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2024-10-25

英伟达

2024-07-05

美股并不像国内大多数媒体报道会出现调整，会经历一段牛市行情

2024-02-20

对英伟达是个挑[微笑]

史上最快大模型炸场！Groq一夜爆红，自研LPU速度碾压英伟达GPU

2021-01-08

$未来金融科技(FTFT)$空试试

2020-11-19

$蛋壳公寓(DNK)$多单跟进1万股，拭目以待

2020-11-06

$Aptevo Therapeutics Inc.(APVO)$22元做多，拭目以待

2020-10-01

$Palantir Technologies Inc.(PLTR)$老虎怎么了？为什么不让人买入了

2020-09-30

$Yalla Group(YALA)$怎么还不分配？晕了

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19:58","market":"hk","language":"zh","title":"史上最快大模型炸场！Groq一夜爆红，自研LPU速度碾压英伟达GPU","url":"https://stock-news.laohu8.com/highlight/detail?id=2412738401","media":"华尔街见闻","summary":"英伟达的挑战者Groq登场！抛弃GPU，自研LPU！文本生成速度比眨眼还快！推理场景速度比英伟达GPU快10倍，但价格和耗电量都仅为后者十分之一。","content":"<html><head></head><body><blockquote><p><strong>英伟达的挑战者Groq登场！抛弃GPU，自研LPU！文本生成速度比眨眼还快！推理场景速度比英伟达GPU快10倍，但价格和耗电量都仅为后者十分之一。</strong></p></blockquote><p>一觉醒来，AI圈又变天了。</p><p>还没消化完Sora带来的震撼，又一家硅谷初创企业带着史上最快的大模型和自研芯片LPU霸占了热搜。</p><p>就在昨天，AI芯片创企Groq（不是马斯克的Gork）开放了自家产品的免费试用。相比其他AI聊天<a href=\"https://laohu8.com/S/300024\">机器人</a>，Groq闪电般的响应速度迅速引爆互联网讨论。经过网友测试，Groq每秒生成速度接近500 tok/s，碾压GPT-4的40 tok/s。</p><p>有网友震惊地说：</p><blockquote><p>它回复的速度比我眨眼还快。</p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/71872eee5a431569aa69be61050106bf\" tg-width=\"1024\" tg-height=\"351\"/></p></blockquote><p>不过，需要强调的是，<strong>Groq并没有研发新模型，它只是一个模型启动器</strong>，主页上运行的是开源模型Mixtral 8x7B-32k和Llama 270B-4k。</p><p><strong>冠绝大模型圈子的响应速度，来自驱动模型的硬件——Groq并未使用<a href=\"https://laohu8.com/S/NVDA\">英伟达</a>的GPU，而是自研了新型AI芯片——LPU（Language Processing Units）。</strong></p><h2 id=\"id_882585354\">每秒500 tokens，写论文比你眨眼还快</h2><p>LPU最突出的特点就是快。</p><p>根据2024年一月的测试结果，由Groq LPU驱动Meta Llama 2模型，推理性能遥遥领先，是顶级云计算供应商的18倍。</p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/bd43fc6c6347857b51f9b4e4a7becd4e\" tg-width=\"1024\" tg-height=\"610\"/></p><p>图片来源：GIT HUB</p><p>华尔街见闻此前文章提及，<strong>Groq LPU搭配Meta Llama 2 70B能在7分钟内就能生成与莎士比亚《哈姆雷特》相同数量的单词，比普通人的打字速度快75倍。</strong></p><p>如下图所示，有推特网友问了一个和营销有关的专业问题，Groq在四秒钟之内就输出了上千词的长篇大论。</p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/6f0631c67875b8ec9434e62894c5a7ca\" tg-width=\"528\" tg-height=\"360\"/></p><p>还有网友测试同时用Gemini、GPT-4和Groq完成一个代码调试问题。</p><p><strong>结果，Groq的输出速度比Gemini快10倍，比GPT-4快18倍。</strong></p><p>Groq在速度上对其他AI模型的降维打击，让网友直呼，“AI推理界的美国队长来了”。</p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/c92587a48ccf22722da0f0bf0cf0bc20\" tg-width=\"1024\" tg-height=\"429\"/></p><h2 id=\"id_2373292048\">LPU，英伟达GPU的挑战者？</h2><p><strong>再强调一遍，Groq没有开发新的模型，它只是用了不一样的芯片。</strong></p><p>根据Groq官网的介绍，LPU是一种专为AI推理所设计的芯片。驱动包括GPT等主流大模型的GPU，是一种为图形渲染而设计的并行处理器，有数百个内核，而LPU架构则与GPU使用的SIMD（单指令，多数据）不同，这种设计可以让芯片更有效地利用每个时钟周期，确保一致的延迟和吞吐量，也降低了复杂调度硬件的需求：</p><blockquote><p>Groq的LPU推理引擎不是普通的处理单元；它是一个端到端系统，专为需要大量计算和连续处理的应用（如LLM）提供最快的推理而设计。通过消除外部内存瓶颈，LPU推理引擎的性能比传统GPU高出几个数量级。</p></blockquote><p>简单来说，对用户而言，最直观的体验就是“快”。</p><p>使用过GPT的读者一定知道，痛苦地等待大模型一个一个地吐出字符是一种怎样痛苦的体验，而LPU驱动下的大模型，基本可以做到实时响应。</p><p>比如下图，华尔街见闻向Groq询问LPU和GPU的区别，Groq生成这个回答用时不到3秒，完全不会像GPT、Gemini那样出现显著的延迟。如果以英文提问，生成速度还会更快。</p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/c2b90bfa3603ee533a675ce9bfec5ff7\" tg-width=\"1024\" tg-height=\"725\"/></p><p>Groq官方的介绍还显示，创新的芯片架构可以把多个张量流处理器（Tensor Streaming Processor，简称TSP）连接在一起，而不会出现GPU集群中的传统瓶颈，因此具有极高的可扩展性，简化了大规模AI模型的硬件要求。</p><p>能效也是LPU的另一个亮点。通过减少管理多个线程的开销和避免内核的利用率不足，LPU每瓦特可以提供更多的算力。</p><p>Groq创始人兼首席执行官Jonathan Ross在采访中，时时不忘给英伟达上眼药。</p><p>他此前对媒体表示，在大模型推理场景，<strong>Groq LPU芯片的速度比英伟达GPU快10倍，但价格和耗电量都仅为后者的十分之一。</strong></p><p>实时推理是通过经过训练的AI模型运行数据的计算过程，以提供AI应用的即时结果，从而实现流畅的最终用户体验。随着AI大模型的发展，实时推理的需求激增。</p><p>Ross认为，对于在产品中使用人工智能的公司来说，推理成本正在成为一个问题，因为随着使用这些产品的客户数量增加，运行模型的成本也在迅速增加。与英伟达GPU相比，Groq LPU集群将为大模型推理提供更高的吞吐量、更低的延迟和更低的成本。</p><p>他还强调，Groq的芯片，由于技术路径不同，在供应方面比英伟达更充足，不会被<a href=\"https://laohu8.com/S/TSM\">台积电</a>或者SK海力士等供应商卡脖子：</p><blockquote><p><strong>GroqChip LPU的独特之处在于，它不依赖于<a href=\"https://laohu8.com/S/SMSN.UK\">三星</a>或SK海力士的HBM，也不依赖于台积电将外部HBM焊接到芯片上的CoWoS封装技术。</strong></p></blockquote><p>不过，另有一些AI专家在社交媒体上表示，Groq芯片的实际成本并不低。</p><p>如人工智能专家贾扬清分析称，<strong>Groq综合成本相当于英伟达GPU的30多倍。</strong></p><p>考虑到每张Groq芯片的内存容量为230MB，实际运行模型需要572张芯片，总成本高达1144万美元。</p><p>相比之下，8张H100的系统在性能上与Groq系统相当，但硬件成本仅为30万美元，年度电费约2.4万美元。三年总运营成本对比显示，Groq系统的运营成本远高于H100系统，</p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/f1873cf74496ae96ee6ad6d760c90541\" tg-width=\"783\" tg-height=\"411\"/></p><p>而且，更关键的是，LPU目前仅用于推理，要训练大模型，仍然需要购买英伟达GPU。</p><h2 id=\"id_3983102120\">创始人为<a href=\"https://laohu8.com/S/GOOG\">谷歌</a>TPU设计者之一 相信未来2年能卖出100万个LPU</h2><p>在今天互联网上一炮而红之前，Groq已经低调埋头研发7年多的时间。</p><p>公开资料显示，Groq成立于2016年，总部位于美国加州圣塔克拉拉山景城。<strong>公司创始人Jonathan Ross是前谷歌高级工程师，是谷歌自研AI芯片TPU的设计者之一。</strong>产品主管John Barrus曾在谷歌及亚马逊担任产品高管。</p><p class=\"t-img-caption\"><img src=\"https://static.tigerbbs.com/2d0f2fecee1d89dd19bd221ba08b4a97\" tg-width=\"1024\" tg-height=\"579\"/></p><p>高管内唯一一位华裔面孔、副总裁Estelle Hong，在公司任职已有四年，此前曾供职于美国军队及<a href=\"https://laohu8.com/S/INTC\">英特尔</a>。</p><p>就在去年8月，Groq也宣布了和三星的合作计划，表示其下一代芯片将在美国德克萨斯州三星芯片工厂采用4纳米工艺生产，预计量产时间为24年下半年。</p><p>展望下一代LPU，Ross相信GroqChip的能效将提高15到20倍，可以在相同的功率范围内为设备增加更多的矩阵计算和SRAM存储器。</p><p>在去年底的采访中，Ross表示，<strong>考虑到GPU的短缺和高昂的成本，他相信Groq未来的发展潜力</strong>：</p><blockquote><p>在12个月内，我们可以部署10万个LPU，在24个月内，我们可以部署100万个LPU。</p></blockquote></body></html>","source":"wallstreetcn_hot_news","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>史上最快大模型炸场！Groq一夜爆红，自研LPU速度碾压英伟达GPU</title>\n<style 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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\">\n史上最快大模型炸场！Groq一夜爆红，自研LPU速度碾压英伟达GPU\n</h2>\n\n<h4 class=\"meta\">\n\n\n2024-02-20 19:58 北京时间&nbsp;&nbsp;&nbsp;<a href=https://wallstreetcn.com/articles/3708651><strong>华尔街见闻</strong></a>\n\n\n</h4>\n\n</header>\n<article>\n<div>\n<p>英伟达的挑战者Groq登场！抛弃GPU，自研LPU！文本生成速度比眨眼还快！推理场景速度比英伟达GPU快10倍，但价格和耗电量都仅为后者十分之一。一觉醒来，AI圈又变天了。还没消化完Sora带来的震撼，又一家硅谷初创企业带着史上最快的大模型和自研芯片LPU霸占了热搜。就在昨天，AI芯片创企Groq（不是马斯克的Gork）开放了自家产品的免费试用。相比其他AI聊天机器人，Groq闪电般的响应速度迅速...</p>\n\n<a href=\"https://wallstreetcn.com/articles/3708651\">Web Link</a>\n\n</div>\n\n\n</article>\n</div>\n</body>\n</html>\n","type":0,"thumbnail":"https://static.tigerbbs.com/35fc458972611c2f98e1cc9b3e7c3b5c","relate_stocks":{"BK4550":"红杉资本持仓","IE00B7KXQ091.USD":"Janus Henderson Balanced A Inc USD","BK4141":"半导体产品","GB00BDT5M118.USD":"天利环球扩展Alpha基金A Acc","LU0198837287.USD":"UBS (LUX) EQUITY SICAV - USA GROWTH \"P\" (USD) ACC","LU0689472784.USD":"安联收益及增长基金Cl AM AT Acc","NVDA":"英伟达","BK4551":"寇图资本持仓","BK4543":"AI","IE00BJJMRX11.SGD":"Janus Henderson Balanced A Acc SGD","IE00B19Z9505.USD":"美盛-美国大盘成长股A Acc","LU0109391861.USD":"富兰克林美国机遇基金A Acc","LU0256863811.USD":"ALLIANZ US EQUITY \"A\" INC","LU0353189763.USD":"ALLSPRING  US ALL CAP GROWTH FUND \"I\" (USD) ACC","LU0820561909.HKD":"ALLIANZ INCOME AND GROWTH \"AM\" (HKD) INC","LU0127658192.USD":"EASTSPRING INVESTMENTS GLOBAL TECHNOLOGY \"A\" (USD) ACC","BK4549":"软银资本持仓","IE00BJJMRY28.SGD":"Janus Henderson Balanced A Inc 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ACC"},"source_url":"https://wallstreetcn.com/articles/3708651","is_english":false,"share_image_url":"https://static.laohu8.com/e9f99090a1c2ed51c021029395664489","article_id":"2412738401","content_text":"英伟达的挑战者Groq登场！抛弃GPU，自研LPU！文本生成速度比眨眼还快！推理场景速度比英伟达GPU快10倍，但价格和耗电量都仅为后者十分之一。一觉醒来，AI圈又变天了。还没消化完Sora带来的震撼，又一家硅谷初创企业带着史上最快的大模型和自研芯片LPU霸占了热搜。就在昨天，AI芯片创企Groq（不是马斯克的Gork）开放了自家产品的免费试用。相比其他AI聊天机器人，Groq闪电般的响应速度迅速引爆互联网讨论。经过网友测试，Groq每秒生成速度接近500 tok/s，碾压GPT-4的40 tok/s。有网友震惊地说：它回复的速度比我眨眼还快。不过，需要强调的是，Groq并没有研发新模型，它只是一个模型启动器，主页上运行的是开源模型Mixtral 8x7B-32k和Llama 270B-4k。冠绝大模型圈子的响应速度，来自驱动模型的硬件——Groq并未使用英伟达的GPU，而是自研了新型AI芯片——LPU（Language Processing Units）。每秒500 tokens，写论文比你眨眼还快LPU最突出的特点就是快。根据2024年一月的测试结果，由Groq LPU驱动Meta Llama 2模型，推理性能遥遥领先，是顶级云计算供应商的18倍。图片来源：GIT HUB华尔街见闻此前文章提及，Groq LPU搭配Meta Llama 2 70B能在7分钟内就能生成与莎士比亚《哈姆雷特》相同数量的单词，比普通人的打字速度快75倍。如下图所示，有推特网友问了一个和营销有关的专业问题，Groq在四秒钟之内就输出了上千词的长篇大论。还有网友测试同时用Gemini、GPT-4和Groq完成一个代码调试问题。结果，Groq的输出速度比Gemini快10倍，比GPT-4快18倍。Groq在速度上对其他AI模型的降维打击，让网友直呼，“AI推理界的美国队长来了”。LPU，英伟达GPU的挑战者？再强调一遍，Groq没有开发新的模型，它只是用了不一样的芯片。根据Groq官网的介绍，LPU是一种专为AI推理所设计的芯片。驱动包括GPT等主流大模型的GPU，是一种为图形渲染而设计的并行处理器，有数百个内核，而LPU架构则与GPU使用的SIMD（单指令，多数据）不同，这种设计可以让芯片更有效地利用每个时钟周期，确保一致的延迟和吞吐量，也降低了复杂调度硬件的需求：Groq的LPU推理引擎不是普通的处理单元；它是一个端到端系统，专为需要大量计算和连续处理的应用（如LLM）提供最快的推理而设计。通过消除外部内存瓶颈，LPU推理引擎的性能比传统GPU高出几个数量级。简单来说，对用户而言，最直观的体验就是“快”。使用过GPT的读者一定知道，痛苦地等待大模型一个一个地吐出字符是一种怎样痛苦的体验，而LPU驱动下的大模型，基本可以做到实时响应。比如下图，华尔街见闻向Groq询问LPU和GPU的区别，Groq生成这个回答用时不到3秒，完全不会像GPT、Gemini那样出现显著的延迟。如果以英文提问，生成速度还会更快。Groq官方的介绍还显示，创新的芯片架构可以把多个张量流处理器（Tensor Streaming Processor，简称TSP）连接在一起，而不会出现GPU集群中的传统瓶颈，因此具有极高的可扩展性，简化了大规模AI模型的硬件要求。能效也是LPU的另一个亮点。通过减少管理多个线程的开销和避免内核的利用率不足，LPU每瓦特可以提供更多的算力。Groq创始人兼首席执行官Jonathan Ross在采访中，时时不忘给英伟达上眼药。他此前对媒体表示，在大模型推理场景，Groq LPU芯片的速度比英伟达GPU快10倍，但价格和耗电量都仅为后者的十分之一。实时推理是通过经过训练的AI模型运行数据的计算过程，以提供AI应用的即时结果，从而实现流畅的最终用户体验。随着AI大模型的发展，实时推理的需求激增。Ross认为，对于在产品中使用人工智能的公司来说，推理成本正在成为一个问题，因为随着使用这些产品的客户数量增加，运行模型的成本也在迅速增加。与英伟达GPU相比，Groq LPU集群将为大模型推理提供更高的吞吐量、更低的延迟和更低的成本。他还强调，Groq的芯片，由于技术路径不同，在供应方面比英伟达更充足，不会被台积电或者SK海力士等供应商卡脖子：GroqChip LPU的独特之处在于，它不依赖于三星或SK海力士的HBM，也不依赖于台积电将外部HBM焊接到芯片上的CoWoS封装技术。不过，另有一些AI专家在社交媒体上表示，Groq芯片的实际成本并不低。如人工智能专家贾扬清分析称，Groq综合成本相当于英伟达GPU的30多倍。考虑到每张Groq芯片的内存容量为230MB，实际运行模型需要572张芯片，总成本高达1144万美元。相比之下，8张H100的系统在性能上与Groq系统相当，但硬件成本仅为30万美元，年度电费约2.4万美元。三年总运营成本对比显示，Groq系统的运营成本远高于H100系统，而且，更关键的是，LPU目前仅用于推理，要训练大模型，仍然需要购买英伟达GPU。创始人为谷歌TPU设计者之一 相信未来2年能卖出100万个LPU在今天互联网上一炮而红之前，Groq已经低调埋头研发7年多的时间。公开资料显示，Groq成立于2016年，总部位于美国加州圣塔克拉拉山景城。公司创始人Jonathan Ross是前谷歌高级工程师，是谷歌自研AI芯片TPU的设计者之一。产品主管John Barrus曾在谷歌及亚马逊担任产品高管。高管内唯一一位华裔面孔、副总裁Estelle Hong，在公司任职已有四年，此前曾供职于美国军队及英特尔。就在去年8月，Groq也宣布了和三星的合作计划，表示其下一代芯片将在美国德克萨斯州三星芯片工厂采用4纳米工艺生产，预计量产时间为24年下半年。展望下一代LPU，Ross相信GroqChip的能效将提高15到20倍，可以在相同的功率范围内为设备增加更多的矩阵计算和SRAM存储器。在去年底的采访中，Ross表示，考虑到GPU的短缺和高昂的成本，他相信Groq未来的发展潜力：在12个月内，我们可以部署10万个LPU，在24个月内，我们可以部署100万个LPU。","news_type":1},"isVote":1,"tweetType":1,"viewCount":772,"authorTweetTopStatus":1,"verified":2,"comments":[],"imageCount":0,"langContent":"CN","totalScore":0},{"id":336244652,"gmtCreate":1610112638376,"gmtModify":1703742772191,"author":{"id":"45014872579125","authorId":"45014872579125","name":"embcx","avatar":"https://static.laohu8.com/tiger_upload_file_1456326044.png","crmLevel":5,"crmLevelSwitch":0,"followedFlag":false,"idStr":"45014872579125","authorIdStr":"45014872579125"},"themes":[],"htmlText":"<a href=\"https://laohu8.com/S/FTFT\">$未来金融科技(FTFT)$</a>空试试","listText":"<a 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