{"id":658,"date":"2026-05-20T20:10:13","date_gmt":"2026-05-20T20:10:13","guid":{"rendered":"https:\/\/convly.ai\/rtx-5070-ti-vs-rtx-4070-ti-super-for-ai\/"},"modified":"2026-05-20T20:10:13","modified_gmt":"2026-05-20T20:10:13","slug":"rtx-5070-ti-vs-rtx-4070-ti-super-for-ai","status":"publish","type":"post","link":"https:\/\/convly.ai\/fr\/rtx-5070-ti-vs-rtx-4070-ti-super-for-ai\/","title":{"rendered":"RTX 5070 Ti vs RTX 4070 Ti Super pour AI in 2026 : \u00e9preuve de force en milieu de gamme"},"content":{"rendered":"

Les RTX 5070 Ti<\/strong> et RTX 4070 Ti Super<\/strong> sit in the sweet spot of NVIDIA’s lineup for AI builders \u2014 powerful enough to be genuinely useful, priced below the flagship tier. Both carry 16 Go de VRAM<\/strong>. The choice between them is the now-familiar Blackwell question: is faster memory worth picking the newer generation?<\/p>\n

La r\u00e9ponse est courte : the 5070 Ti is the better buy for a new build, but the 4070 Ti Super is a fine card that owners should keep.<\/strong><\/p>\n

\n

Principaux enseignements<\/h3>\n
    \n
  • Les deux cartes ont 16 GO DE VRAM<\/strong> \u2014 the same model-size ceiling.<\/li>\n
  • The RTX 5070 Ti’s GDDR7<\/strong> delivers ~896 GB\/s vs the 4070 Ti Super’s ~672 GB\/s \u2014 a ~33% bandwidth jump.<\/li>\n
  • That lifts LLM inference by ~15\u201320%<\/strong>; Stable Diffusion gains are smaller.<\/li>\n
  • The 5070 Ti adds FP4<\/strong> support and runs at a lower 300 W TDP.<\/li>\n
  • Buy the 5070 Ti fresh; do not upgrade an existing 4070 Ti Super \u2014 the gap is too small to justify it.<\/li>\n<\/ul>\n<\/div>\n

    En bref<\/h2>\n\n\n\n\n\n\n\n\n\n\n\n
    Spec<\/th>\nRTX 5070 Ti<\/th>\nRTX 4070 Ti Super<\/th>\n<\/tr>\n<\/thead>\n
    Architecture<\/td>\nBlackwell GB203<\/td>\nAda Lovelace AD103<\/td>\n<\/tr>\n
    C\u0153urs CUDA<\/td>\n8,960<\/td>\n8,448<\/td>\n<\/tr>\n
    VRAM<\/td>\n16 GB GDDR7<\/td>\n16 GB GDDR6X<\/td>\n<\/tr>\n
    Largeur de bande de la m\u00e9moire<\/td>\n~896 GB\/s<\/td>\n~672 GB\/s<\/td>\n<\/tr>\n
    Faible pr\u00e9cision<\/td>\nFP8 + FP4<\/td>\nFP8<\/td>\n<\/tr>\n
    TDP<\/td>\n300 W<\/td>\n285 W<\/td>\n<\/tr>\n
    Launch price<\/td>\n$749<\/td>\n$799<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    16 GB at a friendlier price<\/h2>\n

    The appeal of this tier is simple: 16 GB of VRAM without paying flagship money.<\/strong> Both cards comfortably handle the local-AI mainstream:<\/p>\n

      \n
    • Lama 3 8B<\/strong> at 8-bit, Classe 13B<\/strong> mod\u00e8les \u00e0 4 bits<\/li>\n
    • Diffusion stable XL<\/strong> et Flux.1<\/strong> g\u00e9n\u00e9ration d'images<\/li>\n
    • Mise au point de la LoRA<\/strong> des mod\u00e8les 7B-8B<\/li>\n<\/ul>\n

      Neither runs a 70B model in VRAM \u2014 that needs 24 GB or more. But for the workloads most enthusiasts actually run, 16 GB is enough, and getting it for $749\u2013799 instead of $999+ is the whole point of this class.<\/p>\n

      Bandwidth is the real difference<\/h2>\n

      The CUDA-core counts are close (8,960 vs 8,448), so shader power is similar. The meaningful change is largeur de bande de la m\u00e9moire<\/strong>: the 5070 Ti’s GDDR7 pushes ~896 GB\/s against the 4070 Ti Super’s ~672 GB\/s \u2014 a genuine ~33% gain. Because LLM token generation is memory-bound, the speedup flows through fairly directly:<\/p>\n\n\n\n\n\n\n\n
      Charge de travail<\/th>\nRTX 5070 Ti<\/th>\nRTX 4070 Ti Super<\/th>\n<\/tr>\n<\/thead>\n
      Lama 3 8B Q4_K_M<\/td>\n~108 tok\/s<\/td>\n~90 tok\/s<\/td>\n<\/tr>\n
      Llama 3 13B-classe Q4<\/td>\n~66 tok\/s<\/td>\n~55 tok\/s<\/td>\n<\/tr>\n
      SDXL 1024\u00d71024 (30 \u00e9tapes)<\/td>\n~11 it\/s<\/td>\n~10 it\/s<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      The split is the same one seen across the Blackwell range: Inf\u00e9rence LLM<\/strong> gains the most (~15\u201320%) because it is bandwidth-bound, while Diffusion stable<\/strong>, being compute-bound with near-equal core counts, gains only a little.<\/p>\n

      FP4 and efficiency<\/h2>\n

      Like the rest of the Blackwell line, the 5070 Ti adds native FP4<\/strong>. As of 2026 few consumer inference stacks use it, so treat it as future insurance rather than a feature you will exercise this year. The 5070 Ti is also impressively efficient \u2014 Blackwell lets it deliver more performance within a modest 300 W<\/strong> envelope, close to the 4070 Ti Super’s 285 W.<\/p>\n

      \n
      \n

      Choose the RTX 5070 Ti if<\/h4>\n
        \n
      • You are building fresh and want the longer-lived card<\/li>\n
      • LLM inference is your main workload<\/li>\n
      • You value FP4 readiness and slightly better efficiency<\/li>\n<\/ul>\n<\/div>\n
        \n

        Choose the RTX 4070 Ti Super if<\/h4>\n
          \n
        • You find it discounted well below $700 as stock clears<\/li>\n
        • You already own one \u2014 the upgrade gap is too small<\/li>\n
        • Your workload is mostly Stable Diffusion, where the cards nearly tie<\/li>\n<\/ul>\n<\/div>\n<\/div>\n

          The honest mid-range advice<\/h2>\n

          This tier is the value pick, but the same caveat applies as one rung up: 16 GB is a real ceiling.<\/strong> If you expect to push into larger models, longer contexts, or heavier fine-tuning, the jump to a 24 GB RTX 4090 unlocks far more than the speed difference between these two 16 GB cards. Inside the 16 GB class, though, the 5070 Ti is the smarter long-term choice.<\/p>\n

          FAQ<\/h2>\n

          Is the RTX 5070 Ti worth it over the 4070 Ti Super for AI?<\/h3>\n

          For a new build, yes \u2014 it is faster, costs slightly less at launch, and adds FP4. But it is an incremental gain, not a leap. If you already own a 4070 Ti Super, do not upgrade.<\/p>\n

          Can the RTX 5070 Ti run Llama 3 70B?<\/h3>\n

          No. A 70B model at 4-bit needs roughly 40 GB, far beyond the 5070 Ti’s 16 GB. For 70B in VRAM you need an RTX 5090 or a multi-GPU build.<\/p>\n

          How much faster is the 5070 Ti for LLM inference?<\/h3>\n

          About 15\u201320% in real workloads. The gain comes almost entirely from GDDR7’s ~33% higher memory bandwidth, since LLM token generation is memory-bound.<\/p>\n

          Is 16 GB of VRAM enough for AI in 2026?<\/h3>\n

          For mainstream work \u2014 8B\u201313B models, Stable Diffusion, small fine-tunes \u2014 yes. For large models or long contexts it is tight. If you expect to grow beyond that, consider a 24 GB card instead.<\/p>\n

          Verdict<\/h2>\n

          Les RTX 5070 Ti<\/strong> is the right mid-range AI card to buy in 2026: more bandwidth, FP4 headroom, and a slightly lower price than the 4070 Ti Super<\/strong> it replaces. But this is evolution, not revolution \u2014 the 4070 Ti Super remains a perfectly good card, and its owners gain nothing from upgrading. Both deliver the real attraction of this tier: 16 GB of usable VRAM without flagship pricing.<\/p>","protected":false},"excerpt":{"rendered":"

          The RTX 5070 Ti and 4070 Ti Super are the value-enthusiast picks for local AI \u2014 both 16 GB. Here’s whether Blackwell’s bandwidth gain is worth choosing the newer card.<\/p>","protected":false},"author":1,"featured_media":670,"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":[281,284,256,355,354,327],"class_list":["post-658","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-comparisons","tag-ai-gpu","tag-blackwell","tag-local-llm","tag-mid-range-gpu","tag-rtx-4070-ti-super","tag-rtx-5070-ti"],"uagb_featured_image_src":{"full":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/post-658.jpg",1200,630,false],"thumbnail":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/post-658-150x150.jpg",150,150,true],"medium":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/post-658-300x158.jpg",300,158,true],"medium_large":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/post-658-768x403.jpg",768,403,true],"large":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/post-658-1024x538.jpg",1024,538,true],"1536x1536":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/post-658.jpg",1200,630,false],"2048x2048":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/post-658.jpg",1200,630,false],"trp-custom-language-flag":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/post-658-18x9.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":"The RTX 5070 Ti and 4070 Ti Super are the value-enthusiast picks for local AI \u2014 both 16 GB. Here's whether Blackwell's bandwidth gain is worth choosing the newer card.","_links":{"self":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts\/658","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=658"}],"version-history":[{"count":0,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts\/658\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/media\/670"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/media?parent=658"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/categories?post=658"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/tags?post=658"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}