf3716f607e95\" alt=\"\" title=\"\" tg-width=\"1080\" tg-height=\"608\"/></p><p>However, despite all their efforts, Amazon, Google, and Microsoft all face challenges—how to persuade developers to use these AI chips?</p><p>Currently, Nvidia's GPUs dominate, and developers are already familiar with its proprietary programming language CUDA, which is used to create GPU-driven applications.</p><p>If they switched to custom chips from Amazon, Google, or Microsoft, they would have to learn a completely new software language. Would they be willing?</p><p></body></html></p>","source":"lsy1569730104218","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>Is the era of Nvidia's dominance over? ChatGPT ignites a chip war between Google and Microsoft, with Amazon also entering the fray.</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\">\nIs the era of Nvidia's dominance over? ChatGPT ignites a chip war between Google and Microsoft, with Amazon also entering the fray.\n</h2>\n<h4 class=\"meta\">\n<p class=\"head\">\n<strong class=\"h-name small\">新智元</strong><span class=\"h-time small\">2023-05-09 22:15</span>\n</p>\n</h4>\n</header>\n<article>\n<p><html><head></head><body>ChatGPT has ignited a \"battle of wits\" in the chip industry, with Google, Microsoft, and Amazon all entering the chip war, and Nvidia may no longer have a monopoly. After ChatGPT became a hit,<a href=\"https://laohu8.com/S/GOOG\">Google</a>and<a href=\"https://laohu8.com/S/MSFT\">Microsoft</a>The AI battle between the two giants has spread to a new field—server chips.</p><p>Today, AI and cloud computing have become fiercely contested areas, and chips have become key to reducing costs and winning over commercial customers.</p><p>Originally,<a href=\"https://laohu8.com/S/AMZN\">Amazon</a>Major companies like Microsoft and Google are known for their software, and now they are investing billions of dollars in chip development and production.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/d7e49f3bef3efc25793e385cfb981184\" alt=\"各大科技巨头研发的AI芯片\" title=\"各大科技巨头研发的AI芯片\" tg-width=\"1080\" tg-height=\"607\"/><span>AI chips developed by major tech giants</span></p><p><strong>ChatGPT becomes a sensation, and major manufacturers launch a chip competition.</strong></p><p>According to reports from foreign media outlet The Information and other sources, these three major manufacturers have now launched or plan to launch eight servers and AI chips for internal product development, cloud server rental, or both.</p><p>“If you can create silicon optimized for AI, then a huge victory awaits you,” said Glenn O’Donnell, a director at research firm Forrester.</p><p>Will these tremendous efforts definitely be rewarded?</p><p>The answer is, not necessarily.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/92c58bb9a500fa1734275375f5acd50f\" alt=\"\" title=\"\" tg-width=\"500\" tg-height=\"224\"/></p><p><a href=\"https://laohu8.com/S/INTC\">Intel</a>, AMD and<a href=\"https://laohu8.com/S/NVDA\">NVIDIA</a>They can benefit from economies of scale, but this is far from the case for large technology companies.</p><p>They also face many tricky challenges, such as hiring chip designers and convincing developers to build applications using their custom chips.</p><p>However, major companies have already made remarkable progress in this area.</p><p>According to published performance data, Amazon's Graviton server chips, as well as AI-specific chips released by Amazon and Google, are comparable in performance to traditional chip manufacturers.</p><p>The chips developed by Amazon, Microsoft, and Google for their data centers mainly fall into two categories: standard computing chips and dedicated chips used to train and run machine learning models. It is the latter that powers large language models like ChatGPT.</p><p>Prior to this,<a href=\"https://laohu8.com/S/AAPL\">Apple</a>Chips have been successfully developed for iPhones, iPads, and Macs, improving the handling of some AI tasks. These large manufacturers may have learned inspiration from Apple.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/18da5c3fe29b36b77e2a0c06f1a66858\" alt=\"\" title=\"\" tg-width=\"728\" tg-height=\"382\"/></p><p>Of the three major companies, Amazon is the only cloud service provider that offers two types of chips in its servers. Its acquisition of Israeli chip designer Annapurna Labs in 2015 laid the foundation for this work.</p><p>Google launched a chip for AI workloads in 2015 and is developing a standard server chip to improve server performance in Google Cloud.</p><p>In contrast, Microsoft's chip development started later, in 2019, and recently, Microsoft has accelerated the timeline for launching AI chips specifically designed for LLMs.</p><p>The explosive popularity of ChatGPT has ignited excitement about AI among users worldwide. This further promoted the strategic transformation of the three major manufacturers.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/ca3cd473a03b6e5160c3681d1b2eff89\" alt=\"\" title=\"\" tg-width=\"800\" tg-height=\"534\"/></p><p>ChatGPT runs on Microsoft's Azure cloud and uses tens of thousands of NVIDIA A100 chips. Both ChatGPT and other OpenAI software integrated into Bing and various programs require so much computing power that Microsoft has already allocated server hardware to its internal team developing AI.</p><p>At Amazon, Chief Financial Officer Brian Olsavsky told investors on last week's earnings call that Amazon plans to shift spending from its retail business to AWS, partly due to investments in the infrastructure needed to support ChatGPT.</p><p>At Google, the engineering team responsible for manufacturing tensor processing units has moved to Google Cloud. It is understood that cloud organizations can now develop a roadmap for TPUs and the software running on them, hoping to get cloud customers to rent more TPU-powered servers.</p><p><strong>Google: TPU V4 specially tuned for AI</strong></p><p>Back in 2020, Google deployed the most powerful AI chip at the time—TPU v4—in its own data centers.</p><p>However, it wasn't until April 4th of this year that Google first released the technical details of this AI supercomputer.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/0690ee25b0f2aaecc65aeb54a8db428b\" alt=\"\" title=\"\" tg-width=\"1080\" tg-height=\"593\"/></p><p>Compared to the TPU v3, the TPU v4 has 2.1 times higher performance, and after integrating 4,096 chips, the supercomputer's performance is improved by 10 times.</p><p>Meanwhile, Google also claims that its chips are faster and more energy-efficient than the Nvidia A100. For a system of similar size, the TPU v4 can provide 1.7 times better performance than the NVIDIA A100, while also improving energy efficiency by 1.9 times.</p><p>For a similarly sized system, the TPU v4 is 1.15 times faster than the A100 on BERT and approximately 4.3 times faster than the IPU. For ResNet, TPU v4 is 1.67 times faster and about 4.5 times faster, respectively.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/101223a8a12806cfc2fa2a826f048f98\" alt=\"\" title=\"\" tg-width=\"780\" tg-height=\"452\"/></p><p>In addition, Google has hinted that it is developing a new TPU to compete with the Nvidia H100. Google researcher Jouppi told Reuters that Google has \"the production line for future chips.\"</p><p><strong>Microsoft: Secret Weapon Athena</strong></p><p>Regardless, Microsoft remains eager to try its luck in this chip dispute.</p><p>Previously, it was reported that a 300-person team secretly assembled by Microsoft began developing a custom chip called \"Athena\" in 2019.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/ff32948451fc5fb506c50218077142e3\" alt=\"\" title=\"\" tg-width=\"1080\" tg-height=\"608\"/></p><p>According to the original plan, \"Athena\" would use<a href=\"https://laohu8.com/S/TSM\">TSMC</a>Built using a 5nm process, it is expected to reduce the cost of each chip by one-third.</p><p>If it can be implemented on a large scale next year, Microsoft and OpenAI teams will be able to use \"Athena\" to simultaneously complete model training and inference.</p><p>In this way, the problem of shortage of dedicated computers can be greatly alleviated.</p><p>Bloomberg reported last week that Microsoft's chip division had partnered with AMD to develop the Athena chip, which also caused AMD's stock price to rise 6.5% on Thursday.</p><p>However, an insider said that AMD is not involved, but is developing its own GPUs to compete with Nvidia, and that AMD has been discussing chip design with Microsoft, which is expected to purchase the GPU.</p><p><strong>Amazon: Already a step ahead</strong></p><p>In the chip race against Microsoft and Google, Amazon seems to have taken a lead.</p><p>Over the past decade, Amazon has maintained a competitive advantage over Microsoft and Google in cloud computing services by offering more advanced technology and lower prices.</p><p>Over the next decade, Amazon is expected to continue to maintain its competitive advantage through its internally developed server chip, Graviton.</p><p>As the latest generation of processors, AWS Graviton3 offers up to 25% improvement in computing performance over its predecessor, and up to 2x improvement in floating-point performance. It also supports DDR5 memory, which increases bandwidth by 50% compared to DDR4 memory.</p><p>For machine learning workloads, AWS Graviton3 delivers up to three times the performance of its predecessor and supports bfloat16.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/0d19af72c9e6355e76551f19aa45cb48\" alt=\"\" title=\"\" tg-width=\"1024\" tg-height=\"471\"/></p><p>Cloud services based on Graviton 3 chips are very popular in some regions, even reaching a point where supply falls short of demand.</p><p>Another advantage of Amazon is that it is currently the only cloud provider that offers standard computing chips (Graviton) and AI-specific chips (Inferentia and Trainium) on its servers.</p><p>Back in 2019, Amazon launched its own AI inference chip, Inferentia.</p><p>It enables customers to run large-scale machine learning inference applications such as image recognition, speech recognition, natural language processing, personalization, and fraud detection in the cloud at low cost.</p><p>The latest Inferentia 2 boasts a threefold increase in computing performance, a fourfold increase in total accelerator memory, a fourfold increase in throughput, and a latency reduction of 1/10.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/10067987011c905d7adb326f7e2f0f2c\" alt=\"\" title=\"\" tg-width=\"1080\" tg-height=\"614\"/></p><p>Following the launch of the original Inferentia, Amazon released Trainium, a custom chip it designed primarily for AI training.</p><p>It optimizes deep learning training workloads, including image classification, semantic search, translation, speech recognition, natural language processing, and recommendation engines.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/f3ba57317936b14e104f278577aac690\" alt=\"\" title=\"\" tg-width=\"865\" tg-height=\"382\"/></p><p>In some cases, chip customization can not only reduce costs by an order of magnitude and energy consumption by 1/10, but these customized solutions can also provide customers with better service with lower latency.</p><p><strong>Shaking Nvidia's monopoly won't be easy.</strong></p><p>However, so far, most AI workloads still run on GPUs, and Nvidia produces most of the chips.</p><p>As previously reported, NVIDIA has an 80% market share in discrete GPUs and a 90% market share in high-end GPUs.</p><p>In 20 years, 80.6% of the world's cloud computing and data centers running on AI were powered by NVIDIA GPUs. In 2021, Nvidia stated that approximately 70% of the world's top 500 supercomputers were powered by its own chips.</p><p>Now, even the Microsoft data center that runs ChatGPT uses tens of thousands of NVIDIA A100 GPUs.</p><p>For a long time, whether it's the top-rated ChatGPT or models like Bard and Stable Diffusion, the computing power behind them has always been provided by NVIDIA A100 chips, each worth approximately $10,000.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/c33f78dfa5cc8233ff32625a5924fe86\" alt=\"\" title=\"\" tg-width=\"740\" tg-height=\"416\"/></p><p>Moreover, the A100 has now become a \"mainstay\" for artificial intelligence professionals. The 2022 State of Artificial Intelligence Report also lists some companies using the A100 supercomputer.</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/9d220e98d16ada92a6fd5d48e05fb5cf\" alt=\"\" title=\"\" tg-width=\"920\" tg-height=\"582\"/></p><p>It is obvious that Nvidia has monopolized global computing power, dominating the market with its own chips.</p><p>According to industry insiders, compared to general-purpose chips, application-specific integrated circuit (ASIC) chips, which Amazon, Google, and Microsoft have been developing, perform machine learning tasks faster and consume less power.</p><p>When comparing GPUs and ASICs, Director O'Donnell used the following comparison: \"For normal driving, you can use a Prius, but if you have to use four-wheel drive in the mountains, a Jeep Wrangler would be more suitable.\"</p><p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/90829da8a6f5a2d8163bf3716f607e95\" alt=\"\" title=\"\" tg-width=\"1080\" tg-height=\"608\"/></p><p>However, despite all their efforts, Amazon, Google, and Microsoft all face challenges—how to persuade developers to use these AI chips?</p><p>Currently, Nvidia's GPUs dominate, and developers are already familiar with its proprietary programming language CUDA, which is used to create GPU-driven applications.</p><p>If they switched to custom chips from Amazon, Google, or Microsoft, they would have to learn a completely new software language. Would they be willing?</p><p></body></html></p>\n<div class=\"bt-text\">\n\n\n<p> source：<a href=\"https://mp.weixin.qq.com/s/mDDh98MDwqq31IGc3xpj5A\">新智元</a></p>\n\n\n</div>\n</article>\n</div>\n</body>\n</html>\n","type":0,"thumbnail":"https://static.tigerbbs.com/3009f8f41e8235e7bbbf9b09a66a4539","relate_stocks":{"BK4553":"喜马拉雅资本持仓","LU0957791311.USD":"THREADNEEDLE (LUX) GLOBAL FOCUS \"ZU\" (USD) ACC","IE00B7KXQ091.USD":"Janus Henderson Balanced A Inc USD","LU0080751232.USD":"富达环球多元动力基金A","BK4585":"ETF&股票定投概念","LU1242518857.USD":"FULLERTON LUX FUNDS - ASIA ABSOLUTE ALPHA \"I\" (USD) ACC","BK4567":"ESG概念","MSFT":"微软","LU1852331112.SGD":"Blackrock World Technology Fund A2 SGD-H","BK4573":"虚拟现实","BK4533":"AQR资本管理(全球第二大对冲基金)","IE00B775SV38.USD":"NEUBERGER BERMAN US MULTICAP OPPORTUNITIES \"A\" (USD) ACC","IE00BSNM7G36.USD":"NEUBERGER BERMAN SYSTEMATIC GLOBAL SUSTAINABLE VALUE \"A\" (USD) ACC","BK4587":"ChatGPT概念","LU0786609619.USD":"高盛全球千禧一代股票组合Acc","LU1951198990.SGD":"Natixis Thematics 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Quality Growth Fund Dis SGD","NVDA":"英伟达","BK4576":"AR","BK4543":"AI","LU1720051017.SGD":"Allianz Global Artificial Intelligence AT Acc H2-SGD","LU0198837287.USD":"UBS (LUX) EQUITY SICAV - USA GROWTH \"P\" (USD) ACC","IE00B3S45H60.SGD":"Neuberger Berman US Multicap Opportunities A Acc SGD-H","LU0109391861.USD":"富兰克林美国机遇基金A Acc","LU1839511570.USD":"WELLS FARGO GLOBAL FACTOR ENHANCED EQUITY \"I\" (USD) ACC","LU1861220033.SGD":"Blackrock Next Generation Technology A2 SGD-H","LU0417517546.SGD":"Allianz US Equity Cl AT Acc SGD","BK4097":"系统软件","LU0353189763.USD":"ALLSPRING  US ALL CAP GROWTH FUND \"I\" (USD) ACC","BK4524":"宅经济概念","BK4554":"元宇宙及AR概念","LU0889565833.HKD":"FRANKLIN TECHNOLOGY \"A\" (HKD) ACC","BK4527":"明星科技股"},"source_url":"https://mp.weixin.qq.com/s/mDDh98MDwqq31IGc3xpj5A","is_english":false,"share_image_url":"https://static.laohu8.com/e9f99090a1c2ed51c021029395664489","article_id":"2334722223","content_text":"ChatGPT引爆了芯片界「百家争鸣」，谷歌、微软、亚马逊纷纷入局芯片大战，英伟达恐怕不再一家独大。ChatGPT爆火之后，谷歌和微软两巨头的AI大战战火，已经烧到了新的领域——服务器芯片。如今，AI和云计算都成了必争之地，而芯片，也成为降低成本、赢得商业客户的关键。原本，亚马逊、微软、谷歌这类大厂，都是以软件而闻名的，而现在，它们纷纷斥资数十亿美元，用于芯片开发和生产。各大科技巨头研发的AI芯片ChatGPT爆火，大厂开启芯片争霸赛根据外媒The Information的报道以及其他来源，这三家大厂现在已经推出或计划发布8款服务器和AI芯片，用于内部产品开发、云服务器租赁或者二者兼有。“如果你能制造出针对AI进行优化的硅，那前方等待你的将是巨大的胜利”，研究公司Forrester的董事Glenn O’Donnell这样说。付出这些巨大的努力，一定会得到回报吗？答案是，并不一定。英特尔、AMD和英伟达可以从规模经济中获益，但对大型科技公司来说，情况远非如此。它们还面临着许多棘手的挑战，比如需要聘请芯片设计师，还要说服开发者使用他们定制的芯片构建应用程序。不过，大厂们已经在这一领域取得了令人瞩目的进步。根据公布的性能数据，亚马逊的Graviton服务器芯片，以及亚马逊和谷歌发布的AI专用芯片，在性能上已经可以和传统的芯片厂商相媲美。亚马逊、微软和谷歌为其数据中心开发的芯片，主要有这两种：标准计算芯片和用于训练和运行机器学习模型的专用芯片。正是后者，为ChatGPT之类的大语言模型提供了动力。此前，苹果成功地为iPhone，iPad和Mac开发了芯片，改善了一些AI任务的处理。这些大厂，或许正是跟苹果学来的灵感。在三家大厂中，亚马逊是唯一一家在服务器中提供两种芯片的云服务商，2015年收购的以色列芯片设计商Annapurna Labs，为这些工作奠定了基础。谷歌在2015年推出了一款用于AI工作负载的芯片，并正在开发一款标准服务器芯片，以提高谷歌云的服务器性能。相比之下，微软的芯片研发开始得较晚，是在2019年启动的，而最近，微软更加快了推出专为LLM设计的AI芯片的时间轴。而ChatGPT的爆火，点燃了全世界用户对于AI的兴奋。这更促进了三家大厂的战略转型。ChatGPT运行在微软的Azure云上，使用了上万块英伟达A100。无论是ChatGPT，还是其他整合进Bing和各种程序的OpenAI软件，都需要如此多的算力，以至于微软已经为开发AI的内部团队分配了服务器硬件。在亚马逊，首席财务官Brian Olsavsky在上周的财报电话会议上告诉投资者，亚马逊计划将支出从零售业务转移到AWS，部分原因是投资于支持ChatGPT所需的基础设施。在谷歌，负责制造张量处理单元的工程团队已经转移到谷歌云。据悉，云组织现在可以为TPU和在其上运行的软件制定路线图，希望让云客户租用更多TPU驱动的服务器。谷歌：为AI特调的TPU V4早在2020年，谷歌就在自家的数据中心上部署了当时最强的AI芯片——TPU v4。不过直到今年的4月4日，谷歌才首次公布了这台AI超算的技术细节。相比于TPU v3，TPU v4的性能要高出2.1倍，而在整合4096个芯片之后，超算的性能更是提升了10倍。同时，谷歌还声称，自家芯片要比英伟达A100更快、更节能。对于规模相当的系统，TPU v4可以提供比英伟达A100强1.7倍的性能，同时在能效上也能提高1.9倍。对于相似规模的系统，TPU v4在BERT上比A100快1.15倍，比IPU快大约4.3倍。对于ResNet，TPU v4分别快1.67倍和大约4.5倍。另外，谷歌曾暗示，它正在研发一款与Nvidia H100竞争的新TPU。谷歌研究员Jouppi在接受路透社采访时表示，谷歌拥有“未来芯片的生产线”。微软：秘密武器雅典娜不管怎么说，微软在这场芯片纷争中，依旧跃跃欲试。此前有消息爆出，微软秘密组建的300人团队，在2019年时就开始研发一款名为“雅典娜”（Athena）的定制芯片。根据最初的计划，“雅典娜”会使用台积电的5nm工艺打造，预计可以将每颗芯片的成本降低1/3。如果在明年能够大面积实装，微软内部和OpenAI的团队便可以借助“雅典娜”同时完成模型的训练和推理。这样一来，就可以极大地缓解专用计算机紧缺的问题。彭博社在上周的报道中，称微软的芯片部门已与AMD合作开发雅典娜芯片，这也导致AMD的股价在周四上涨了6.5%。但一位知情者表示，AMD并未参与其中，而是在开发自己的GPU，与英伟达竞争，并且AMD一直在与微软讨论芯片的设计，因为微软预计要购买这款GPU。亚马逊：已抢跑一个身位而在与微软和谷歌的芯片竞赛中，亚马逊似乎已经领先了一个身位。在过去的十年中，亚马逊在云计算服务方面，通过提供更加先进的技术和更低的价格，一直保持了对微软和谷歌的竞争优势。而未来十年内，亚马逊也有望通过自己内部开发的服务器芯片——Graviton，继续在竞争中保持优势。作为最新一代的处理器，AWS Graviton3在计算性能上比上一代提高多达25%，浮点性能提高多达2倍。并支持DDR5内存，相比DDR4内存带宽增加了50%。针对机器学习工作负载，AWS Graviton3比上一代的性能高出多达3倍，并支持 bfloat16。基于Graviton 3芯片的云服务在一些地区非常受欢迎，甚至于达到了供不应求的状态。亚马逊另一方面的优势还表现在，它是目前唯一一家在其服务器中提供标准计算芯片（Graviton）和AI专用芯片（Inferentia和Trainium）云供应商。早在2019年，亚马逊就推出了自己的AI推理芯片——Inferentia。它可以让客户可以在云端低成本运行大规模机器学习推理应用程序，例如图像识别、语音识别、自然语言处理、个性化和欺诈检测。而最新的Inferentia 2更是在计算性能提高了3倍，加速器总内存扩大了4倍，吞吐量提高了4倍，延迟降低到1/10。在初代Inferentia推出之后，亚马逊又发布了其设计的主要用于AI训练的定制芯片——Trainium。它对深度学习训练工作负载进行了优化，包括图像分类、语义搜索、翻译、语音识别、自然语言处理和推荐引擎等。在一些情况下，芯片定制不仅仅可以把成本降低一个数量级，能耗减少到1/10，并且这些定制化的方案可以给客户以更低的延迟提供更好的服务。撼动英伟达的垄断，没那么容易不过到目前为止，大多数的AI负载还是跑在GPU上的，而英伟达生产了其中的大部分芯片。据此前报道，英伟达独立GPU市场份额达80%，在高端GPU市场份额高达90%。20年，全世界跑AI的云计算与数据中心，80.6%都由英伟达GPU驱动。21年，英伟达表示，全球前500个超算中，大约七成是由自家的芯片驱动。而现在，就连运行ChatGPT的微软数据中心用了上万块英伟达A100 GPU。一直以来，不管是成为顶流的ChatGPT，还是Bard、Stable Diffusion等模型，背后都是由每个大约价值1万美元的芯片英伟达A100提供算力。不仅如此，A100目前已成为人工智能专业人士的“主力”。2022人工智能现状报告还列出了使用A100超级计算机部分公司的名单。显而易见，英伟达已经垄断了全球算力，凭借自家的芯片，一统江湖。根据从业者的说法，相比于通用芯片，亚马逊、谷歌和微软一直在研发的专用集成电路（ASIC）芯片，在执行机器学习任务的速度更快，功耗更低。O’Donnell董事在比较GPU和ASIC时，用了这样一个比较：“平时开车，你可以用普锐斯，但如果你必须在山上用四轮驱动，用吉普牧马人就会更合适。”然而尽管已经做出了种种努力，但亚马逊、谷歌和微软都面临着挑战——如何说服开发者使用这些AI芯片呢？现在，英伟达的GPU是占主导地位的，开发者早已熟悉其专有的编程语言CUDA，用于制作GPU驱动的应用程序。如果换到亚马逊、谷歌或微软的定制芯片，就需要学习全新的软件语言了，他们会愿意吗？","news_type":1,"symbols_score_info":{"MSFT":1,"GOOGL":1,"AMZN":1,"NVDA":1}},"isVote":1,"tweetType":1,"viewCount":3121,"authorTweetTopStatus":1,"verified":2,"comments":[],"imageCount":0,"langContent":"EN","totalScore":0},{"id":920695449,"gmtCreate":1584984521481,"gmtModify":1705296499464,"author":{"id":"3433023691829222","authorId":"3433023691829222","name":"美股判官","avatar":"https://static.tigerbbs.com/42554bc1fcf5ab0f4dec98e8a1d32c23","crmLevel":2,"crmLevelSwitch":0,"followedFlag":false,"idStr":"3433023691829222","authorIdStr":"3433023691829222"},"themes":[],"title":"","htmlText":"<a 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