{"id":802,"date":"2026-06-06T02:13:45","date_gmt":"2026-06-06T02:13:45","guid":{"rendered":"https:\/\/convly.ai\/rtx-5070-vs-rtx-5080-for-ai-2026\/"},"modified":"2026-06-06T02:13:45","modified_gmt":"2026-06-06T02:13:45","slug":"rtx-5070-vs-rtx-5080-for-ai-2026","status":"publish","type":"post","link":"https:\/\/convly.ai\/fr\/rtx-5070-vs-rtx-5080-for-ai-2026\/","title":{"rendered":"RTX 5070 vs RTX 5080 for AI in 2026: Is the Jump to 16GB Worth $450?"},"content":{"rendered":"<p>The RTX 5070 and RTX 5080 sit two tiers apart in price \u2014 $549 versus $999 \u2014 and for AI the gap is wider than a single step. You&#8217;re paying not just for more VRAM (16GB vs 12GB) but for nearly double the AI compute. The question is whether your workload actually uses it. Here&#8217;s the breakdown for local LLMs and image generation in 2026.<\/p>\n<div class=\"convly-tldr\">\n<h3>Principaux enseignements<\/h3>\n<ul>\n<li><strong>RTX 5070:<\/strong> 12GB GDDR7, 672 GB\/s, 988 AI TOPS, $549.<\/li>\n<li><strong>RTX 5080:<\/strong> 16GB GDDR7, 960 GB\/s, ~1,801 AI TOPS, $999 \u2014 roughly 1.8\u00d7 the compute and 4GB more VRAM.<\/li>\n<li><strong>For local LLMs:<\/strong> the 5080&#8217;s 16GB runs models the 12GB 5070 can&#8217;t; for models that fit both, it&#8217;s faster but not transformative.<\/li>\n<li><strong>For Stable Diffusion \/ heavy batches:<\/strong> the 5080&#8217;s compute lead is most visible here.<\/li>\n<li><strong>Verdict:<\/strong> serious AI \u2192 5080; budget AI or gaming-first \u2192 5070. The middle ground is the 5070 Ti.<\/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-6a23c752560fc\" 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-6a23c752560fc\"  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\/rtx-5070-vs-rtx-5080-for-ai-2026\/#Specs_side_by_side\" >Specs side by side<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/convly.ai\/fr\/rtx-5070-vs-rtx-5080-for-ai-2026\/#Local_LLMs_capacity_first_speed_second\" >Local LLMs: capacity first, speed second<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/convly.ai\/fr\/rtx-5070-vs-rtx-5080-for-ai-2026\/#Stable_Diffusion_and_training\" >Stable Diffusion and training<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/convly.ai\/fr\/rtx-5070-vs-rtx-5080-for-ai-2026\/#The_honest_value_call\" >The honest value call<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/convly.ai\/fr\/rtx-5070-vs-rtx-5080-for-ai-2026\/#FAQ\" >FAQ<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/convly.ai\/fr\/rtx-5070-vs-rtx-5080-for-ai-2026\/#Bottom_line\" >R\u00e9sultat<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"Specs_side_by_side\"><\/span>Specs side by side<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<table class=\"convly-vs\">\n<thead>\n<tr>\n<th>Spec<\/th>\n<th>RTX 5070<\/th>\n<th>RTX 5080<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>VRAM<\/td>\n<td>12GB GDDR7<\/td>\n<td>16GB GDDR7<\/td>\n<\/tr>\n<tr>\n<td>Memory bus<\/td>\n<td>192-bit<\/td>\n<td>256-bit<\/td>\n<\/tr>\n<tr>\n<td>Bandwidth<\/td>\n<td>672 GB\/s<\/td>\n<td>960 GB\/s<\/td>\n<\/tr>\n<tr>\n<td>C\u0153urs CUDA<\/td>\n<td>6,144<\/td>\n<td>10,752<\/td>\n<\/tr>\n<tr>\n<td>AI TOPS<\/td>\n<td>988<\/td>\n<td>~1,801<\/td>\n<\/tr>\n<tr>\n<td>MSRP<\/td>\n<td>$549<\/td>\n<td>$999<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>The 5080 brings about 75% more CUDA cores, 43% more bandwidth, nearly double the AI TOPS, and the all-important step from 12GB to 16GB of VRAM.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Local_LLMs_capacity_first_speed_second\"><\/span>Local LLMs: capacity first, speed second<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>As always with local LLMs, memory sets the ceiling before compute sets the speed. The 5080&#8217;s 16GB matches the RTX 5070 Ti and RTX 5060 Ti 16GB \u2014 meaning it runs the same broader set of models (up to ~14B comfortably, larger quants with usable context) that the 12GB 5070 can&#8217;t fully hold.<\/p>\n<p>For models that <em>do<\/em> fit on both cards, the 5080&#8217;s extra bandwidth makes generation faster, but local single-user inference is bandwidth-bound, so the gain is real rather than dramatic. The bigger practical difference is simply <em>which<\/em> models you can run. To see where your target models land, use our <a href=\"https:\/\/convly.ai\/fr\/vram-requirements-every-major-llm-2026\/\">VRAM requirements guide<\/a>.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Stable_Diffusion_and_training\"><\/span>Stable Diffusion and training<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>This is where the 5080&#8217;s compute earns its price. In image generation and any light fine-tuning, the ~1.8\u00d7 TOPS advantage translates into noticeably faster iterations and bigger batches. If you generate images at volume, train LoRAs, or do diffusion-heavy work, the 5080 pulls clearly ahead \u2014 far more than it does in token-by-token LLM chat.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"The_honest_value_call\"><\/span>The honest value call<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>At $999, the RTX 5080 is nearly twice the price of the $549 RTX 5070. For pure LLM chat where a model fits both, that&#8217;s a lot to pay for a moderate speed bump. But for serious, mixed AI work \u2014 image generation, larger models, occasional fine-tuning \u2014 the 5080 is the more capable tool and the 16GB future-proofs you against the 12GB wall.<\/p>\n<p>If $999 is too much but the 12GB 5070 feels tight, the sweet spot is the <a href=\"https:\/\/convly.ai\/fr\/rtx-5070-vs-rtx-5070-ti-for-ai-2026\/\">RTX 5070 Ti<\/a> \u2014 16GB at $749. And if you&#8217;re comparing the 5080 against its closer rival, see <a href=\"https:\/\/convly.ai\/fr\/rtx-5080-vs-rtx-5070-ti-ai-value\/\">RTX 5080 vs 5070 Ti<\/a>. For the full picture, our <a href=\"https:\/\/convly.ai\/fr\/best-gpus-for-local-llms-2026\/\">les meilleurs GPU pour les LLM locaux<\/a> ranks them all.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"FAQ\"><\/span>FAQ<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3>Is the RTX 5080 worth almost double the RTX 5070 for AI?<\/h3>\n<p>For serious or mixed AI work \u2014 Stable Diffusion, larger local LLMs, light fine-tuning \u2014 yes, the 5080&#8217;s 16GB and ~1.8\u00d7 compute justify the price. For light LLM chat where the model already fits in 12GB, the cheaper 5070 delivers most of the experience for far less.<\/p>\n<h3>How much VRAM difference is there?<\/h3>\n<p>The RTX 5080 has 16GB versus the RTX 5070&#8217;s 12GB \u2014 a 4GB gap that lets the 5080 run 13\u201314B models and longer contexts the 5070 can&#8217;t hold. For AI, that capacity difference usually matters more than raw speed.<\/p>\n<h3>Should I get the RTX 5070 Ti instead?<\/h3>\n<p>Often, yes. The 5070 Ti gives you the 5080&#8217;s 16GB capacity at $749 \u2014 splitting the difference between the 5070 and 5080. If your goal is to clear the 12GB wall without paying $999, the 5070 Ti is the value sweet spot.<\/p>\n<h3>Which is better for Stable Diffusion?<\/h3>\n<p>The RTX 5080, clearly. Its ~1,801 AI TOPS versus the 5070&#8217;s 988 makes a real difference in image-generation speed and batch size \u2014 diffusion is exactly the workload where the 5080&#8217;s extra compute shows up most.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Bottom_line\"><\/span>R\u00e9sultat<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The RTX 5080 is the better AI card on every axis \u2014 more VRAM, more bandwidth, far more compute \u2014 but at nearly double the price, it&#8217;s only worth it if your workload uses that power. For image generation, larger models, and future-proofing, buy the 5080. For budget LLM work, the 5070 is enough. And if you just need to escape 12GB affordably, the 5070 Ti is the answer to both.<\/p>","protected":false},"excerpt":{"rendered":"<p>$450 separates these two, and for AI it buys both more VRAM and almost double the compute. Here&#8217;s whether the RTX 5080 justifies the gap over the 5070 for local AI work.<\/p>","protected":false},"author":1,"featured_media":809,"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":[281,659,657,662,326,661],"class_list":["post-802","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-gpus","tag-ai-gpu","tag-local-llm-gpu","tag-rtx-5070","tag-rtx-5070-vs-5080","tag-rtx-5080","tag-stable-diffusion-gpu"],"_links":{"self":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts\/802","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=802"}],"version-history":[{"count":0,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts\/802\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/media\/809"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/media?parent=802"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/categories?post=802"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/tags?post=802"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}