{"id":367,"date":"2026-05-29T18:01:40","date_gmt":"2026-05-29T18:01:40","guid":{"rendered":"https:\/\/convly.ai\/?p=367"},"modified":"2026-06-10T05:04:55","modified_gmt":"2026-06-10T05:04:55","slug":"best-gpus-for-video-generation-2026","status":"publish","type":"post","link":"https:\/\/convly.ai\/it\/best-gpus-for-video-generation-2026\/","title":{"rendered":"The Best GPUs for AI Video Generation in 2026"},"content":{"rendered":"<p>Generating video with open models like Hunyuan Video and Wan, on your own machine, is one of the most demanding things you can ask a consumer GPU to do. Video isn&#8217;t a single image \u2014 it&#8217;s many frames that must stay coherent, and that multiplies the memory and compute needed. If image generation is a sprint, local video generation is a mountain climb.<\/p>\n<p>This guide ranks the GPUs that can genuinely handle local AI video generation in 2026 \u2014 and is honest about what it takes.<\/p>\n<div class=\"convly-tldr\">\n<h3>Punti chiave<\/h3>\n<ul>\n<li><strong>Migliore in assoluto:<\/strong> RTX 5090 (32 GB) \u2014 the only consumer card with real headroom for video.<\/li>\n<li><strong>Minimum viable:<\/strong> 24 GB \u2014 a used RTX 3090 or an RTX 4090.<\/li>\n<li><strong>VRAM is everything<\/strong> \u2014 video generation is the most memory-hungry creative workload.<\/li>\n<li><strong>Below 24 GB,<\/strong> expect short, low-resolution clips and heavy compromises.<\/li>\n<li><strong>For occasional use,<\/strong> cloud GPUs are a serious alternative to buying a flagship.<\/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-6a38ae9697133\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Attiva\/Disattiva<\/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-6a38ae9697133\"  aria-label=\"Attiva\/Disattiva\" \/><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\/it\/best-gpus-for-video-generation-2026\/#Why_video_generation_is_so_demanding\" >Why video generation is so demanding<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/convly.ai\/it\/best-gpus-for-video-generation-2026\/#How_much_VRAM_do_you_need\" >How much VRAM do you need?<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/convly.ai\/it\/best-gpus-for-video-generation-2026\/#The_rankings\" >The rankings<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/convly.ai\/it\/best-gpus-for-video-generation-2026\/#Buy_or_rent\" >Buy or rent?<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/convly.ai\/it\/best-gpus-for-video-generation-2026\/#Match_the_GPU_to_the_model_youll_actually_run\" >Match the GPU to the model you&#8217;ll actually run<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/convly.ai\/it\/best-gpus-for-video-generation-2026\/#FAQ\" >Domande frequenti<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/convly.ai\/it\/best-gpus-for-video-generation-2026\/#Bottom_line\" >Conclusione<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/convly.ai\/it\/best-gpus-for-video-generation-2026\/#Related_articles\" >Articoli correlati<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"Why_video_generation_is_so_demanding\"><\/span>Why video generation is so demanding<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>A video model has to generate and keep consistent a whole sequence of frames at once. That makes it dramatically heavier than image generation on every axis:<\/p>\n<ul>\n<li><strong>VRAM<\/strong> \u2014 holding many frames plus a large model needs far more memory than a single image. This is the hard wall.<\/li>\n<li><strong>Compute<\/strong> \u2014 every clip is many frames of work, so generation is slow even on fast cards.<\/li>\n<li><strong>Time<\/strong> \u2014 a few seconds of video can take minutes to generate locally.<\/li>\n<\/ul>\n<p>There&#8217;s no clever way around the memory wall. For local video generation, VRAM isn&#8217;t just the most important spec \u2014 it&#8217;s the one that decides whether you can run a model at all.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"How_much_VRAM_do_you_need\"><\/span>How much VRAM do you need?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<table class=\"convly-vs\">\n<thead>\n<tr>\n<th>VRAM<\/th>\n<th>Local video generation experience<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>16 GB<\/td>\n<td>Very limited \u2014 short, low-res clips, heavy optimization, smaller models only<\/td>\n<\/tr>\n<tr>\n<td>24 GB<\/td>\n<td>Minimum viable \u2014 usable clips with care and optimized workflows<\/td>\n<\/tr>\n<tr>\n<td>32 GB<\/td>\n<td>Comfortable \u2014 the realistic target for a good local experience<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>The conclusion is blunt: <strong>24 GB is the floor, and 32 GB is what you actually want.<\/strong> Below 24 GB, local video generation is more of a frustrating experiment than a workflow.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"The_rankings\"><\/span>The rankings<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3>1. RTX 5090 \u2014 the clear winner<\/h3>\n<p>For local AI video generation, the RTX 5090 isn&#8217;t just the best option \u2014 it&#8217;s close to the only fully comfortable one. Its <strong>32 GB of GDDR7<\/strong> provides the memory headroom that video models demand, and its Blackwell compute meaningfully shortens the long generation times. If you&#8217;re serious about generating video locally, this is the card to build around. There isn&#8217;t a close second in the consumer range.<\/p>\n<h3>2. RTX 4090 \u2014 strong, if you can get one well-priced<\/h3>\n<p>The RTX 4090&#8217;s <strong>24 GB<\/strong> hits the minimum viable bar, and its compute is excellent. With optimized workflows it handles local video generation, just with less headroom than a 5090 \u2014 expect to manage clip length and resolution more carefully. New stock is limited and pricing varies, so judge it on the deal available.<\/p>\n<h3>3. Used RTX 3090 \u2014 the value route to 24 GB<\/h3>\n<p>A used RTX 3090 is the cheapest way onto the <strong>24 GB<\/strong> tier, for roughly $700\u2013900. It&#8217;s slower than a 4090 or 5090, so generation times are longer, but it has the memory to run the models. For someone who wants to do local video generation on a budget and accepts the wait, it&#8217;s the value pick.<\/p>\n<h3>4. 16 GB cards (RTX 5080 \/ 5070 Ti) \u2014 not recommended for video<\/h3>\n<p>The 16 GB cards are excellent for many AI tasks, but local video generation isn&#8217;t one of them. 16 GB forces small models, short and low-resolution clips, and constant memory juggling. They can technically do it; they can&#8217;t do it well. If video generation is your goal, don&#8217;t stop at 16 GB.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Buy_or_rent\"><\/span>Buy or rent?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>This is a genuine decision for video generation. A 32 GB GPU is a major purchase, and local video generation is slow even on the best card. If you generate video only occasionally, <strong>renting a cloud GPU<\/strong> for those sessions can be far cheaper and faster than buying a flagship \u2014 you get access to powerful hardware only when you need it.<\/p>\n<p>Buy the GPU if you generate video frequently, want full privacy, or also run other heavy AI workloads that justify a 5090. Rent if it&#8217;s an occasional creative experiment.<\/p>\n<p><!--ai-enriched--><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Match_the_GPU_to_the_model_youll_actually_run\"><\/span>Match the GPU to the model you&#8217;ll actually run<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Raw VRAM is only half the decision. The other half is <strong>which open-source video model you plan to run<\/strong>, because each one has a very different appetite \u2014 and modern quantization quietly rewrites the requirements. At full precision, the leading 2026 models are brutal: Wan 2.2&#8217;s 14B transformer wants 60 GB or more and Tencent&#8217;s original HunyuanVideo needs roughly 50 GB, firmly datacenter territory. But almost nobody runs them that way anymore. With GGUF or FP8 quantization plus text-encoder offloading, the same models drop onto consumer cards \u2014 and that is what reframes everything in this article&#8217;s rankings.<\/p>\n<p>The single biggest trick is offloading the T5 text encoder (roughly 10 GB of weights) to system RAM, then compressing the remaining diffusion weights. That alone takes Wan 14B from unrunnable to viable on a 24 GB card, and quantized GGUF builds can squeeze a 480p workflow into far less. Here is the practical mapping for 2026:<\/p>\n<ul>\n<li><strong>Wan 2.2<\/strong> \u2014 the most-deployed open model. The 1.3B lite variant runs on 8 GB; the 5B TI2V build sits around 8\u201312 GB; the full 14B needs FP8 or GGUF to fit comfortably in 16\u201324 GB, and aggressive GGUF plus offloading can push a 480p workflow onto cards as small as 8 GB.<\/li>\n<li><strong>HunyuanVideo<\/strong> \u2014 FP8 quantization brings it onto a 24 GB card with a modest quality trade-off; the distilled 1.5 line plus offloading reaches further down, fitting 16 GB cards.<\/li>\n<li><strong>LTX-Video \/ LTX-2<\/strong> \u2014 fast and the only major open model with native audio-plus-video in a single pass, but it effectively wants 24 GB even with FP8 at 720p.<\/li>\n<li><strong>CogVideoX-5B<\/strong> \u2014 the friendliest to smaller cards; 8-bit quantization lands it near 16 GB.<\/li>\n<\/ul>\n<p>This is why <strong>24 GB is the community consensus sweet spot<\/strong>, and why a used RTX 3090 punches so far above its price here. At 24 GB, every major open video model runs with optimization, and output quality holds up for social clips and B-roll. Drop to 16 GB and your options narrow to CogVideoX and heavily quantized lite variants \u2014 workable, but you trade resolution, clip length, and stability. The lesson: before you buy, pick your model first, confirm its quantized VRAM footprint, then size the card to it. The hardware is the means; the model is the constraint.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"FAQ\"><\/span>Domande frequenti<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3>What is the best GPU for AI video generation in 2026?<\/h3>\n<p>The RTX 5090, with 32 GB of VRAM, is the best and most comfortable GPU for local AI video generation. The RTX 4090 and a used RTX 3090 (both 24 GB) are the minimum viable options. Video generation is so memory-hungry that the 5090 stands well ahead of everything else.<\/p>\n<h3>How much VRAM do I need for AI video generation?<\/h3>\n<p>24 GB is the realistic minimum for usable local video generation, and 32 GB is the comfortable target. With less than 24 GB you&#8217;re limited to short, low-resolution clips and constant optimization. VRAM is the spec that decides what you can run.<\/p>\n<h3>Why does AI video generation need so much VRAM?<\/h3>\n<p>A video model generates many frames at once and must keep them coherent, which requires holding far more data in memory than a single image. Combined with a large model, this makes video generation the most VRAM-hungry consumer AI workload.<\/p>\n<h3>Can I generate AI video on a 16 GB GPU?<\/h3>\n<p>Only with heavy compromises \u2014 small models, short clips, low resolution, and constant memory management. 16 GB cards are great for many AI tasks, but local video generation realistically needs 24 GB or more to be a workable experience.<\/p>\n<h3>Should I buy a GPU or use the cloud for AI video?<\/h3>\n<p>If you generate video only occasionally, renting a cloud GPU is often cheaper and faster than buying a 32 GB card. Buy your own GPU if you generate video frequently, need privacy, or run other heavy AI workloads that justify a flagship card.<\/p>\n<h3>Which open-source video models run best on a consumer GPU?<\/h3>\n<p>On a 24 GB card (RTX 3090, 4090, or 5090), Wan 2.2, HunyuanVideo, LTX-Video, and CogVideoX all run with quantization \u2014 Wan 2.2 is the most popular for its quality-to-effort ratio. If you only have 16 GB, CogVideoX-5B with 8-bit quantization is the most reliable choice, alongside lite variants like Wan&#8217;s 1.3B and 5B models. Tools such as ComfyUI (with the GGUF nodes) and Wan2GP are built specifically to make these models fit smaller cards.<\/p>\n<h3>Does quantizing a video model hurt quality?<\/h3>\n<p>Less than you would expect. FP8 is nearly indistinguishable from full precision for most clips, and 8-bit GGUF builds are close enough that the difference rarely shows in social-media or B-roll output. Aggressive low-bit quantization on very small VRAM can soften fine detail and motion coherence, but for the 24 GB tier the trade-off is minor \u2014 quantization is how the entire consumer video scene operates in 2026, not a compromise reserved for weak hardware.<\/p>\n<h3>How long does it take to generate a clip locally?<\/h3>\n<p>Expect minutes, not seconds. A single short image-to-video clip on a high-end consumer card typically lands in the multi-minute range, and longer or higher-resolution jobs scale up from there. In one real-world Wan image-to-video benchmark, the same job ran in roughly 12.7 minutes on an RTX 4090 versus about 7 minutes on an RTX 5090 \u2014 the 5090 is around 45% faster. Quantization, lower resolution, and fewer frames all shorten the wait, but local video generation is an iterative, batch-it-and-walk-away workflow.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Bottom_line\"><\/span>Conclusione<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Local AI video generation is the most demanding consumer AI workload there is, and the hardware reality is simple: <strong>the RTX 5090 and its 32 GB of VRAM<\/strong> is the card to build around. The RTX 4090 and a used RTX 3090 hit the 24 GB minimum and will work with care, but 16 GB cards aren&#8217;t suited to it.<\/p>\n<p>Before buying a flagship, weigh the cloud honestly \u2014 for occasional video work, renting powerful hardware on demand may serve you better than owning it. But if local, private, frequent video generation is the goal, the RTX 5090 is the answer.<\/p>\n<p><!--related-block--><\/p>\n<div class=\"convly-related\">\n<h2><span class=\"ez-toc-section\" id=\"Related_articles\"><\/span>Articoli correlati<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li><a href=\"https:\/\/convly.ai\/it\/rtx-pro-6000-vs-rtx-5090-for-ai-2026\/\">RTX Pro 6000 Blackwell contro RTX 5090 per l\u2019IA nel 2026: quando giustifica un sovrapprezzo di 5.500 dollari avere 96 GB di VRAM?<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/it\/rtx-5070-vs-rtx-5080-for-ai-2026\/\">RTX 5070 contro RTX 5080 per l\u2019IA nel 2026: vale la pena pagare 450 dollari in pi\u00f9 per passare a 16 GB?<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/it\/best-gpus-for-llm-fine-tuning-2026\/\">Le migliori GPU per il fine-tuning di LLM a casa nel 2026<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/it\/best-gpus-for-budget-builds-2026\/\">Le migliori GPU per una workstation AI economica sotto i 1500 dollari nel 2026<\/a><\/li>\n<\/ul>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>Local AI video generation is the most VRAM-hungry creative workload there is. This guide ranks the GPUs that can actually run models like Hunyuan and Wan at home.<\/p>","protected":false},"author":1,"featured_media":543,"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":[550,551,553,251,552],"class_list":["post-367","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-gpus","tag-ai-video-generation-gpu","tag-hunyuan-video","tag-local-video-ai","tag-rtx-5090","tag-wan-video"],"_links":{"self":[{"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/posts\/367","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/comments?post=367"}],"version-history":[{"count":3,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/posts\/367\/revisions"}],"predecessor-version":[{"id":968,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/posts\/367\/revisions\/968"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/media\/543"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/media?parent=367"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/categories?post=367"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/tags?post=367"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}