{"id":411,"date":"2026-09-16T08:07:55","date_gmt":"2026-09-16T00:07:55","guid":{"rendered":"https:\/\/aidashxp.com\/nvidia-vera-rubin-efficiency\/"},"modified":"2026-09-16T08:07:55","modified_gmt":"2026-09-16T00:07:55","slug":"nvidia-vera-rubin-efficiency","status":"publish","type":"post","link":"https:\/\/aidashxp.com\/en\/nvidia-vera-rubin-efficiency\/","title":{"rendered":"Nvidia Vera Rubin Achieves 7\u00d7 the Energy Efficiency of Blackwell; Meta\u2019s In-House Chip Enters Mass Production in 2027"},"content":{"rendered":"<p class=\"wp-block-paragraph\">The AI chip race is shifting from \u201csingle-GPU performance\u201d to a more pragmatic question:<strong>How many tokens can be generated per kilowatt-hour?<\/strong>Nvidia\u2019s latest disclosed data shows that on the next-generation <strong>Vera Rubin NVL72<\/strong> exist <a href=\"https:\/\/chat.deepseek.com\" target=\"_blank\" rel=\"nofollow noopener\">DeepSeek<\/a> V4 Pro running a 1.6-trillion-parameter model, token throughput per megawatt is <strong>7\u00d7 that of Blackwell<\/strong>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Nvidia\u2019s New Accounting Methodology<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Specific figures: Under an assumption of 100 tokens per second per user, Rubin delivers 59.4 million tokens\/second per megawatt, versus GB300\u2019s 28.5 million. Nvidia has also introduced a new metric\u2014<strong>\u201cProfit per gigawatt-year\u201d<\/strong>\u2014with Rubin at approximately $14.99 billion and GB300 at approximately $10.53 billion. This clearly targets the industry pain point that \u201cpower supply and capacity are the bottlenecks,\u201d as power consumption is precisely the wall every buyer is hitting this month.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Note that<strong>Vera Rubin<\/strong> Vera Rubin is Nvidia\u2019s next-generation architecture following Blackwell (named after astronomer Vera Rubin), and the \u201c7\u00d7\u201d figure is a modeled projection\u2014not a measured result. Yet even halving it would sufficiently explain why all hyperscale customers are rushing orders for Rubin before Blackwell has fully depreciated\u2014power allocations are finite, and higher output per unit of energy consumption means more users served within the same data center footprint.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Meta Accelerates In-House Chip Development<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Meanwhile, Meta has confirmed its in-house <strong>MTIA 450<\/strong> chip will enter mass production in the first half of 2027<strong>MTIA 500<\/strong> Follow-up by end of 2027: the former doubles HBM bandwidth over the previous generation, while the latter boosts it by another 50% and increases HBM capacity by up to 80%, targeting approximately <strong>44% TCO (total cost of ownership) savings versus GPUs<\/strong>, aligned with a roughly $115 billion capital expenditure plan.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">One commonality is:<strong>Memory has become central to all in-house chips<\/strong>. Meta\u2019s HBM addition and Positron\u2019s launch last week of 2304 GB per die both point to the same conclusion\u2014what\u2019s currently scarce is not compute, but memory.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Another disruptor<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Also noteworthy is <strong>Cornelis<\/strong>: this company raised $205 million to build a GPU-agnostic interconnect layer, competing with InfiniBand and NVLink; its 400 Gbps CN5000 is already shipping, and its 800 Gbps CN6000 will ship in Q4. Its significance lies in enabling buyers to mix Nvidia, AMD, and in-house chips within the same cluster.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For ordinary users, these changes will ultimately manifest in two ways: continued decline in AI service costs and longer uptime. To stay updated on AI tools and infrastructure developments, visit <a href=\"https:\/\/aidashxp.com\/en\/\">AI Dash<\/a>.<\/p>","protected":false},"excerpt":{"rendered":"<p>AI \u82af\u7247\u7684\u7ade\u4e89\uff0c\u6b63\u5728\u4ece\u300c\u5355\u5361\u6027\u80fd\u300d\u8f6c\u5411 [&hellip;]<\/p>\n","protected":false},"author":0,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[1],"tags":[],"class_list":["post-411","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/aidashxp.com\/en\/wp-json\/wp\/v2\/posts\/411","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/aidashxp.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/aidashxp.com\/en\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/aidashxp.com\/en\/wp-json\/wp\/v2\/comments?post=411"}],"version-history":[{"count":0,"href":"https:\/\/aidashxp.com\/en\/wp-json\/wp\/v2\/posts\/411\/revisions"}],"wp:attachment":[{"href":"https:\/\/aidashxp.com\/en\/wp-json\/wp\/v2\/media?parent=411"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/aidashxp.com\/en\/wp-json\/wp\/v2\/categories?post=411"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/aidashxp.com\/en\/wp-json\/wp\/v2\/tags?post=411"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}