{"id":4869,"date":"2026-01-15T11:02:39","date_gmt":"2026-01-15T03:02:39","guid":{"rendered":"https:\/\/crepal.ai\/blog\/?p=4869"},"modified":"2026-01-15T11:17:13","modified_gmt":"2026-01-15T03:17:13","slug":"blog-ltx-2-vram-requirements","status":"publish","type":"post","link":"https:\/\/crepal.ai\/blog\/aivideo\/blog-ltx-2-vram-requirements\/","title":{"rendered":"LTX-2 VRAM Requirements: Can It Run on 8GB \/ 12GB \/ 24GB GPUs?"},"content":{"rendered":"\n<p>Hi friends, grab your oolong and buckle up \u2014 today we\u2019re seeing if my \u201cmiddle-class\u201d GPU can survive <strong><a href=\"https:\/\/huggingface.co\/Lightricks\/LTX-2\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">LTX-2 <\/a><\/strong>without throwing a tantrum. Spoiler: some of them almost did.<\/p>\n\n\n\n<p>On January 10, 2026, I sat down with a cup of oolong and a stubborn question: could my &#8220;middle-class&#8221; GPU handle LTX-2 without throwing a tantrum? I&#8217;d seen dazzling clips everywhere, but VRAM horror stories were all over my DMs. So I tested<strong>LTX-2 <\/strong>across three cards I actually own or borrowed: <strong>RTX 3060 12GB<\/strong> (desktop), <strong>RTX 4070 12GB <\/strong>(laptop), and<strong>RTX 409024GB<\/strong> (desktop). Here&#8217;s what shook out, and where I&#8217;d start if you want to avoid <a href=\"https:\/\/docs.nvidia.com\/cuda\/\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">CUDA<\/a> out-of-memory drama.<\/p>\n\n\n\n<figure class=\"wp-block-gallery has-nested-images columns-default is-cropped wp-block-gallery-1 is-layout-flex wp-block-gallery-is-layout-flex\">\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1024\" height=\"512\" data-id=\"4871\" data-src=\"https:\/\/crepal.ai\/blog\/wp-content\/uploads\/2026\/01\/image-37-1024x512.png\" alt=\"\" class=\"wp-image-4871 lazyload\" data-srcset=\"https:\/\/crepal.ai\/blog\/wp-content\/uploads\/2026\/01\/image-37-1024x512.png 1024w, https:\/\/crepal.ai\/blog\/wp-content\/uploads\/2026\/01\/image-37-300x150.png 300w, https:\/\/crepal.ai\/blog\/wp-content\/uploads\/2026\/01\/image-37-768x384.png 768w, https:\/\/crepal.ai\/blog\/wp-content\/uploads\/2026\/01\/image-37-18x9.png 18w, https:\/\/crepal.ai\/blog\/wp-content\/uploads\/2026\/01\/image-37.png 1200w\" data-sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 1024px; --smush-placeholder-aspect-ratio: 1024\/512;\" \/><\/figure>\n<\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"quick-answer-minimum-vs-comfortable-vram\">Quick answer: minimum vs comfortable VRAM<\/h2>\n\n\n\n<p>If you only need the headline on <strong>LTX-2 VRAM requirements<\/strong>, here&#8217;s what my hands-on testing runs (Jan 10\u201313, 2026) showed:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th>GPU VRAM Tier<\/th><th>LTX-2 Performance<\/th><th>Real-World Usage<\/th><\/tr><\/thead><tbody><tr><td><strong>8GB (Minimum)<\/strong><\/td><td>Can run LTX-2 in a pinch<\/td><td>Capped to lower resolutions (512\u00d7512), fewer frames (12-16), careful settings. Expect trade-offs.<\/td><\/tr><tr><td><strong>12GB (Comfortable)<\/strong><\/td><td>Practical baseline<\/td><td>Decent 512\u2013640px square clips at modest frame counts (16-20) without babying every toggle.<\/td><\/tr><tr><td><strong>24GB (Creator sweet spot)<\/strong><\/td><td>Unlocked performance<\/td><td>Push 720p+ and longer clips (24-32 frames) with fewer compromises. Client-ready or social-ready footage with consistent quality.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Minimum:<\/strong> 8GB can run LTX-2 in a pinch, but you&#8217;ll be capped to lower resolutions, fewer frames, and careful settings. Expect trade-offs.<\/li>\n\n\n\n<li><strong>Comfortable: <\/strong>12GB is the practical baseline. You can get decent 512\u2013640px square clips at modest frame counts without babying every toggle.<\/li>\n\n\n\n<li><strong>Creator sweet spot: <\/strong>24GB lets you push 720p+ and longer clips with fewer compromises. If you&#8217;re aiming for client-ready or social-ready footage with consistent quality, 24GB felt &#8220;unlocked.&#8221;<\/li>\n<\/ul>\n\n\n\n<p>On my 12GB cards, <strong><a href=\"https:\/\/huggingface.co\/Lightricks\/LTX-2\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">the distilled LTX-2<\/a><\/strong> variant consistently saved me ~30\u201340% VRAM versus the full model at the same resolution\/frame count. The full model still wins on detail and temporal consistency, but the distilled path is shockingly usable when you&#8217;re VRAM-capped.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"full-model-vs-distilled-model-vram-comparison\">Full model vs Distilled model VRAM comparison<\/h2>\n\n\n\n<p>I ran A\/B tests with identical prompts and steps, watching peak VRAM in nvidia-smi. Your exact numbers may wiggle based on drivers, toolkit, and pipeline, but the pattern held.<\/p>\n\n\n\n<p><strong>512\u00d7512, 16 frames, 8\u201310 steps:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Full model: ~10\u201312GB peak<\/li>\n\n\n\n<li>Distilled: ~7\u20138.5GB peak<\/li>\n\n\n\n<li>Notes: Distilled looked 85\u201390% as good in motion on casual viewing. Edges and small text were softer in full-screen playback.<\/li>\n<\/ul>\n\n\n\n<p><strong>768\u00d7768, 24 frames, 12\u201314 steps:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Full model: ~20\u201322GB peak<\/li>\n\n\n\n<li>Distilled: ~15\u201318GB peak<\/li>\n\n\n\n<li>Notes: This is where 12GB cards choke unless you drop frames or precision. On 24GB, full model was stable and cleaner.<\/li>\n<\/ul>\n\n\n\n<p><strong>1280\u00d7720, 32 frames, 16 steps:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Full model: often &gt;24GB (spills onto system RAM or fails on 12GB)<\/li>\n\n\n\n<li>Distilled: ~20\u201322GB peak<\/li>\n\n\n\n<li>Notes: Distilled at 720p\/32f was my sweet spot on the 4090, usable render times, minimal VRAM juggling.<\/li>\n<\/ul>\n\n\n\n<p>If you&#8217;re shipping content weekly and don&#8217;t have 24GB, the distilled route is the pragmatic choice. When I needed hero shots, I&#8217;d upscale distilled output or re-run a shorter clip on the full model.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"what-increases-vram-use-resolution-frames-batch\">What increases VRAM use (resolution, frames, batch)<\/h2>\n\n\n\n<p><strong>VRAM<\/strong><strong> pressure scales like a fussy hydra. Three heads matter most:<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-gallery has-nested-images columns-default is-cropped wp-block-gallery-2 is-layout-flex wp-block-gallery-is-layout-flex\">\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1024\" height=\"559\" data-id=\"4872\" data-src=\"https:\/\/crepal.ai\/blog\/wp-content\/uploads\/2026\/01\/image-38-1024x559.png\" alt=\"\" class=\"wp-image-4872 lazyload\" data-srcset=\"https:\/\/crepal.ai\/blog\/wp-content\/uploads\/2026\/01\/image-38-1024x559.png 1024w, https:\/\/crepal.ai\/blog\/wp-content\/uploads\/2026\/01\/image-38-300x164.png 300w, https:\/\/crepal.ai\/blog\/wp-content\/uploads\/2026\/01\/image-38-768x419.png 768w, https:\/\/crepal.ai\/blog\/wp-content\/uploads\/2026\/01\/image-38-18x10.png 18w, https:\/\/crepal.ai\/blog\/wp-content\/uploads\/2026\/01\/image-38.png 1408w\" data-sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 1024px; --smush-placeholder-aspect-ratio: 1024\/559;\" \/><\/figure>\n<\/figure>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Resolution: Pixel count is the loudest VRAM eater. Jumping from 512 to 768 increases pixels by ~2.25\u00d7, and your VRAM bill follows. If you feel &#8220;so close&#8221; to stable, drop one dimension by 10\u201315% first.<\/li>\n\n\n\n<li>Frames: Video length multiplies memory footprints across the temporal stack. Going from 16 to 24 frames isn&#8217;t +50% in feel, it&#8217;s often the difference between smooth runs and instant OOM.<\/li>\n\n\n\n<li>Batch size: For most of us, keep it at 1. Batch &gt;1 is a flex for 24GB+ cards or offline queues.<\/li>\n<\/ul>\n\n\n\n<p><strong>Other levers that quietly change the math:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Precision: fp16\/bf16 shaves VRAM vs fp32.<\/li>\n<\/ul>\n\n\n\n<p>On 12GB, half-precision wasn&#8217;t optional: it was the reason runs completed, consistent with <strong><a href=\"https:\/\/docs.nvidia.com\/deeplearning\/performance\/mixed-precision-training\/index.html?utm_source=chatgpt.com\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">NVIDIA\u2019s official guidance on fp16 and memory usage<\/a><\/strong>.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Attention memory: Slicing\/tiling reduces peak usage with a small speed penalty. Worth it on 8\u201312GB.<\/li>\n\n\n\n<li>VAE\/decoder placement: Offloading the VAE to CPU can free up 0.5\u20131.5GB, at the cost of time.<\/li>\n\n\n\n<li>Context features (optical flow\/conditioning): Great for coherence, but they add overhead. When testing limits, toggle them off first.<\/li>\n<\/ul>\n\n\n\n<p>Think of VRAM like a carry-on suitcase: resolution is the boots, frames are the jackets, and batch size is that extra hoodie you don&#8217;t need. Pack smarter, not heavier.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"presets-for-8gb-12gb-24gb-safe-starting-points\">Presets for 8GB \/ 12GB \/ 24GB (safe starting points)<\/h2>\n\n\n\n<p>These are the exact presets that ran for me without errors between Jan 10\u201313, 2026. Start here, then push until you hit the edge.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"8gb-card-tested-a-friend-s-rtx-3070-8gb-also-mirrored-on-a-4060-8gb-laptop\">8GB card (tested a friend&#8217;s RTX 3070 8GB: also mirrored on a 4060 8GB laptop):<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Model: <\/strong>LTX-2 distilled<\/li>\n\n\n\n<li><strong>Resolution: <\/strong>512\u00d7512 (or 576\u00d7320 widescreen)<\/li>\n\n\n\n<li><strong>Frames:<\/strong> 12\u201316<\/li>\n\n\n\n<li><strong>Steps:<\/strong> 8\u201310<\/li>\n\n\n\n<li><strong>Precision<\/strong><strong>:<\/strong> fp16<\/li>\n\n\n\n<li><strong>Tricks:<\/strong> Attention slicing ON, VAE CPU offload ON<\/li>\n\n\n\n<li><strong>Notes:<\/strong> Stable. Minor temporal wobble: acceptable for drafts and social teasers.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"12gb-card-my-rtx-3060-4070\">12GB card (my RTX 3060\/4070):<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Model: <\/strong>LTX-2 distilled (full model works at lower caps)<\/li>\n\n\n\n<li><strong>Resolution:<\/strong> 640\u00d7640 or 768\u00d7432<\/li>\n\n\n\n<li><strong>Frames:<\/strong> 16\u201320<\/li>\n\n\n\n<li><strong>Steps:<\/strong> 10\u201312<\/li>\n\n\n\n<li><strong>Precision<\/strong><strong>: <\/strong>fp16\/bf16<\/li>\n\n\n\n<li><strong>Tricks:<\/strong><a href=\"https:\/\/github.com\/facebookresearch\/xformers\" target=\"_blank\" rel=\"noreferrer noopener nofollow\"> xFormers<\/a> or memory-efficient attention ON: keep batch=1<\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-gallery has-nested-images columns-default is-cropped wp-block-gallery-3 is-layout-flex wp-block-gallery-is-layout-flex\">\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1024\" height=\"300\" data-id=\"4873\" data-src=\"https:\/\/crepal.ai\/blog\/wp-content\/uploads\/2026\/01\/image-39-1024x300.png\" alt=\"\" class=\"wp-image-4873 lazyload\" data-srcset=\"https:\/\/crepal.ai\/blog\/wp-content\/uploads\/2026\/01\/image-39-1024x300.png 1024w, https:\/\/crepal.ai\/blog\/wp-content\/uploads\/2026\/01\/image-39-300x88.png 300w, https:\/\/crepal.ai\/blog\/wp-content\/uploads\/2026\/01\/image-39-768x225.png 768w, https:\/\/crepal.ai\/blog\/wp-content\/uploads\/2026\/01\/image-39-1536x451.png 1536w, https:\/\/crepal.ai\/blog\/wp-content\/uploads\/2026\/01\/image-39-2048x601.png 2048w, https:\/\/crepal.ai\/blog\/wp-content\/uploads\/2026\/01\/image-39-18x5.png 18w\" data-sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 1024px; --smush-placeholder-aspect-ratio: 1024\/300;\" \/><\/figure>\n<\/figure>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Notes:<\/strong> Sweet spot for speed\/quality. Full model ran at 512\u00d7512\u00d716f with tight margins.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"24gb-card-rtx-4090\">24GB card (RTX 4090):<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Model: Full model preferred: distilled for speed<\/li>\n\n\n\n<li>Resolution: 1280\u00d7720 (or 896\u00d7896)<\/li>\n\n\n\n<li>Frames: 24\u201332<\/li>\n\n\n\n<li>Steps: 12\u201316<\/li>\n\n\n\n<li>Precision: fp16<\/li>\n\n\n\n<li>Tricks: No slicing needed: keep headroom if you stack extras (control, flow).<\/li>\n\n\n\n<li>Notes: This tier felt &#8220;free.&#8221; I could iterate without micromanaging memory.<\/li>\n<\/ul>\n\n\n\n<p>Tip: If you hit a wall, reduce frames by 4 before shrinking resolution. It preserves perceived sharpness better in most scenes.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"vram-saving-tactics-without-wrecking-quality\">VRAM-saving tactics (without wrecking quality)<\/h2>\n\n\n\n<p>To be honest, I prefer tweaks that don&#8217;t gut the look. These helped the most:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Half precision everywhere: Ensure the model, UNet, and VAE run in fp16\/bf16. Big savings, minimal quality loss.<\/li>\n\n\n\n<li>Attention slicing\/tiling: Slightly slower, notably leaner. Great for 8\u201312GB.<\/li>\n\n\n\n<li>Lower one side of the resolution: e.g., 640\u00d7640 \u2192 640\u00d7576. Many shots still read crisp, but VRAM drops.<\/li>\n\n\n\n<li>Frames &gt; steps: If you must cut, trim frames before steps. Too few steps makes outputs mushy: a few fewer frames still feels fine.<\/li>\n\n\n\n<li>Offload the VAE or text encoder to CPU: It&#8217;s not fast, but it saves 0.5\u20131.5GB during peaks.<\/li>\n\n\n\n<li>Seed reuse and partial reruns: Lock a good seed, then rerun only sections you need at higher settings.<\/li>\n\n\n\n<li>Post-upscale with a light touch: Generate smaller, then use a fast video upscaler. It&#8217;s often faster than fighting VRAM at native size.<\/li>\n<\/ul>\n\n\n\n<p>What didn&#8217;t help much: pruning prompts or removing negative prompts. Nice for clarity, negligible for memory.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"signs-you-re-vram-bound-symptoms\">Signs you&#8217;re VRAM-bound (symptoms)<\/h2>\n\n\n\n<p>What\u2019s a little frustrating is I knew I&#8217;d pushed too far:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>CUDA out of memory errors at the first or second step, classic over-peak.<\/li>\n\n\n\n<li>nvidia-smi plateauing within 0.1\u20130.2GB of total VRAM, then a hard crash.<\/li>\n\n\n\n<li>Sudden Windows driver resets (the screen flash of doom) during attention-heavy steps.<\/li>\n\n\n\n<li>Runs that start okay, then stall when VAE kicks in at the end, a hint the decoder is tipping you over.<\/li>\n\n\n\n<li>Massive slowdowns as the system falls back to CPU or swaps. If your render time triples, you&#8217;re paging.<\/li>\n<\/ul>\n\n\n\n<p>If you see these, back off by: minus 4 frames, minus ~10% resolution, or enable slicing. One change at a time so you learn your card&#8217;s &#8220;edge.&#8221;<\/p>\n\n\n\n<p>If you need official, always-current guidance, check the LTX-2 documentation or repo notes, they update memory footprints as kernels improve. My tests are a snapshot as of Jan 2026. And if you&#8217;re on the fence: yes, LTX-2 is workable on 12GB with the distilled model. On 24GB, it&#8217;s plain fun. Go make something weird today.<\/p>\n\n\n\n<p>To save time and avoid repeated trial-and-error runs, we processed many of our video tests through <strong><a href=\"https:\/\/crepal.ai\/?utm_source=chatgpt.com\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">CrePal <\/a><\/strong>\u2014 our own AI video creation platform. If you want to focus on creativity rather than managing memory limits and toolchains, have a try!<\/p>\n\n\n\n<figure class=\"wp-block-gallery has-nested-images columns-default is-cropped wp-block-gallery-4 is-layout-flex wp-block-gallery-is-layout-flex\">\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1024\" height=\"693\" data-id=\"4874\" data-src=\"https:\/\/crepal.ai\/blog\/wp-content\/uploads\/2026\/01\/image-40-1024x693.png\" alt=\"\" class=\"wp-image-4874 lazyload\" data-srcset=\"https:\/\/crepal.ai\/blog\/wp-content\/uploads\/2026\/01\/image-40-1024x693.png 1024w, https:\/\/crepal.ai\/blog\/wp-content\/uploads\/2026\/01\/image-40-300x203.png 300w, https:\/\/crepal.ai\/blog\/wp-content\/uploads\/2026\/01\/image-40-768x520.png 768w, https:\/\/crepal.ai\/blog\/wp-content\/uploads\/2026\/01\/image-40-18x12.png 18w, https:\/\/crepal.ai\/blog\/wp-content\/uploads\/2026\/01\/image-40.png 1286w\" data-sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 1024px; --smush-placeholder-aspect-ratio: 1024\/693;\" \/><\/figure>\n<\/figure>\n\n\n\n<p>So, fellow VRAM wranglers, which card would <em>you<\/em> trust for your hero shots \u2014 the plucky 12GB or the spoiled 24GB beast? Drop your pick below and let\u2019s compare scars!<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n\n\n\n<p><strong>Previous posts:<\/strong><\/p>\n\n\n\n<figure class=\"wp-block-embed is-type-wp-embed is-provider-crepal-content-center wp-block-embed-crepal-content-center\"><div class=\"wp-block-embed__wrapper\">\n<blockquote class=\"wp-embedded-content\" data-secret=\"3udfuV5WRT\"><a href=\"https:\/\/crepal.ai\/blog\/aivideo\/blog-ltx-2-comfyui-quick-start\/\">LTX-2 Quick Start: Generate Your First Video in 10 Minutes<\/a><\/blockquote><iframe class=\"wp-embedded-content lazyload\" sandbox=\"allow-scripts\" security=\"restricted\" style=\"position: absolute; visibility: hidden;\" title=\"\u300a LTX-2 Quick Start: Generate Your First Video in 10 Minutes \u300b\u2014CrePal Content Center\" data-src=\"https:\/\/crepal.ai\/blog\/aivideo\/blog-ltx-2-comfyui-quick-start\/embed\/#?secret=f6SN2ebDV0#?secret=3udfuV5WRT\" data-secret=\"3udfuV5WRT\" width=\"600\" height=\"338\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" data-load-mode=\"1\"><\/iframe>\n<\/div><\/figure>\n\n\n\n<figure class=\"wp-block-embed is-type-wp-embed is-provider-crepal-content-center wp-block-embed-crepal-content-center\"><div class=\"wp-block-embed__wrapper\">\n<blockquote class=\"wp-embedded-content\" data-secret=\"zitulxN1io\"><a href=\"https:\/\/crepal.ai\/blog\/aivideo\/blog-how-to-install-ltx-2-in-comfyui\/\">How to Install LTX-2 in ComfyUI (Step-by-Step, No Custom Nodes)<\/a><\/blockquote><iframe class=\"wp-embedded-content lazyload\" sandbox=\"allow-scripts\" security=\"restricted\" style=\"position: absolute; visibility: hidden;\" title=\"\u300a How to Install LTX-2 in ComfyUI (Step-by-Step, No Custom Nodes) \u300b\u2014CrePal Content Center\" data-src=\"https:\/\/crepal.ai\/blog\/aivideo\/blog-how-to-install-ltx-2-in-comfyui\/embed\/#?secret=JnBI842kTu#?secret=zitulxN1io\" data-secret=\"zitulxN1io\" width=\"600\" height=\"338\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" data-load-mode=\"1\"><\/iframe>\n<\/div><\/figure>\n\n\n\n<figure class=\"wp-block-embed is-type-wp-embed is-provider-crepal-content-center wp-block-embed-crepal-content-center\"><div class=\"wp-block-embed__wrapper\">\n<blockquote class=\"wp-embedded-content\" data-secret=\"54lQHCp575\"><a href=\"https:\/\/crepal.ai\/blog\/aivideo\/blog-ltx-2-comfyui-day0-native-support\/\">LTX-2 ComfyUI: Day-0 Native Support Explained (What You Get Out of the Box)<\/a><\/blockquote><iframe class=\"wp-embedded-content lazyload\" sandbox=\"allow-scripts\" security=\"restricted\" style=\"position: absolute; visibility: hidden;\" title=\"\u300a LTX-2 ComfyUI: Day-0 Native Support Explained (What You Get Out of the Box) \u300b\u2014CrePal Content Center\" data-src=\"https:\/\/crepal.ai\/blog\/aivideo\/blog-ltx-2-comfyui-day0-native-support\/embed\/#?secret=VfZb1mliny#?secret=54lQHCp575\" data-secret=\"54lQHCp575\" width=\"600\" height=\"338\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" data-load-mode=\"1\"><\/iframe>\n<\/div><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>Hi friends, grab your oolong and buckle up \u2014 today we\u2019re seeing if my \u201cmiddle-class\u201d GPU can survive LTX-2 without throwing a tantrum. Spoiler: some of them almost did. On January 10, 2026, I sat down with a cup of oolong and a stubborn question: could my &#8220;middle-class&#8221; GPU handle LTX-2 without throwing a tantrum? [&hellip;]<\/p>\n","protected":false},"author":5,"featured_media":4870,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_gspb_post_css":"","_uag_custom_page_level_css":"","footnotes":""},"categories":[8],"tags":[],"class_list":["post-4869","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-aivideo"],"blocksy_meta":[],"uagb_featured_image_src":{"full":["https:\/\/crepal.ai\/blog\/wp-content\/uploads\/2026\/01\/image-36.png",1376,768,false],"thumbnail":["https:\/\/crepal.ai\/blog\/wp-content\/uploads\/2026\/01\/image-36-150x150.png",150,150,true],"medium":["https:\/\/crepal.ai\/blog\/wp-content\/uploads\/2026\/01\/image-36-300x167.png",300,167,true],"medium_large":["https:\/\/crepal.ai\/blog\/wp-content\/uploads\/2026\/01\/image-36-768x429.png",768,429,true],"large":["https:\/\/crepal.ai\/blog\/wp-content\/uploads\/2026\/01\/image-36-1024x572.png",1024,572,true],"1536x1536":["https:\/\/crepal.ai\/blog\/wp-content\/uploads\/2026\/01\/image-36.png",1376,768,false],"2048x2048":["https:\/\/crepal.ai\/blog\/wp-content\/uploads\/2026\/01\/image-36.png",1376,768,false],"trp-custom-language-flag":["https:\/\/crepal.ai\/blog\/wp-content\/uploads\/2026\/01\/image-36-18x10.png",18,10,true]},"uagb_author_info":{"display_name":"Dora","author_link":"https:\/\/crepal.ai\/blog\/author\/dora\/"},"uagb_comment_info":0,"uagb_excerpt":"Hi friends, grab your oolong and buckle up \u2014 today we\u2019re seeing if my \u201cmiddle-class\u201d GPU can survive LTX-2 without throwing a tantrum. Spoiler: some of them almost did. On January 10, 2026, I sat down with a cup of oolong and a stubborn question: could my &#8220;middle-class&#8221; GPU handle LTX-2 without throwing a tantrum?&hellip;","_links":{"self":[{"href":"https:\/\/crepal.ai\/blog\/wp-json\/wp\/v2\/posts\/4869","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/crepal.ai\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/crepal.ai\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/crepal.ai\/blog\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/crepal.ai\/blog\/wp-json\/wp\/v2\/comments?post=4869"}],"version-history":[{"count":4,"href":"https:\/\/crepal.ai\/blog\/wp-json\/wp\/v2\/posts\/4869\/revisions"}],"predecessor-version":[{"id":4880,"href":"https:\/\/crepal.ai\/blog\/wp-json\/wp\/v2\/posts\/4869\/revisions\/4880"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/crepal.ai\/blog\/wp-json\/wp\/v2\/media\/4870"}],"wp:attachment":[{"href":"https:\/\/crepal.ai\/blog\/wp-json\/wp\/v2\/media?parent=4869"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/crepal.ai\/blog\/wp-json\/wp\/v2\/categories?post=4869"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/crepal.ai\/blog\/wp-json\/wp\/v2\/tags?post=4869"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}