{"id":659,"date":"2026-05-20T20:10:14","date_gmt":"2026-05-20T20:10:14","guid":{"rendered":"https:\/\/convly.ai\/rtx-5080-vs-rtx-4080-super-for-ai\/"},"modified":"2026-05-20T20:10:14","modified_gmt":"2026-05-20T20:10:14","slug":"rtx-5080-vs-rtx-4080-super-for-ai","status":"publish","type":"post","link":"https:\/\/convly.ai\/ar\/rtx-5080-vs-rtx-4080-super-for-ai\/","title":{"rendered":"RTX 5080 \u0645\u0642\u0627\u0628\u0644 RTX 4080 \u0633\u0648\u0628\u0631 \u0644\u0644\u0630\u0643\u0627\u0621 \u0627\u0644\u0627\u0635\u0637\u0646\u0627\u0639\u064a \u0641\u064a \u0639\u0627\u0645 2026: \u0641\u062c\u0648\u0629 \u0627\u0644\u062c\u064a\u0644 \u0623\u0645 \u0627\u0644\u0635\u0641 \u0627\u0644\u062c\u0627\u0646\u0628\u064a\u061f"},"content":{"rendered":"

\u0625\u0646 RTX 5080<\/strong> and the RTX 4080 Super<\/strong> occupy the exact same slot in NVIDIA’s lineup \u2014 the $999 enthusiast card one tier below the flagship. Both carry 16 GB of VRAM<\/strong>. So the AI buyer’s question is simple: does Blackwell bring enough to justify choosing the 5080, or is the 4080 Super still the smart pickup?<\/p>\n

\u0627\u0644\u0625\u062c\u0627\u0628\u0629 \u0627\u0644\u0645\u062e\u062a\u0635\u0631\u0629 the 5080 is the better card, but the upgrade gap is narrower than the generation number suggests.<\/strong><\/p>\n

\n

\u0627\u0644\u0648\u062c\u0628\u0627\u062a \u0627\u0644\u0631\u0626\u064a\u0633\u064a\u0629<\/h3>\n
    \n
  • Both cards have 16 GB VRAM<\/strong> \u2014 identical model-size ceiling.<\/li>\n
  • The RTX 5080’s GDDR7 memory<\/strong> delivers ~960 GB\/s vs the 4080 Super’s ~736 GB\/s \u2014 a real ~30% bandwidth jump.<\/li>\n
  • Expect ~15\u201320% faster LLM inference<\/strong> on the 5080, driven mostly by bandwidth.<\/li>\n
  • Blackwell adds native FP4<\/strong> support \u2014 useful for next-gen quantized models, irrelevant today.<\/li>\n
  • If you already own a 4080 Super, do not upgrade. If you are buying fresh, the 5080 is the better long-term card.<\/li>\n<\/ul>\n<\/div>\n

    \u0644\u0645\u062d\u0629 \u0633\u0631\u064a\u0639\u0629<\/h2>\n\n\n\n\n\n\n\n\n\n\n\n\n
    \u0627\u0644\u0645\u0648\u0627\u0635\u0641\u0627\u062a<\/th>\nRTX 5080<\/th>\nRTX 4080 Super<\/th>\n<\/tr>\n<\/thead>\n
    Architecture<\/td>\nBlackwell GB203<\/td>\nAda Lovelace AD103<\/td>\n<\/tr>\n
    CUDA cores<\/td>\n10,752<\/td>\n10,240<\/td>\n<\/tr>\n
    VRAM<\/td>\n16 GB GDDR7<\/td>\n16 GB GDDR6X<\/td>\n<\/tr>\n
    \u0639\u0631\u0636 \u0627\u0644\u0646\u0637\u0627\u0642 \u0627\u0644\u062a\u0631\u062f\u062f\u064a \u0644\u0644\u0630\u0627\u0643\u0631\u0629<\/td>\n~960 GB\/s<\/td>\n~736 GB\/s<\/td>\n<\/tr>\n
    FP16 Tensor (dense)<\/td>\n~450 TFLOPS<\/td>\n~390 TFLOPS<\/td>\n<\/tr>\n
    Low-precision<\/td>\nFP8 + FP4<\/td>\nFP8<\/td>\n<\/tr>\n
    TDP<\/td>\n360 W<\/td>\n320 W<\/td>\n<\/tr>\n
    \u0627\u0644\u0633\u0639\u0631<\/td>\n$999<\/td>\n$999<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    16 GB: the shared ceiling<\/h2>\n

    Neither card is a big-model machine. 16 GB of VRAM<\/strong> comfortably handles:<\/p>\n

      \n
    • Llama 3 8B<\/strong> at 8-bit, or 13B-class<\/strong> models at 4-bit<\/li>\n
    • Stable Diffusion XL<\/strong> \u0648 Flux.1<\/strong> image generation<\/li>\n
    • LoRA fine-tuning<\/strong> of 7B\u20138B models<\/li>\n<\/ul>\n

      Neither card runs a 70B model in VRAM. If that is your goal, you want a 24 GB or 32 GB card and should stop reading here. For everyone else \u2014 the large majority of local AI users \u2014 16 GB is the practical sweet spot, and both cards deliver it.<\/p>\n

      Where Blackwell pulls ahead: bandwidth<\/h2>\n

      The CUDA-core counts are nearly identical (10,752 vs 10,240), so raw shader power is close. The real generational change is memory bandwidth<\/strong>. LLM token generation is memory-bound \u2014 the GPU spends most of its time reading weights, not computing \u2014 so the 5080’s GDDR7 advantage shows up directly:<\/p>\n\n\n\n\n\n\n\n\n
      \u0639\u0628\u0621 \u0627\u0644\u0639\u0645\u0644<\/th>\nRTX 5080<\/th>\nRTX 4080 Super<\/th>\n<\/tr>\n<\/thead>\n
      Llama 3 8B Q4_K_M<\/td>\n~125 tok\/s<\/td>\n~108 tok\/s<\/td>\n<\/tr>\n
      Llama 3 13B-class Q4<\/td>\n~78 tok\/s<\/td>\n~66 tok\/s<\/td>\n<\/tr>\n
      SDXL 1024\u00d71024 (30 steps)<\/td>\n~14 it\/s<\/td>\n~13 it\/s<\/td>\n<\/tr>\n
      Flux.1 dev (1024px)<\/td>\n~3.1 s\/image<\/td>\n~3.5 s\/image<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      Note the split: LLM inference<\/strong> sees the biggest gains (~15\u201320%) because it is bandwidth-bound, while Stable Diffusion<\/strong> \u2014 which is compute-bound \u2014 shows only a small lead since the core counts are so close.<\/p>\n

      FP4: a feature for tomorrow<\/h2>\n

      Blackwell introduces native FP4<\/strong> (4-bit floating point) tensor operations. On paper this doubles low-precision throughput versus FP8. In practice, as of 2026, almost no mainstream inference stack ships production FP4 kernels for consumer workloads. It is a genuine advantage, but a future-facing<\/strong> one \u2014 it will matter more in 2027 than it does today.<\/p>\n

      If you keep GPUs for four or five years, FP4 support is a real reason to favor the 5080. If you upgrade every cycle, it is close to irrelevant.<\/p>\n

      Power and efficiency<\/h2>\n

      The 5080 draws 360 W<\/strong> versus the 4080 Super’s 320 W<\/strong>. Blackwell is more efficient per operation, but the 5080 spends that headroom on higher clocks, so absolute draw is up. Both are happy on an 850 W PSU. Neither is a thermal problem in a well-ventilated case.<\/p>\n

      \n
      \n

      Choose the RTX 5080 if<\/h4>\n
        \n
      • You are buying fresh and want the longer-lived card<\/li>\n
      • Your main workload is LLM inference (bandwidth-bound)<\/li>\n
      • You want FP4 readiness for future quantized models<\/li>\n<\/ul>\n<\/div>\n
        \n

        Choose the RTX 4080 Super if<\/h4>\n
          \n
        • You find one discounted below $850 as stock clears<\/li>\n
        • Your focus is Stable Diffusion, where the gap is tiny<\/li>\n
        • You already own one \u2014 there is no reason to upgrade<\/li>\n<\/ul>\n<\/div>\n<\/div>\n

          The 16 GB warning<\/h2>\n

          Whichever you choose, understand the limitation you are buying into. 16 GB is increasingly tight<\/strong> for 2026 AI work. Larger image models, longer LLM context windows, and fine-tuning all push against that ceiling. If your budget can stretch to a 24 GB RTX 4090 or 32 GB RTX 5090, the capacity headroom outlasts the speed difference between these two 16 GB cards.<\/p>\n

          \u0627\u0644\u0623\u0633\u0626\u0644\u0629 \u0627\u0644\u0634\u0627\u0626\u0639\u0629<\/h2>\n

          Is the RTX 5080 worth upgrading to from a 4080 Super?<\/h3>\n

          No. Both have 16 GB, and the 5080 is only ~15\u201320% faster. That is not enough to justify the cost of a full GPU swap. Upgrade only if you are jumping two tiers, to a 24 GB or 32 GB card.<\/p>\n

          Can the RTX 5080 run Llama 3 70B?<\/h3>\n

          No. 70B at 4-bit needs roughly 40 GB. The 5080’s 16 GB forces heavy CPU offload, which is slow. For 70B in VRAM you need an RTX 5090 (32 GB) or a multi-GPU build.<\/p>\n

          Does FP4 support matter in 2026?<\/h3>\n

          Not yet for most users. FP4 is real and future-proof, but production inference stacks have not widely adopted it. Treat it as insurance for 2027, not a feature you will use this year.<\/p>\n

          Which is better for Stable Diffusion, the 5080 or 4080 Super?<\/h3>\n

          They are nearly tied. Stable Diffusion is compute-bound and the two cards have almost identical CUDA-core counts. The 5080 leads by only ~5\u20138%.<\/p>\n

          \u0627\u0644\u062d\u0643\u0645<\/h2>\n

          For a fresh purchase, the RTX 5080<\/strong> is the right call: same price as the 4080 Super, meaningfully more memory bandwidth, and FP4 headroom for the future. But this is an evolutionary step, not a revolution \u2014 anyone already running a 4080 Super<\/strong> should keep it. And both buyers should weigh the same hard truth: 16 GB is the real constraint here, and no amount of Blackwell polish changes that ceiling.<\/p>","protected":false},"excerpt":{"rendered":"

          The RTX 5080 and RTX 4080 Super both ship 16 GB of VRAM at a $999 price. The difference is Blackwell’s GDDR7 bandwidth and FP4 \u2014 here’s whether it matters for AI.<\/p>","protected":false},"author":1,"featured_media":671,"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":[281,284,256,356,326,351],"class_list":["post-659","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-comparisons","tag-ai-gpu","tag-blackwell","tag-local-llm","tag-rtx-4080-super","tag-rtx-5080","tag-stable-diffusion"],"uagb_featured_image_src":{"full":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/post-659.jpg",1200,630,false],"thumbnail":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/post-659-150x150.jpg",150,150,true],"medium":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/post-659-300x158.jpg",300,158,true],"medium_large":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/post-659-768x403.jpg",768,403,true],"large":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/post-659-1024x538.jpg",1024,538,true],"1536x1536":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/post-659.jpg",1200,630,false],"2048x2048":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/post-659.jpg",1200,630,false],"trp-custom-language-flag":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/post-659-18x9.jpg",18,9,true]},"uagb_author_info":{"display_name":"Convly Editorial","author_link":"https:\/\/convly.ai\/ar\/author\/mustafa\/"},"uagb_comment_info":0,"uagb_excerpt":"The RTX 5080 and RTX 4080 Super both ship 16 GB of VRAM at a $999 price. The difference is Blackwell's GDDR7 bandwidth and FP4 \u2014 here's whether it matters for AI.","_links":{"self":[{"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/posts\/659","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/comments?post=659"}],"version-history":[{"count":0,"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/posts\/659\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/media\/671"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/media?parent=659"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/categories?post=659"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/convly.ai\/ar\/wp-json\/wp\/v2\/tags?post=659"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}