{"id":1078,"date":"2026-06-11T10:04:50","date_gmt":"2026-06-11T10:04:50","guid":{"rendered":"https:\/\/convly.ai\/nvidia-vera-rubin-explained-2026\/"},"modified":"2026-06-11T10:15:33","modified_gmt":"2026-06-11T10:15:33","slug":"nvidia-vera-rubin-explained-2026","status":"publish","type":"post","link":"https:\/\/convly.ai\/fr\/nvidia-vera-rubin-explained-2026\/","title":{"rendered":"NVIDIA Vera Rubin Explained: The Next-Gen AI Platform That Cuts Inference Costs 10\u00d7 (2026)"},"content":{"rendered":"<p>At Computex 2026, NVIDIA confirmed that <strong>Vera Rubin<\/strong> \u2014 the successor to the Blackwell architecture that powers today&#8217;s AI boom \u2014 is now <strong>in full production<\/strong>. It&#8217;s the most consequential AI-hardware announcement of the year, and the headline number is staggering: NVIDIA says Rubin cuts the cost of AI inference by <strong>up to 10\u00d7<\/strong>. That doesn&#8217;t just matter to hyperscalers building data centers \u2014 it shapes the price of every AI tool you use. Here&#8217;s a clear, professional breakdown of what Vera Rubin actually is.<\/p>\n<div class=\"convly-tldr\">\n<h3>Principaux enseignements<\/h3>\n<ul>\n<li><strong>Vera Rubin<\/strong> is NVIDIA&#8217;s next-generation AI platform, the successor to Blackwell \u2014 now in full production (announced at Computex 2026).<\/li>\n<li><strong>The headline:<\/strong> NVIDIA&#8217;s figures claim <strong>up to 10\u00d7 lower inference token cost<\/strong> et <strong>4\u00d7 fewer GPUs<\/strong> to train Mixture-of-Experts models vs Blackwell.<\/li>\n<li><strong>It&#8217;s a six-chip platform<\/strong>, not just a GPU \u2014 the flagship Vera Rubin NVL72 packs 72 Rubin GPUs and 36 Vera CPUs.<\/li>\n<li><strong>Rubin CPX<\/strong> is a separate new GPU built for <strong>million-token context<\/strong> inference (coding, video), with 128GB of GDDR7 each.<\/li>\n<li><strong>Availability:<\/strong> cloud instances in <strong>H2 2026<\/strong> (AWS, Google Cloud, Azure, OCI and more); Rubin CPX at the end of 2026.<\/li>\n<\/ul>\n<\/div>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_84 counter-flat ez-toc-counter ez-toc-container-direction\">\n<label for=\"ez-toc-cssicon-toggle-item-6a2fc74f531ca\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #000000;color:#000000\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #000000;color:#000000\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewbox=\"0 0 24 24\" version=\"1.2\" baseprofile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-6a2fc74f531ca\"  aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1' ><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/convly.ai\/fr\/nvidia-vera-rubin-explained-2026\/#What_is_NVIDIA_Vera_Rubin\" >What is NVIDIA Vera Rubin?<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/convly.ai\/fr\/nvidia-vera-rubin-explained-2026\/#The_headline_numbers_%E2%80%94_and_what_they_mean\" >The headline numbers \u2014 and what they mean<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/convly.ai\/fr\/nvidia-vera-rubin-explained-2026\/#The_six_chips_that_make_up_the_platform\" >The six chips that make up the platform<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/convly.ai\/fr\/nvidia-vera-rubin-explained-2026\/#Rubin_CPX_a_GPU_built_for_million-token_context\" >Rubin CPX: a GPU built for million-token context<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/convly.ai\/fr\/nvidia-vera-rubin-explained-2026\/#When_can_you_actually_use_it\" >When can you actually use it?<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/convly.ai\/fr\/nvidia-vera-rubin-explained-2026\/#Rubin_vs_Blackwell\" >Rubin vs Blackwell<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/convly.ai\/fr\/nvidia-vera-rubin-explained-2026\/#Why_it_matters_%E2%80%94_even_if_you_never_touch_one\" >Why it matters \u2014 even if you never touch one<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/convly.ai\/fr\/nvidia-vera-rubin-explained-2026\/#FAQ\" >FAQ<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/convly.ai\/fr\/nvidia-vera-rubin-explained-2026\/#Bottom_line\" >R\u00e9sultat<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/convly.ai\/fr\/nvidia-vera-rubin-explained-2026\/#Related_articles\" >Articles connexes<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"What_is_NVIDIA_Vera_Rubin\"><\/span>What is NVIDIA Vera Rubin?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Vera Rubin is NVIDIA&#8217;s <strong>next-generation AI compute platform<\/strong> \u2014 the architecture that follows Blackwell (the GB200\/GB300 generation currently powering most frontier AI training and inference). Named after the astronomer who provided early evidence for dark matter, Rubin isn&#8217;t a single chip but a tightly <strong>co-designed platform of six chips<\/strong> engineered to work as one &#8220;AI factory.&#8221;<\/p>\n<p>The strategic goal is efficiency. Training and serving today&#8217;s largest models is brutally expensive, and the single biggest cost in production AI is <strong>d\u00e9duction<\/strong> \u2014 actually running the model for users. Rubin is NVIDIA&#8217;s answer to that cost curve.<\/p>\n<div class=\"convly-specs\">\n<div><strong>Platform<\/strong><span>NVIDIA Vera Rubin (successor to Blackwell)<\/span><\/div>\n<div><strong>Announced<\/strong><span>Computex 2026 \u2014 now in full production<\/span><\/div>\n<div><strong>Flagship system<\/strong><span>Vera Rubin NVL72 (72 Rubin GPUs + 36 Vera CPUs)<\/span><\/div>\n<div><strong>Rubin GPU<\/strong><span>3rd-gen Transformer Engine, 50 petaflops NVFP4 inference<\/span><\/div>\n<div><strong>Vera CPU<\/strong><span>88 custom Olympus cores, Armv9.2, NVLink-C2C<\/span><\/div>\n<div><strong>Inference cost vs Blackwell<\/strong><span>Up to 10\u00d7 lower (NVIDIA figures)<\/span><\/div>\n<div><strong>Cloud availability<\/strong><span>Second half of 2026<\/span><\/div>\n<\/div>\n<h2><span class=\"ez-toc-section\" id=\"The_headline_numbers_%E2%80%94_and_what_they_mean\"><\/span>The headline numbers \u2014 and what they mean<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Two figures from NVIDIA define why Rubin matters:<\/p>\n<ul>\n<li><strong>Up to a 10\u00d7 reduction in inference token cost<\/strong> versus Blackwell. Inference cost is what determines the price of an AI API call. A 10\u00d7 efficiency gain is the kind of step-change that lets providers slash prices, raise rate limits, or ship far more capable models at the same cost.<\/li>\n<li><strong>A 4\u00d7 reduction in the number of GPUs<\/strong> needed to train Mixture-of-Experts (MoE) models. Nearly every frontier model in 2026 \u2014 from GPT to Claude to the open Chinese models \u2014 is an MoE. Cutting the GPU count 4\u00d7 directly lowers the barrier to training frontier-scale models.<\/li>\n<\/ul>\n<p>As always with vendor benchmarks, treat these as NVIDIA&#8217;s best-case figures until independent labs verify them. But even a fraction of the claimed gains reshapes the economics of AI. The reason your AI tools keep getting cheaper and faster is hardware like this.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"The_six_chips_that_make_up_the_platform\"><\/span>The six chips that make up the platform<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Rubin&#8217;s efficiency comes from <strong>co-designing the whole rack<\/strong>, not just the GPU. The platform spans six chips:<\/p>\n<ol>\n<li><strong>Vera CPU<\/strong> \u2014 88 custom &#8220;Olympus&#8221; cores (Armv9.2), tuned for agentic reasoning and tightly coupled to the GPUs via NVLink-C2C.<\/li>\n<li><strong>Rubin GPU<\/strong> \u2014 the compute engine, with a third-generation Transformer Engine, hardware-accelerated adaptive compression, and <strong>50 petaflops of NVFP4<\/strong> inference performance.<\/li>\n<li><strong>NVLink 6 Switch<\/strong> \u2014 the interconnect, at <strong>3.6 TB\/s per GPU<\/strong> et <strong>260 TB\/s aggregate<\/strong> across a single NVL72 rack.<\/li>\n<li><strong>ConnectX-9 SuperNIC<\/strong> \u2014 high-speed networking integrated into the NVL72 design.<\/li>\n<li><strong>BlueField-4 DPU<\/strong> \u2014 powers AI-native storage and efficient <strong>key-value (KV) cache reuse<\/strong>, which directly speeds up long-context inference.<\/li>\n<li><strong>Spectrum-6 Ethernet Switch<\/strong> \u2014 built on 200G SerDes with co-packaged optics for scale-out AI factories.<\/li>\n<\/ol>\n<p>The flagship system, the <strong>Vera Rubin NVL72<\/strong>, combines 72 Rubin GPUs and 36 Vera CPUs into one rack \u2014 and NVIDIA says it&#8217;s up to <strong>18\u00d7 faster to assemble and service<\/strong> than Blackwell, which matters enormously at data-center scale.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Rubin_CPX_a_GPU_built_for_million-token_context\"><\/span>Rubin CPX: a GPU built for million-token context<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Alongside the standard platform, NVIDIA unveiled a genuinely new category: the <strong>Rubin CPX<\/strong>, a GPU &#8220;purpose-built for massive-context processing.&#8221; This is the chip aimed squarely at the long-context era \u2014 the million-token software-coding and generative-video workloads that today&#8217;s models increasingly demand.<\/p>\n<p>Each Rubin CPX carries <strong>128GB of GDDR7<\/strong> and up to <strong>30 petaflops<\/strong> of NVFP4 compute, and uniquely integrates video encode\/decode hardware alongside long-context inference on one chip. At rack scale, the <strong>Vera Rubin NVL144 CPX<\/strong> delivers a claimed <strong>8 exaflops<\/strong> of AI compute and <strong>100TB of fast memory<\/strong>, which NVIDIA says is <strong>7.5\u00d7 more AI performance<\/strong> than a GB300 NVL72 system, with <strong>3\u00d7 faster attention<\/strong>. It&#8217;s expected at the <strong>end of 2026<\/strong>.<\/p>\n<p>For anyone tracking why context windows keep ballooning \u2014 the 1M-token windows in models like <a href=\"\/fr\/deepseek-vs-chatgpt-2026\/\">DeepSeek and the latest frontier models<\/a> \u2014 Rubin CPX is the hardware making million-token inference economically viable.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"When_can_you_actually_use_it\"><\/span>When can you actually use it?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Rubin is a data-center platform, so you won&#8217;t buy one \u2014 but you&#8217;ll feel it through the services you use:<\/p>\n<ul>\n<li><strong>Cloud instances arrive in the second half of 2026.<\/strong> Among the first providers: <strong>AWS, Google Cloud, Microsoft Azure, and OCI<\/strong>, plus NVIDIA Cloud Partners CoreWeave, Lambda, Nebius, and Nscale. If you rent GPUs, watch our roundup of the <a href=\"\/fr\/best-cloud-gpu-providers-for-ai-2026\/\">best cloud GPU providers for AI<\/a> for when Rubin instances list.<\/li>\n<li><strong>Rubin CPX lands at the end of 2026<\/strong> for long-context and video workloads.<\/li>\n<li><strong>The local angle:<\/strong> at Computex, NVIDIA also laid out a roadmap bringing the architecture toward <strong>local AI desktops and laptops<\/strong> \u2014 its RTX\/DGX Spark line, with a Rubin-based generation (using LPDDR6 memory) followed by future &#8220;Rosa&#8221; and &#8220;Feynman&#8221; designs. So the technology that starts in the data center is on a path to the desk, much like today&#8217;s <a href=\"\/fr\/nvidia-digits-personal-ai-computer-review\/\">personal AI computers<\/a>.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"Rubin_vs_Blackwell\"><\/span>Rubin vs Blackwell<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<table class=\"convly-vs\">\n<thead>\n<tr>\n<th>Dimension<\/th>\n<th>Vera Rubin (next-gen)<\/th>\n<th>Blackwell (current)<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Flagship system<\/td>\n<td class=\"convly-vs-winner\">Vera Rubin NVL72<\/td>\n<td>GB300 NVL72<\/td>\n<\/tr>\n<tr>\n<td>Inference token cost<\/td>\n<td class=\"convly-vs-winner\">Up to 10\u00d7 lower<\/td>\n<td>Baseline<\/td>\n<\/tr>\n<tr>\n<td>GPUs to train an MoE<\/td>\n<td class=\"convly-vs-winner\">4\u00d7 fewer<\/td>\n<td>Baseline<\/td>\n<\/tr>\n<tr>\n<td>Assembly \/ servicing<\/td>\n<td class=\"convly-vs-winner\">Up to 18\u00d7 faster<\/td>\n<td>Baseline<\/td>\n<\/tr>\n<tr>\n<td>Long-context chip<\/td>\n<td class=\"convly-vs-winner\">Rubin CPX (128GB, 1M-token)<\/td>\n<td>\u2014<\/td>\n<\/tr>\n<tr>\n<td>Status<\/td>\n<td>Full production; cloud H2 2026<\/td>\n<td class=\"convly-vs-winner\">Shipping now<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2><span class=\"ez-toc-section\" id=\"Why_it_matters_%E2%80%94_even_if_you_never_touch_one\"><\/span>Why it matters \u2014 even if you never touch one<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>It&#8217;s tempting to file data-center GPUs under &#8220;not my problem.&#8221; But Rubin affects everyone who uses AI:<\/p>\n<ul>\n<li><strong>Cheaper, more capable AI tools.<\/strong> A 10\u00d7 inference efficiency gain is what lets providers keep cutting API prices and raising limits. The relentless drop in the cost of using models like <a href=\"\/fr\/gpt5-vs-claude4-vs-gemini3\/\">Claude and GPT<\/a> is downstream of exactly this kind of hardware leap.<\/li>\n<li><strong>Longer context, for real.<\/strong> Rubin CPX makes million-token inference economical, which is why frontier models keep extending their context windows.<\/li>\n<li><strong>The squeeze on consumer GPUs.<\/strong> The flip side: insatiable demand for AI accelerators (and the memory they consume) is part of why consumer graphics cards are scarce and pricey in 2026. If you&#8217;re building a local AI rig, see our <a href=\"\/fr\/best-gpus-for-local-llms-2026\/\">les meilleurs GPU pour les LLM locaux<\/a> guide.<\/li>\n<li><strong>The local trickle-down.<\/strong> What ships in an NVL72 rack today defines what lands in a desktop AI box in a couple of years.<\/li>\n<\/ul>\n<h2><span class=\"ez-toc-section\" id=\"FAQ\"><\/span>FAQ<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3>What is NVIDIA Vera Rubin?<\/h3>\n<p>Vera Rubin is NVIDIA&#8217;s next-generation AI platform and the successor to Blackwell, announced in full production at Computex 2026. It&#8217;s a co-designed six-chip platform (Vera CPU, Rubin GPU, NVLink 6, ConnectX-9, BlueField-4, Spectrum-6) built to dramatically lower the cost of training and running AI models.<\/p>\n<h3>How much faster is Rubin than Blackwell?<\/h3>\n<p>According to NVIDIA&#8217;s own figures, Rubin delivers up to a 10\u00d7 reduction in inference token cost and needs 4\u00d7 fewer GPUs to train Mixture-of-Experts models compared with Blackwell. Its flagship NVL72 system is also up to 18\u00d7 faster to assemble and service. These are vendor benchmarks, so independent verification is still pending.<\/p>\n<h3>What is the Rubin CPX?<\/h3>\n<p>Rubin CPX is a new class of NVIDIA GPU purpose-built for massive-context inference \u2014 think million-token coding and generative video. Each has 128GB of GDDR7 and up to 30 petaflops of NVFP4 compute, with integrated video encode\/decode. It&#8217;s expected at the end of 2026.<\/p>\n<h3>When will NVIDIA Rubin be available?<\/h3>\n<p>Rubin is in full production now, with cloud instances expected in the second half of 2026 from providers including AWS, Google Cloud, Microsoft Azure, OCI, CoreWeave, Lambda, Nebius, and Nscale. Rubin CPX arrives at the end of 2026.<\/p>\n<h3>Can I buy a Rubin GPU for my PC?<\/h3>\n<p>No \u2014 Rubin is a data-center platform you&#8217;ll access through cloud providers, not a consumer card. However, NVIDIA outlined a roadmap bringing the architecture to local AI desktops and laptops (its RTX\/DGX Spark line) over the next few generations.<\/p>\n<h3>What does Rubin mean for AI prices?<\/h3>\n<p>Lower inference cost is the main lever behind falling AI API prices and rising usage limits. If NVIDIA&#8217;s efficiency claims hold up, Rubin should help make the AI tools you use cheaper, faster, and capable of handling much longer inputs.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Bottom_line\"><\/span>R\u00e9sultat<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Vera Rubin is the clearest signal yet of where AI is heading: not just smarter models, but <strong>radically cheaper ones to run<\/strong>. By co-designing an entire six-chip platform around inference efficiency \u2014 and adding a dedicated million-token chip in the Rubin CPX \u2014 NVIDIA is attacking the single biggest cost in production AI. The claimed 10\u00d7 inference saving won&#8217;t all reach your bill, and the vendor numbers deserve independent scrutiny. But the direction is unmistakable: the hardware that makes AI expensive today is being replaced by hardware that makes it cheap tomorrow \u2014 and that&#8217;s why your AI tools will keep getting better and more affordable through 2026 and beyond.<\/p>\n<p><!--related-block--><\/p>\n<div class=\"convly-related\">\n<h2><span class=\"ez-toc-section\" id=\"Related_articles\"><\/span>Articles connexes<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li><a href=\"https:\/\/convly.ai\/fr\/rtx-50-super-for-ai-2026\/\">RTX 5080 Super &amp; 5070 Super for AI: What the Leaked VRAM Upgrades Mean for Local LLMs (2026)<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/fr\/rx-9070-xt-vs-rtx-5080-for-ai-2026\/\">AMD RX 9070 XT vs RTX 5080 for AI in 2026: Can AMD Punch Above Its Price?<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/fr\/rx-9070-xt-vs-rtx-5070-ti-for-ai-2026\/\">AMD RX 9070 XT vs RTX 5070 Ti for AI in 2026: Does ROCm Close the Gap?<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/fr\/rtx-pro-6000-vs-rtx-5090-for-ai-2026\/\">RTX Pro 6000 Blackwell vs RTX 5090 for AI in 2026: When Is 96GB Worth $5,500 More?<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/fr\/rtx-5070-vs-rtx-5080-for-ai-2026\/\">RTX 5070 vs RTX 5080 for AI in 2026: Is the Jump to 16GB Worth $450?<\/a><\/li>\n<\/ul>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>NVIDIA&#8217;s Vera Rubin is the biggest AI-hardware story of 2026: a six-chip platform that NVIDIA says cuts inference costs up to 10\u00d7 versus Blackwell. Here&#8217;s what it is and why it matters even if you never own one.<\/p>","protected":false},"author":1,"featured_media":1079,"comment_status":"closed","ping_status":"closed","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":[248],"tags":[718,717,714,716,715],"class_list":["post-1078","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-gpus","tag-ai-data-center","tag-nvidia-blackwell","tag-nvidia-rubin","tag-rubin-gpu","tag-vera-rubin"],"_links":{"self":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts\/1078","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/comments?post=1078"}],"version-history":[{"count":2,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts\/1078\/revisions"}],"predecessor-version":[{"id":1097,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts\/1078\/revisions\/1097"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/media\/1079"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/media?parent=1078"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/categories?post=1078"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/tags?post=1078"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}