{"id":657,"date":"2026-05-20T20:10:12","date_gmt":"2026-05-20T20:10:12","guid":{"rendered":"https:\/\/convly.ai\/rtx-4090-vs-rtx-3090-for-ai\/"},"modified":"2026-05-20T20:10:12","modified_gmt":"2026-05-20T20:10:12","slug":"rtx-4090-vs-rtx-3090-for-ai","status":"publish","type":"post","link":"https:\/\/convly.ai\/fr\/rtx-4090-vs-rtx-3090-for-ai\/","title":{"rendered":"RTX 4090 vs RTX 3090 pour AI en 2026 : la mise \u00e0 niveau en vaut-elle la peine ?"},"content":{"rendered":"

For local AI work, the RTX 3090<\/strong> has aged into one of the best value cards ever made: 24 GB of VRAM on the used market for $700\u2013900. The RTX 4090<\/strong> doubles down \u2014 same 24 GB, but a far faster GPU at roughly $1,200\u20131,500 used in 2026.<\/p>\n

If both cards hold the same amount of memory, is the 4090 worth nearly double? The honest answer: it depends entirely on whether your time is the bottleneck.<\/strong><\/p>\n

\n

Principaux enseignements<\/h3>\n
    \n
  • Les deux cartes ont 24 GB VRAM<\/strong> \u2014 they fit the exact same models. No model runs on one but not the other.<\/li>\n
  • The RTX 4090 is ~1.7x faster<\/strong> for AI inference and ~1.8x faster<\/strong> for fine-tuning.<\/li>\n
  • Pour Diffusion stable XL<\/strong>, expect ~18 it\/s on the 4090 vs ~10 it\/s on the 3090.<\/li>\n
  • The 3090 wins decisively on value-per-dollar<\/strong> and on dual-card builds (48 GB for ~$1,600).<\/li>\n
  • Buy the 4090 if iteration speed matters; buy the 3090 (or two) if VRAM capacity matters more than speed.<\/li>\n<\/ul>\n<\/div>\n

    En bref<\/h2>\n\n\n\n\n\n\n\n\n\n\n\n\n
    Spec<\/th>\nRTX 4090<\/th>\nRTX 3090<\/th>\n<\/tr>\n<\/thead>\n
    Architecture<\/td>\nAda Lovelace AD102<\/td>\nAmpere GA102<\/td>\n<\/tr>\n
    C\u0153urs CUDA<\/td>\n16,384<\/td>\n10,496<\/td>\n<\/tr>\n
    VRAM<\/td>\n24 GB GDDR6X<\/td>\n24 GB GDDR6X<\/td>\n<\/tr>\n
    Largeur de bande de la m\u00e9moire<\/td>\n1,008 GB\/s<\/td>\n936 GB\/s<\/td>\n<\/tr>\n
    Tenseur FP16 (dense)<\/td>\n~330 TFLOPS<\/td>\n~142 TFLOPS<\/td>\n<\/tr>\n
    TDP<\/td>\n450 W<\/td>\n350 W<\/td>\n<\/tr>\n
    Launch price<\/td>\n$1,599<\/td>\n$1,499<\/td>\n<\/tr>\n
    Used price (2026)<\/td>\n$1,200\u20131,500<\/td>\n$700\u2013900<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    VRAM: a tie that changes everything<\/h2>\n

    The single most important number for local AI is VRAM, and here the two cards are identical: 24 GB<\/strong>. That means any model that fits on one fits on the other:<\/p>\n

      \n
    • Lama 3 8B<\/strong> et Classe 13B<\/strong> models run comfortably at full or near-full precision.<\/li>\n
    • Llama 3 70B<\/strong> fits only at aggressive 4-bit quantization (Q4_K_M \u2248 40 GB) with partial CPU offload \u2014 painful on either card alone.<\/li>\n
    • Diffusion stable XL<\/strong> et Flux<\/strong> image models fit with room to spare.<\/li>\n<\/ul>\n

      Because the memory ceiling is the same, the 4090 never unlocks a model the 3090 can’t touch. The 4090’s advantage is purely speed<\/strong> \u2014 it does the same work faster.<\/p>\n

      Inference benchmarks<\/h2>\n

      Pour Inf\u00e9rence LLM<\/strong>, the gap tracks memory bandwidth and tensor throughput:<\/p>\n\n\n\n\n\n\n\n\n
      Charge de travail<\/th>\nRTX 4090<\/th>\nRTX 3090<\/th>\n<\/tr>\n<\/thead>\n
      Lama 3 8B Q4_K_M<\/td>\n~140 tok\/s<\/td>\n~95 tok\/s<\/td>\n<\/tr>\n
      Llama 3 13B-classe Q4<\/td>\n~90 tok\/s<\/td>\n~58 tok\/s<\/td>\n<\/tr>\n
      SDXL 1024\u00d71024 (30 \u00e9tapes)<\/td>\n~18 it\/s<\/td>\n~10 it\/s<\/td>\n<\/tr>\n
      Flux.1 dev (1024px)<\/td>\n~2.4 s\/image<\/td>\n~4.6 s\/image<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      The pattern is consistent: the 4090 lands around 1.6\u20131.8x<\/strong> the 3090’s throughput. That is a real, felt difference \u2014 a Stable Diffusion batch that takes the 3090 ten minutes finishes in roughly six on the 4090.<\/p>\n

      Fine-tuning and training<\/h2>\n

      Pour Mise au point de la LoRA<\/strong> of a 7B\u20138B model, the 4090’s larger tensor-core throughput and faster FP16\/BF16 paths matter more than in inference. A typical LoRA run that takes the 3090 around five hours completes in roughly two-and-three-quarter hours<\/strong> on the 4090 \u2014 close to a 1.8x speedup.<\/p>\n

      The 3090 has one quiet weakness here: it lacks the 4090’s improved FP8 support, so emerging FP8 training recipes either fall back to BF16 or don’t run at all. If you intend to follow cutting-edge training techniques, the 4090 ages better.<\/p>\n

      Power and heat<\/h2>\n

      The 3090 draws 350 W<\/strong>; the 4090 draws 450 W<\/strong> and can spike higher under sustained AI load. Over a year of heavy use that is a measurable difference on your power bill, and the 4090 demands a stronger PSU (850 W minimum, 1000 W recommended). The 3090 also runs hot on its GDDR6X memory modules \u2014 worth a thermal-pad replacement on used units.<\/p>\n

      \n
      \n

      Choose the RTX 4090 if<\/h4>\n
        \n
      • You iterate constantly and value time over money<\/li>\n
      • You want FP8 support and better long-term software relevance<\/li>\n
      • You fine-tune models regularly, not just run inference<\/li>\n<\/ul>\n<\/div>\n
        \n

        Choose the RTX 3090 if<\/h4>\n
          \n
        • You want the most VRAM per dollar on the planet<\/li>\n
        • You plan a dual-card build (48 GB total for ~$1,600)<\/li>\n
        • Your workloads are batch jobs you can leave running overnight<\/li>\n<\/ul>\n<\/div>\n<\/div>\n

          The dual-3090 wildcard<\/h2>\n

          Here is the argument that keeps the 3090 alive in 2026: two of them cost about the same as one used 4090<\/strong> and give you 48 GB of pooled VRAM<\/strong>. With tensor parallelism (vLLM, ExLlamaV2), a dual-3090 rig runs Llama 3 70B<\/strong> entirely in VRAM \u2014 something no single consumer card except the RTX 5090 can do.<\/p>\n

          You trade speed and power efficiency for capacity. For anyone whose real constraint is “I need to run bigger models,” two 3090s beat one 4090.<\/p>\n

          FAQ<\/h2>\n

          Is the RTX 4090 worth double the price of a 3090 for AI?<\/h3>\n

          Only if speed is your bottleneck. The 4090 is ~1.7x faster but unlocks no new models, since both have 24 GB. If you run batch jobs overnight, the 3090’s value is unbeatable.<\/p>\n

          Can the RTX 3090 run Llama 3 70B?<\/h3>\n

          Not comfortably on its own \u2014 70B at 4-bit needs ~40 GB. A single 3090 must offload layers to system RAM, which is slow. Two 3090s (48 GB pooled) run it well.<\/p>\n

          Which card is better for Stable Diffusion?<\/h3>\n

          The RTX 4090, clearly \u2014 around 18 it\/s on SDXL versus 10 it\/s on the 3090. For image generation, where you iterate on prompts constantly, that speed gap is felt every minute.<\/p>\n

          Does the RTX 3090 still get good software support in 2026?<\/h3>\n

          Yes. Ampere is fully supported by CUDA, PyTorch, vLLM, and llama.cpp. Its only gap is native FP8, which affects a small but growing set of training recipes.<\/p>\n

          Verdict<\/h2>\n

          Both cards are excellent AI hardware in 2026. The RTX 4090<\/strong> is the better card in every raw metric and the right buy if you iterate fast and can absorb the price. The RTX 3090<\/strong> remains the value champion \u2014 and in a dual-card configuration it does something the 4090 simply cannot, running a 70B model fully in VRAM for less money. Match the card to your real constraint: speed, or capacity.<\/p>","protected":false},"excerpt":{"rendered":"

          Both the RTX 4090 and RTX 3090 ship 24 GB of VRAM \u2014 so for AI, the question isn’t whether a model fits, it’s how fast it runs. Here’s the benchmark-backed verdict.<\/p>","protected":false},"author":1,"featured_media":669,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_uag_custom_page_level_css":"","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":""}},"_themeisle_gutenberg_block_has_review":false,"footnotes":""},"categories":[246],"tags":[281,256,352,280,282,353],"class_list":["post-657","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-comparisons","tag-ai-gpu","tag-local-llm","tag-rtx-3090","tag-rtx-4090","tag-stable-diffusion-benchmark","tag-used-gpu"],"uagb_featured_image_src":{"full":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/post-657.jpg",1200,630,false],"thumbnail":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/post-657-150x150.jpg",150,150,true],"medium":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/post-657-300x158.jpg",300,158,true],"medium_large":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/post-657-768x403.jpg",768,403,true],"large":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/post-657-1024x538.jpg",1024,538,true],"1536x1536":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/post-657.jpg",1200,630,false],"2048x2048":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/post-657.jpg",1200,630,false],"trp-custom-language-flag":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/post-657-18x9.jpg",18,9,true]},"uagb_author_info":{"display_name":"Convly Editorial","author_link":"https:\/\/convly.ai\/fr\/author\/mustafa\/"},"uagb_comment_info":0,"uagb_excerpt":"Both the RTX 4090 and RTX 3090 ship 24 GB of VRAM \u2014 so for AI, the question isn't whether a model fits, it's how fast it runs. Here's the benchmark-backed verdict.","_links":{"self":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts\/657","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=657"}],"version-history":[{"count":0,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts\/657\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/media\/669"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/media?parent=657"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/categories?post=657"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/tags?post=657"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}