{"id":660,"date":"2026-05-20T20:10:15","date_gmt":"2026-05-20T20:10:15","guid":{"rendered":"https:\/\/convly.ai\/rtx-5090-vs-mac-studio-m4-ultra-for-local-llms\/"},"modified":"2026-05-20T20:10:15","modified_gmt":"2026-05-20T20:10:15","slug":"rtx-5090-vs-mac-studio-m4-ultra-for-local-llms","status":"publish","type":"post","link":"https:\/\/convly.ai\/fr\/rtx-5090-vs-mac-studio-m4-ultra-for-local-llms\/","title":{"rendered":"RTX 5090 vs Mac Studio M4 Ultra pour les LLM locaux en 2026"},"content":{"rendered":"

If you want to run large language models on your own desk in 2026, two very different machines top the list. The RTX 5090<\/strong> is the fastest consumer GPU ever made. The Mac Studio M4 Ultra<\/strong> is a quiet box that can hold models several times larger. They represent two opposite philosophies \u2014 raw speed<\/strong> versus raw capacity<\/strong> \u2014 and the right answer depends entirely on which models you want to run.<\/p>\n

\n

Principaux enseignements<\/h3>\n
    \n
  • The RTX 5090 has 32 GB GDDR7<\/strong> at 1,792 GB\/s \u2014 blistering speed, limited capacity.<\/li>\n
  • The Mac Studio M4 Ultra offers far more unified memory<\/strong> \u2014 it holds much larger models, more slowly per token.<\/li>\n
  • For models that fit in 32 GB, the RTX 5090 is dramatically faster<\/strong>.<\/li>\n
  • For models above 32 GB \u2014 100B-class and up \u2014 the Mac is the only one that can load them<\/strong>.<\/li>\n
  • For training and fine-tuning, the RTX 5090 and CUDA win clearly; the Mac is an inference machine.<\/li>\n<\/ul>\n<\/div>\n

    En bref<\/h2>\n\n\n\n\n\n\n\n\n\n\n
    Factor<\/th>\nRTX 5090 (PC)<\/th>\nMac Studio M4 Ultra<\/th>\n<\/tr>\n<\/thead>\n
    Memory for models<\/td>\n32 GB GDDR7<\/td>\nLarge unified pool<\/td>\n<\/tr>\n
    Largeur de bande de la m\u00e9moire<\/td>\n1,792 GB\/s<\/td>\n~2x M4 Max (lower than 5090)<\/td>\n<\/tr>\n
    Speed (models that fit)<\/td>\nMuch faster<\/td>\nModerate<\/td>\n<\/tr>\n
    Largest model it can load<\/td>\n~70B quantized<\/td>\n100B-class and beyond<\/td>\n<\/tr>\n
    Training \/ fine-tuning<\/td>\nExcellent (CUDA)<\/td>\nLimited<\/td>\n<\/tr>\n
    Puissance absorb\u00e9e<\/td>\n575 W GPU alone<\/td>\nLow, near-silent<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    The core trade-off: speed vs capacity<\/h2>\n

    This comparison is not about which machine is “better.” It is about a genuine engineering trade-off:<\/p>\n

      \n
    • Les RTX 5090<\/strong> has the fastest memory<\/strong> here by a wide margin \u2014 1,792 GB\/s. Since LLM token generation is bandwidth-bound, any model that fits in its 32 GB runs fast<\/em>. But 32 GB is a hard ceiling.<\/li>\n
    • Les Mac Studio M4 Ultra<\/strong> has far more memory<\/strong> but less bandwidth<\/strong>. It can hold<\/em> enormous models the 5090 cannot touch \u2014 but it generates each token more slowly.<\/li>\n<\/ul>\n

      So the decision reduces to one question: are the models you care about above or below 32 GB?<\/strong><\/p>\n

      Models that fit in 32 GB: the RTX 5090 wins<\/h2>\n

      For everything that fits in the 5090’s VRAM \u2014 8B, 13B, 32B, and 70B-class models at 4-bit<\/strong> \u2014 the RTX 5090 is the clear winner. Its enormous bandwidth produces token rates the Mac cannot match, often by a factor of two or more. If your daily work is models in this range, the PC is faster, and it is not close.<\/p>\n

      The 5090 also wins on iteration. For Stable Diffusion, video generation, and any workload where you tweak and re-run constantly, that speed compounds into real productivity.<\/p>\n

      Models above 32 GB: only the Mac can run them<\/h2>\n

      Now flip it. A 100B-class model<\/strong>, or a 70B model at high precision, or several large models held resident at once \u2014 these simply do not fit<\/strong> in 32 GB. The RTX 5090 cannot load them without spilling to system RAM, which collapses performance.<\/p>\n

      The Mac Studio M4 Ultra, with its large unified memory pool, loads them and runs them<\/strong>. Slower per token than the 5090 would be \u2014 but the 5090 cannot run them at all. For the researcher or hobbyist whose goal is specifically “run the biggest open models on my desk,” the Mac is not the faster option; it is the only option.<\/p>\n

      Training and fine-tuning: the PC, clearly<\/h2>\n

      If your work goes beyond inference into formation et perfectionnement<\/strong>, the RTX 5090 and the CUDA ecosystem win decisively. The PC stack \u2014 PyTorch, Flash Attention, bitsandbytes, the entire research toolchain \u2014 assumes CUDA. The Mac runs MLX, which is excellent for inference but far thinner for training. Anyone whose workflow includes regular fine-tuning should choose the PC.<\/p>\n

      \n
      \n

      Choose the RTX 5090 if<\/h4>\n
        \n
      • Your models fit in 32 GB \u2014 up to 70B quantized<\/li>\n
      • You fine-tune or train, not just run inference<\/li>\n
      • You want maximum speed and the broadest software support<\/li>\n<\/ul>\n<\/div>\n
        \n

        Choose the Mac Studio M4 Ultra if<\/h4>\n
          \n
        • You need to run 100B-class models locally<\/li>\n
        • You want a silent, low-power machine that “just works”<\/li>\n
        • Your work is inference, and capacity beats raw speed<\/li>\n<\/ul>\n<\/div>\n<\/div>\n

          The honest recommendation<\/h2>\n

          Pour most people<\/strong>, the RTX 5090 is the better local-LLM machine in 2026: it is faster, it trains as well as it infers, and 32 GB covers the models the large majority actually run. Choose the Mac Studio M4 Ultra<\/strong> when you have a specific, deliberate need to run models beyond<\/em> what 32 GB allows \u2014 and when near-silent, low-power operation has real value to you. One is the high-performance generalist; the other is the large-capacity specialist.<\/p>\n

          FAQ<\/h2>\n

          Is the RTX 5090 or Mac Studio better for local LLMs?<\/h3>\n

          For models that fit in the 5090’s 32 GB (up to ~70B quantized), the RTX 5090 is much faster. For larger models \u2014 100B-class and up \u2014 only the Mac Studio M4 Ultra has enough memory to load them.<\/p>\n

          Can the RTX 5090 run 100B-parameter models?<\/h3>\n

          Not in VRAM. With 32 GB it tops out around 70B at 4-bit. Running 100B-class models locally requires the large unified memory of a Mac Studio M4 Ultra or a multi-GPU PC build.<\/p>\n

          Why is the Mac slower per token if it has more memory?<\/h3>\n

          Token generation speed is governed by memory bandwidth, and the RTX 5090’s 1,792 GB\/s is significantly higher than the Mac’s. The Mac trades per-token speed for the ability to hold much larger models.<\/p>\n

          Which is better for fine-tuning AI models?<\/h3>\n

          The RTX 5090. The CUDA ecosystem dominates training and fine-tuning, with mature support across every major library. The Mac’s MLX framework is strong for inference but limited for training.<\/p>\n

          Verdict<\/h2>\n

          Les RTX 5090<\/strong> et Mac Studio M4 Ultra<\/strong> answer two different questions. If you ask “how fast can I run the models I use?” \u2014 and those models fit in 32 GB \u2014 the RTX 5090 wins, decisively, and it trains too. If you ask “what is the biggest model I can run at home?” the Mac Studio M4 Ultra wins, because capacity is something raw speed cannot substitute for. Know which question is yours, and the choice is obvious.<\/p>","protected":false},"excerpt":{"rendered":"

          It’s the classic 2026 local-AI dilemma: an RTX 5090’s blistering speed and 32 GB, or a Mac Studio M4 Ultra’s enormous unified memory. Here’s which platform wins, and why.<\/p>","protected":false},"author":1,"featured_media":672,"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":[347,256,344,343,251,299],"class_list":["post-660","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-comparisons","tag-apple-silicon","tag-local-llm","tag-m4-ultra","tag-mac-studio","tag-rtx-5090","tag-unified-memory"],"uagb_featured_image_src":{"full":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/post-660.jpg",1200,630,false],"thumbnail":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/post-660-150x150.jpg",150,150,true],"medium":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/post-660-300x158.jpg",300,158,true],"medium_large":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/post-660-768x403.jpg",768,403,true],"large":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/post-660-1024x538.jpg",1024,538,true],"1536x1536":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/post-660.jpg",1200,630,false],"2048x2048":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/post-660.jpg",1200,630,false],"trp-custom-language-flag":["https:\/\/convly.ai\/wp-content\/uploads\/2026\/05\/post-660-18x9.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":"It's the classic 2026 local-AI dilemma: an RTX 5090's blistering speed and 32 GB, or a Mac Studio M4 Ultra's enormous unified memory. Here's which platform wins, and why.","_links":{"self":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts\/660","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=660"}],"version-history":[{"count":0,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/posts\/660\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/media\/672"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/media?parent=660"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/categories?post=660"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/convly.ai\/fr\/wp-json\/wp\/v2\/tags?post=660"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}