{"id":659,"date":"2026-05-20T20:10:14","date_gmt":"2026-05-20T20:10:14","guid":{"rendered":"https:\/\/convly.ai\/rtx-5080-vs-rtx-4080-super-for-ai\/"},"modified":"2026-06-10T05:05:09","modified_gmt":"2026-06-10T05:05:09","slug":"rtx-5080-vs-rtx-4080-super-for-ai","status":"publish","type":"post","link":"https:\/\/convly.ai\/pt\/rtx-5080-vs-rtx-4080-super-for-ai\/","title":{"rendered":"RTX 5080 versus RTX 4080 Super para IA em 2026: Diferen\u00e7a geracional ou atualiza\u00e7\u00e3o lateral?"},"content":{"rendered":"<p>O <strong>RTX 5080<\/strong> e o <strong>RTX 4080 Super<\/strong> occupy the exact same slot in NVIDIA&#8217;s lineup \u2014 the $999 enthusiast card one tier below the flagship. Both carry <strong>16 GB of VRAM<\/strong>. So the AI buyer&#8217;s question is simple: does Blackwell bring enough to justify choosing the 5080, or is the 4080 Super still the smart pickup?<\/p>\n<p>The short answer: <strong>the 5080 is the better card, but the upgrade gap is narrower than the generation number suggests.<\/strong><\/p>\n<div class=\"convly-tldr\">\n<h3>Principais conclus\u00f5es<\/h3>\n<ul>\n<li>Both cards have <strong>16 GB VRAM<\/strong> \u2014 identical model-size ceiling.<\/li>\n<li>The RTX 5080&#8217;s <strong>GDDR7 memory<\/strong> delivers ~960 GB\/s vs the 4080 Super&#8217;s ~736 GB\/s \u2014 a real ~30% bandwidth jump.<\/li>\n<li>Expect <strong>~15\u201320% faster LLM inference<\/strong> on the 5080, driven mostly by bandwidth.<\/li>\n<li>Blackwell adds native <strong>FP4<\/strong> support \u2014 useful for next-gen quantized models, irrelevant today.<\/li>\n<li>If you already own a 4080 Super, do not upgrade. If you are buying fresh, the 5080 is the better long-term card.<\/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-6a38af2b188b9\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Alternar<\/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-6a38af2b188b9\"  aria-label=\"Alternar\" \/><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\/pt\/rtx-5080-vs-rtx-4080-super-for-ai\/#At_a_glance\" >At a glance<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/convly.ai\/pt\/rtx-5080-vs-rtx-4080-super-for-ai\/#16_GB_the_shared_ceiling\" >16 GB: the shared ceiling<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/convly.ai\/pt\/rtx-5080-vs-rtx-4080-super-for-ai\/#Where_Blackwell_pulls_ahead_bandwidth\" >Where Blackwell pulls ahead: bandwidth<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/convly.ai\/pt\/rtx-5080-vs-rtx-4080-super-for-ai\/#FP4_a_feature_for_tomorrow\" >FP4: a feature for tomorrow<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/convly.ai\/pt\/rtx-5080-vs-rtx-4080-super-for-ai\/#Power_and_efficiency\" >Power and efficiency<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/convly.ai\/pt\/rtx-5080-vs-rtx-4080-super-for-ai\/#The_16_GB_warning\" >The 16 GB warning<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/convly.ai\/pt\/rtx-5080-vs-rtx-4080-super-for-ai\/#The_price_reality_what_you_actually_pay_and_which_to_buy\" >The price reality: what you actually pay, and which to buy<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/convly.ai\/pt\/rtx-5080-vs-rtx-4080-super-for-ai\/#FAQ\" >Perguntas frequentes<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/convly.ai\/pt\/rtx-5080-vs-rtx-4080-super-for-ai\/#Verdict\" >Verdict<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/convly.ai\/pt\/rtx-5080-vs-rtx-4080-super-for-ai\/#Related_articles\" >Artigos relacionados<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"At_a_glance\"><\/span>At a glance<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<table class=\"convly-vs\">\n<thead>\n<tr>\n<th>Especifica\u00e7\u00f5es<\/th>\n<th>RTX 5080<\/th>\n<th>RTX 4080 Super<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Arquitetura<\/td>\n<td>Blackwell GB203<\/td>\n<td>Ada Lovelace AD103<\/td>\n<\/tr>\n<tr>\n<td>N\u00facleos CUDA<\/td>\n<td class=\"convly-vs-winner\">10,752<\/td>\n<td>10,240<\/td>\n<\/tr>\n<tr>\n<td>VRAM<\/td>\n<td>16 GB GDDR7<\/td>\n<td>16 GB GDDR6X<\/td>\n<\/tr>\n<tr>\n<td>Largura de banda de mem\u00f3ria<\/td>\n<td class=\"convly-vs-winner\">~960 GB\/s<\/td>\n<td>~736 GB\/s<\/td>\n<\/tr>\n<tr>\n<td>FP16 Tensor (dense)<\/td>\n<td class=\"convly-vs-winner\">~450 TFLOPS<\/td>\n<td>~390 TFLOPS<\/td>\n<\/tr>\n<tr>\n<td>Low-precision<\/td>\n<td class=\"convly-vs-winner\">FP8 + FP4<\/td>\n<td>FP8<\/td>\n<\/tr>\n<tr>\n<td>TDP<\/td>\n<td>360 W<\/td>\n<td class=\"convly-vs-winner\">320 W<\/td>\n<\/tr>\n<tr>\n<td>Price<\/td>\n<td>$999<\/td>\n<td>$999<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2><span class=\"ez-toc-section\" id=\"16_GB_the_shared_ceiling\"><\/span>16 GB: the shared ceiling<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Neither card is a big-model machine. <strong>16 GB of VRAM<\/strong> comfortably handles:<\/p>\n<ul>\n<li><strong>Llama 3 8B<\/strong> at 8-bit, or <strong>13B-class<\/strong> models at 4-bit<\/li>\n<li><strong>Stable Diffusion XL<\/strong> e <strong>Flux.1<\/strong> image generation<\/li>\n<li><strong>LoRA fine-tuning<\/strong> of 7B\u20138B models<\/li>\n<\/ul>\n<p>Neither card runs a 70B model in VRAM. If that is your goal, you want a 24 GB or 32 GB card and should stop reading here. For everyone else \u2014 the large majority of local AI users \u2014 16 GB is the practical sweet spot, and both cards deliver it.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Where_Blackwell_pulls_ahead_bandwidth\"><\/span>Where Blackwell pulls ahead: bandwidth<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The CUDA-core counts are nearly identical (10,752 vs 10,240), so raw shader power is close. The real generational change is <strong>memory bandwidth<\/strong>. LLM token generation is memory-bound \u2014 the GPU spends most of its time reading weights, not computing \u2014 so the 5080&#8217;s GDDR7 advantage shows up directly:<\/p>\n<table class=\"convly-vs\">\n<thead>\n<tr>\n<th>Workload<\/th>\n<th>RTX 5080<\/th>\n<th>RTX 4080 Super<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Llama 3 8B Q4_K_M<\/td>\n<td class=\"convly-vs-winner\">~125 tok\/s<\/td>\n<td>~108 tok\/s<\/td>\n<\/tr>\n<tr>\n<td>Llama 3 13B-class Q4<\/td>\n<td class=\"convly-vs-winner\">~78 tok\/s<\/td>\n<td>~66 tok\/s<\/td>\n<\/tr>\n<tr>\n<td>SDXL 1024\u00d71024 (30 steps)<\/td>\n<td class=\"convly-vs-winner\">~14 it\/s<\/td>\n<td>~13 it\/s<\/td>\n<\/tr>\n<tr>\n<td>Flux.1 dev (1024px)<\/td>\n<td class=\"convly-vs-winner\">~3.1 s\/image<\/td>\n<td>~3.5 s\/image<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Note the split: <strong>LLM inference<\/strong> sees the biggest gains (~15\u201320%) because it is bandwidth-bound, while <strong>Stable Diffusion<\/strong> \u2014 which is compute-bound \u2014 shows only a small lead since the core counts are so close.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"FP4_a_feature_for_tomorrow\"><\/span>FP4: a feature for tomorrow<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Blackwell introduces native <strong>FP4<\/strong> (4-bit floating point) tensor operations. On paper this doubles low-precision throughput versus FP8. In practice, as of 2026, almost no mainstream inference stack ships production FP4 kernels for consumer workloads. It is a genuine advantage, but a <strong>future-facing<\/strong> one \u2014 it will matter more in 2027 than it does today.<\/p>\n<p>If you keep GPUs for four or five years, FP4 support is a real reason to favor the 5080. If you upgrade every cycle, it is close to irrelevant.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Power_and_efficiency\"><\/span>Power and efficiency<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The 5080 draws <strong>360 W<\/strong> versus the 4080 Super&#8217;s <strong>320 W<\/strong>. Blackwell is more efficient per operation, but the 5080 spends that headroom on higher clocks, so absolute draw is up. Both are happy on an 850 W PSU. Neither is a thermal problem in a well-ventilated case.<\/p>\n<div class=\"convly-procons\">\n<div class=\"pros\">\n<h4>Escolha a RTX 5080 se<\/h4>\n<ul>\n<li>You are buying fresh and want the longer-lived card<\/li>\n<li>Your main workload is LLM inference (bandwidth-bound)<\/li>\n<li>You want FP4 readiness for future quantized models<\/li>\n<\/ul>\n<\/div>\n<div class=\"cons\">\n<h4>Choose the RTX 4080 Super if<\/h4>\n<ul>\n<li>You find one discounted below $850 as stock clears<\/li>\n<li>Your focus is Stable Diffusion, where the gap is tiny<\/li>\n<li>You already own one \u2014 there is no reason to upgrade<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<h2><span class=\"ez-toc-section\" id=\"The_16_GB_warning\"><\/span>The 16 GB warning<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Whichever you choose, understand the limitation you are buying into. <strong>16 GB is increasingly tight<\/strong> for 2026 AI work. Larger image models, longer LLM context windows, and fine-tuning all push against that ceiling. If your budget can stretch to a 24 GB RTX 4090 or 32 GB RTX 5090, the capacity headroom outlasts the speed difference between these two 16 GB cards.<\/p>\n<p><!--ai-enriched--><\/p>\n<h2><span class=\"ez-toc-section\" id=\"The_price_reality_what_you_actually_pay_and_which_to_buy\"><\/span>The price reality: what you actually pay, and which to buy<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Spec sheets only matter once you factor in price, and this is where the two cards split decisively. They are no longer competing on the same shelf: the RTX 5080 is the current product, while the RTX 4080 Super has been discontinued and now lives almost entirely on the secondhand market. That changes the question from &#8220;which is faster&#8221; to &#8220;which makes sense at the price you can actually find.&#8221;<\/p>\n<p>The 5080 carries a $999 MSRP, but Blackwell supply has stayed tight because NVIDIA prioritised enterprise AI silicon, so real street prices have sat well above sticker for most of 2026 \u2014 frequently in the <strong>$1,150\u2013$1,250<\/strong> range. The 4080 Super, by contrast, has settled into a used-market groove around <strong>$850\u2013$900<\/strong>, with new old-stock units commanding inflated, often nonsensical, scalper pricing. So in practice you are weighing a new ~$1,200 card against a used ~$870 one.<\/p>\n<p>Here is the honest way to decide:<\/p>\n<ul>\n<li><strong>Buy the RTX 5080<\/strong> if you want a warranty, the newest software path (the 5th-gen Tensor cores and FP4 support are a forward-looking bet), and the genuine ~30% memory-bandwidth uplift that helps inference throughput. It is the right call for a fresh build where you would be buying a new GPU anyway.<\/li>\n<li><strong>Buy a used RTX 4080 Super<\/strong> if value-per-dollar for AI is the priority. You give up bandwidth and FP4, but you keep the same 16 GB ceiling \u2014 which is the real limiter for model size \u2014 and pocket roughly $300. For running quantised 7B\u201314B models and Stable Diffusion, that gap rarely shows up in everyday use.<\/li>\n<li><strong>Do not &#8220;upgrade&#8221; from a 4080 Super to a 5080.<\/strong> Selling one to buy the other nets a single-digit-to-low-double-digit performance change for a real cash outlay. Put that money toward a 24 GB card instead, where the extra VRAM unlocks models neither 16 GB card can touch.<\/li>\n<\/ul>\n<p>One wrinkle worth flagging: a rumoured RTX 5080 Super with 24 GB of GDDR7 has circulated, but it has been delayed indefinitely amid GDDR7 supply constraints, so it is not a card you can plan a purchase around today. If 16 GB is genuinely too tight for your workload, the answer is a 24 GB-class GPU now \u2014 not waiting on an unconfirmed launch.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"FAQ\"><\/span>Perguntas frequentes<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3>Is the RTX 5080 worth upgrading to from a 4080 Super?<\/h3>\n<p>No. Both have 16 GB, and the 5080 is only ~15\u201320% faster. That is not enough to justify the cost of a full GPU swap. Upgrade only if you are jumping two tiers, to a 24 GB or 32 GB card.<\/p>\n<h3>Can the RTX 5080 run Llama 3 70B?<\/h3>\n<p>No. 70B at 4-bit needs roughly 40 GB. The 5080&#8217;s 16 GB forces heavy CPU offload, which is slow. For 70B in VRAM you need an RTX 5090 (32 GB) or a multi-GPU build.<\/p>\n<h3>Does FP4 support matter in 2026?<\/h3>\n<p>Not yet for most users. FP4 is real and future-proof, but production inference stacks have not widely adopted it. Treat it as insurance for 2027, not a feature you will use this year.<\/p>\n<h3>Which is better for Stable Diffusion, the 5080 or 4080 Super?<\/h3>\n<p>They are nearly tied. Stable Diffusion is compute-bound and the two cards have almost identical CUDA-core counts. The 5080 leads by only ~5\u20138%.<\/p>\n<h3>Is a used RTX 4080 Super a smart buy for AI in 2026?<\/h3>\n<p>For many people, yes. It shares the 5080&#8217;s 16 GB VRAM ceiling \u2014 the factor that actually decides which models you can load \u2014 while typically costing a few hundred dollars less on the secondhand market. You sacrifice the 5080&#8217;s higher memory bandwidth and FP4 support, but for running quantised 7B\u201314B models and Stable Diffusion that trade-off is easy to live with. Buy from a seller with returns, and stress-test the card on day one.<\/p>\n<h3>Should I wait for the RTX 5080 Super with 24 GB before buying?<\/h3>\n<p>We would not plan around it. A 24 GB GDDR7 &#8220;5080 Super&#8221; has been rumoured, but reports point to an indefinite delay tied to GDDR7 memory supply, so there is no reliable date. If 16 GB is enough for your models, buy a 5080 or a used 4080 Super now. If you genuinely need more than 16 GB, get a 24 GB-class card today rather than betting on an unconfirmed release.<\/p>\n<h3>Why does the RTX 5080 cost more than its $999 MSRP?<\/h3>\n<p>Because supply has been constrained. NVIDIA shifted much of its manufacturing toward enterprise AI accelerators, leaving consumer Blackwell cards in short supply, so the 5080 has frequently sold above its $999 sticker \u2014 often around $1,150\u2013$1,250 \u2014 through 2026. Always budget against the real street price you can find in stock, not the MSRP, when comparing it to a used 4080 Super.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Verdict\"><\/span>Verdict<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>For a fresh purchase, the <strong>RTX 5080<\/strong> is the right call: same price as the 4080 Super, meaningfully more memory bandwidth, and FP4 headroom for the future. But this is an evolutionary step, not a revolution \u2014 anyone already running a <strong>4080 Super<\/strong> should keep it. And both buyers should weigh the same hard truth: 16 GB is the real constraint here, and no amount of Blackwell polish changes that ceiling.<\/p>\n<p><!--related-block--><\/p>\n<div class=\"convly-related\">\n<h2><span class=\"ez-toc-section\" id=\"Related_articles\"><\/span>Artigos relacionados<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li><a href=\"https:\/\/convly.ai\/pt\/rx-7900-xtx-vs-rtx-4090-for-ai\/\">AMD RX 7900 XTX versus RTX 4090 para IA em 2026: O ROCm consegue competir?<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/pt\/rtx-5070-ti-vs-rtx-4070-ti-super-for-ai\/\">RTX 5070 Ti versus RTX 4070 Ti Super para IA em 2026: Confronto na faixa intermedi\u00e1ria<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/pt\/rtx-4090-vs-rtx-3090-for-ai\/\">RTX 4090 versus RTX 3090 para IA em 2026: Vale a pena fazer a atualiza\u00e7\u00e3o?<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/pt\/rtx-4060-ti-16gb-vs-rtx-3060-12gb-for-ai\/\">RTX 4060 Ti 16GB vs RTX 3060 12GB for AI in 2026: Best Budget GPU?<\/a><\/li>\n<\/ul>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>The RTX 5080 and RTX 4080 Super both ship 16 GB of VRAM at a $999 price. The difference is Blackwell&#8217;s GDDR7 bandwidth and FP4 \u2014 here&#8217;s whether it matters for AI.<\/p>","protected":false},"author":1,"featured_media":671,"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":[246],"tags":[281,284,256,356,326,351],"class_list":["post-659","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-comparisons","tag-ai-gpu","tag-blackwell","tag-local-llm","tag-rtx-4080-super","tag-rtx-5080","tag-stable-diffusion"],"_links":{"self":[{"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/posts\/659","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/comments?post=659"}],"version-history":[{"count":2,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/posts\/659\/revisions"}],"predecessor-version":[{"id":983,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/posts\/659\/revisions\/983"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/media\/671"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/media?parent=659"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/categories?post=659"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/convly.ai\/pt\/wp-json\/wp\/v2\/tags?post=659"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}