{"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":"2026-06-15T18:18:25","modified_gmt":"2026-06-15T18:18:25","slug":"rtx-5090-vs-mac-studio-m4-ultra-for-local-llms","status":"publish","type":"post","link":"https:\/\/convly.ai\/it\/rtx-5090-vs-mac-studio-m4-ultra-for-local-llms\/","title":{"rendered":"RTX 5090 vs Mac Studio M4 Ultra per LLM locali nel 2026"},"content":{"rendered":"<p>If you want to run large language models on your own desk in 2026, two very different machines top the list. The <strong>RTX 5090<\/strong> is the fastest consumer GPU ever made. The <strong>Mac Studio M4 Ultra<\/strong> is a quiet box that can hold models several times larger. They represent two opposite philosophies \u2014 <strong>raw speed<\/strong> contro <strong>raw capacity<\/strong> \u2014 and the right answer depends entirely on which models you want to run.<\/p>\n<div class=\"convly-tldr\">\n<h3>Punti chiave<\/h3>\n<ul>\n<li>The RTX 5090 has <strong>32 GB GDDR7<\/strong> at 1,792 GB\/s \u2014 blistering speed, limited capacity.<\/li>\n<li>The Mac Studio M4 Ultra offers <strong>far more unified memory<\/strong> \u2014 it holds much larger models, more slowly per token.<\/li>\n<li>For models that fit in 32 GB, the <strong>RTX 5090 is dramatically faster<\/strong>.<\/li>\n<li>For models above 32 GB \u2014 100B-class and up \u2014 the <strong>Mac is the only one that can load them<\/strong>.<\/li>\n<li>For training and fine-tuning, the RTX 5090 and CUDA win clearly; the Mac is an inference machine.<\/li>\n<\/ul>\n<\/div>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_84 counter-flat ez-toc-counter ez-toc-container-direction\">\n<label for=\"ez-toc-cssicon-toggle-item-6a38bbee3e2e8\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Attiva\/Disattiva<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #000000;color:#000000\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewbox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #000000;color:#000000\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewbox=\"0 0 24 24\" version=\"1.2\" baseprofile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-6a38bbee3e2e8\"  aria-label=\"Attiva\/Disattiva\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1' ><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/convly.ai\/it\/rtx-5090-vs-mac-studio-m4-ultra-for-local-llms\/#At_a_glance\" >At a glance<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/convly.ai\/it\/rtx-5090-vs-mac-studio-m4-ultra-for-local-llms\/#The_core_trade-off_speed_vs_capacity\" >The core trade-off: speed vs capacity<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/convly.ai\/it\/rtx-5090-vs-mac-studio-m4-ultra-for-local-llms\/#Models_that_fit_in_32_GB_the_RTX_5090_wins\" >Models that fit in 32 GB: the RTX 5090 wins<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/convly.ai\/it\/rtx-5090-vs-mac-studio-m4-ultra-for-local-llms\/#Models_above_32_GB_only_the_Mac_can_run_them\" >Models above 32 GB: only the Mac can run them<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/convly.ai\/it\/rtx-5090-vs-mac-studio-m4-ultra-for-local-llms\/#Training_and_fine-tuning_the_PC_clearly\" >Training and fine-tuning: the PC, clearly<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/convly.ai\/it\/rtx-5090-vs-mac-studio-m4-ultra-for-local-llms\/#The_honest_recommendation\" >The honest recommendation<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/convly.ai\/it\/rtx-5090-vs-mac-studio-m4-ultra-for-local-llms\/#Total_cost_of_ownership_power_heat_and_the_real_price\" >Total cost of ownership: power, heat, and the real price<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/convly.ai\/it\/rtx-5090-vs-mac-studio-m4-ultra-for-local-llms\/#FAQ\" >Domande frequenti<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/convly.ai\/it\/rtx-5090-vs-mac-studio-m4-ultra-for-local-llms\/#Verdict\" >Verdict<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/convly.ai\/it\/rtx-5090-vs-mac-studio-m4-ultra-for-local-llms\/#Related_articles\" >Articoli correlati<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"At_a_glance\"><\/span>At a glance<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<table class=\"convly-vs\">\n<thead>\n<tr>\n<th>Factor<\/th>\n<th>RTX 5090 (PC)<\/th>\n<th>Mac Studio M4 Ultra<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Memory for models<\/td>\n<td>32 GB GDDR7<\/td>\n<td class=\"convly-vs-winner\">Large unified pool<\/td>\n<\/tr>\n<tr>\n<td>Larghezza di banda della memoria<\/td>\n<td class=\"convly-vs-winner\">1.792 GB\/s<\/td>\n<td>~2x M4 Max (lower than 5090)<\/td>\n<\/tr>\n<tr>\n<td>Speed (models that fit)<\/td>\n<td class=\"convly-vs-winner\">Much faster<\/td>\n<td>Moderato<\/td>\n<\/tr>\n<tr>\n<td>Largest model it can load<\/td>\n<td>~70B quantized<\/td>\n<td class=\"convly-vs-winner\">100B-class and beyond<\/td>\n<\/tr>\n<tr>\n<td>Training \/ fine-tuning<\/td>\n<td class=\"convly-vs-winner\">Excellent (CUDA)<\/td>\n<td>Limitato<\/td>\n<\/tr>\n<tr>\n<td>Power draw<\/td>\n<td>575 W GPU alone<\/td>\n<td class=\"convly-vs-winner\">Low, near-silent<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2><span class=\"ez-toc-section\" id=\"The_core_trade-off_speed_vs_capacity\"><\/span>The core trade-off: speed vs capacity<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>This comparison is not about which machine is &#8220;better.&#8221; It is about a genuine engineering trade-off:<\/p>\n<ul>\n<li>Il <strong>RTX 5090<\/strong> has the <strong>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 <em>fast<\/em>. But 32 GB is a hard ceiling.<\/li>\n<li>Il <strong>Mac Studio M4 Ultra<\/strong> has <strong>far more memory<\/strong> but <strong>less bandwidth<\/strong>. It can <em>hold<\/em> enormous models the 5090 cannot touch \u2014 but it generates each token more slowly.<\/li>\n<\/ul>\n<p>So the decision reduces to one question: <strong>are the models you care about above or below 32 GB?<\/strong><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Models_that_fit_in_32_GB_the_RTX_5090_wins\"><\/span>Models that fit in 32 GB: the RTX 5090 wins<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>For everything that fits in the 5090&#8217;s VRAM \u2014 <strong>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<p>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<h2><span class=\"ez-toc-section\" id=\"Models_above_32_GB_only_the_Mac_can_run_them\"><\/span>Models above 32 GB: only the Mac can run them<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Now flip it. A <strong>100B-class model<\/strong>, or a 70B model at high precision, or several large models held resident at once \u2014 these simply <strong>do not fit<\/strong> in 32 GB. The RTX 5090 cannot load them without spilling to system RAM, which collapses performance.<\/p>\n<p>The Mac Studio M4 Ultra, with its large unified memory pool, <strong>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 &#8220;run the biggest open models on my desk,&#8221; the Mac is not the faster option; it is the only option.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Training_and_fine-tuning_the_PC_clearly\"><\/span>Training and fine-tuning: the PC, clearly<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>If your work goes beyond inference into <strong>training and fine-tuning<\/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<div class=\"convly-procons\">\n<div class=\"pros\">\n<h4>Choose the RTX 5090 if<\/h4>\n<ul>\n<li>Your models fit in 32 GB \u2014 up to 70B quantized<\/li>\n<li>You fine-tune or train, not just run inference<\/li>\n<li>You want maximum speed and the broadest software support<\/li>\n<\/ul>\n<\/div>\n<div class=\"cons\">\n<h4>Choose the Mac Studio M4 Ultra if<\/h4>\n<ul>\n<li>You need to run 100B-class models locally<\/li>\n<li>You want a silent, low-power machine that &#8220;just works&#8221;<\/li>\n<li>Your work is inference, and capacity beats raw speed<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<h2><span class=\"ez-toc-section\" id=\"The_honest_recommendation\"><\/span>The honest recommendation<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Per <strong>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 <strong>Mac Studio M4 Ultra<\/strong> when you have a specific, deliberate need to run models <em>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<p><!--ai-enriched--><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Total_cost_of_ownership_power_heat_and_the_real_price\"><\/span>Total cost of ownership: power, heat, and the real price<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Sticker price is only the start. These two machines diverge sharply on what they cost to <strong>buy<\/strong>, <strong>run<\/strong>, e <strong>live next to<\/strong> \u2014 and the 2026 GPU market makes that gap wider than the spec sheets suggest.<\/p>\n<p>On purchase price, the RTX 5090 looks cheaper on paper: NVIDIA&#8217;s launch MSRP was <strong>$1,999<\/strong>, versus roughly <strong>$3,999<\/strong> for the base top-end Mac Studio. But the 5090 is a bare card. You still need a capable host PC, and through 2026 the ongoing memory shortage has pushed real 5090 street prices well above MSRP \u2014 frequently into the <strong>$3,000-$4,000+<\/strong> range for in-stock cards. Add a CPU, motherboard, RAM, storage, case, and a <strong>1000W+ power supply<\/strong>, and a complete 5090 build often lands at or above the price of the Mac it&#8217;s competing with.<\/p>\n<p>Running costs tilt further toward Apple. The 5090 carries a <strong>575W TDP<\/strong> with transient spikes that can approach 900W, and a loaded desktop around it can pull well over 700W from the wall under sustained inference. The Mac Studio is in a different class entirely: it idles in the single-watt range and, in independent testing, drew only around <strong>200W while running a 671B-parameter model<\/strong>. Over a year of heavy daily use, that difference compounds into a meaningful electricity bill \u2014 and it is far more pronounced in regions with high power prices or where you&#8217;re paying to cool the room afterward.<\/p>\n<p>Two factors people forget until the box is on the desk:<\/p>\n<ul>\n<li><strong>Heat and noise.<\/strong> A 5090 under load dumps serious heat and spins fans audibly; in a small office or bedroom that is genuinely disruptive. The Mac Studio stays cool and near-silent, which matters if the machine sits where you work.<\/li>\n<li><strong>Resale and upgrade path.<\/strong> The PC is modular \u2014 you can reuse the chassis and drop in a future GPU. The Mac is fixed at purchase: the unified memory you buy is the memory you keep, so size it generously up front (and note that in 2026 the largest memory tiers have grown scarcer and pricier as the same shortage bites Apple too).<\/li>\n<\/ul>\n<p><strong>Bottom line:<\/strong> if you optimize for raw tokens-per-dollar on models that fit in 32GB, the PC can win \u2014 but only once you account for the full build and your local electricity rate. If you value low running cost, silence, and a small footprint, the Mac&#8217;s higher entry price buys real advantages over its lifetime.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"FAQ\"><\/span>Domande frequenti<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3>Is the RTX 5090 or Mac Studio better for local LLMs?<\/h3>\n<p>For models that fit in the 5090&#8217;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<h3>Can the RTX 5090 run 100B-parameter models?<\/h3>\n<p>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<h3>Why is the Mac slower per token if it has more memory?<\/h3>\n<p>Token generation speed is governed by memory bandwidth, and the RTX 5090&#8217;s 1,792 GB\/s is significantly higher than the Mac&#8217;s. The Mac trades per-token speed for the ability to hold much larger models.<\/p>\n<h3>Which is better for fine-tuning AI models?<\/h3>\n<p>The RTX 5090. The CUDA ecosystem dominates training and fine-tuning, with mature support across every major library. The Mac&#8217;s MLX framework is strong for inference but limited for training.<\/p>\n<h3>How much does it cost in electricity to run an RTX 5090 versus a Mac Studio?<\/h3>\n<p>The gap is large. The RTX 5090 has a 575W TDP, and a full PC around it can draw 700W or more under sustained inference, whereas the Mac Studio idles in the single-watt range and pulled roughly 200W in testing while running a very large model. For occasional use the difference is minor, but for a machine running models all day, the Mac can cost a fraction as much to operate \u2014 and it generates far less waste heat to cool.<\/p>\n<h3>Is the RTX 5090 loud, and does it run hot for local LLM use?<\/h3>\n<p>Under sustained load it is both. The 575W card produces significant heat and audible fan noise during long inference sessions, which can be disruptive in a quiet room. The Mac Studio, by contrast, runs cool and near-silent even under heavy model workloads. If the machine will sit on your desk rather than in a separate space, acoustics and heat are a real, often-overlooked deciding factor.<\/p>\n<h3>Should I buy two RTX 5090s instead of one Mac Studio for more memory?<\/h3>\n<p>Only if your software and workload genuinely support multi-GPU. Two 5090s give you more combined VRAM and strong parallel throughput, but you take on much higher power draw, a demanding PSU and cooling setup, and the complexity of splitting models across cards \u2014 and many local-LLM tools handle multi-GPU imperfectly. For simply loading one very large model with minimal fuss, a single Mac Studio&#8217;s large unified memory pool is usually the simpler, quieter, and more power-efficient route.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Verdict\"><\/span>Verdict<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Il <strong>RTX 5090<\/strong> e <strong>Mac Studio M4 Ultra<\/strong> answer two different questions. If you ask &#8220;how fast can I run the models I use?&#8221; \u2014 and those models fit in 32 GB \u2014 the RTX 5090 wins, decisively, and it trains too. If you ask &#8220;what is the biggest model I can run at home?&#8221; 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>\n<p><!--related-block--><\/p>\n<div class=\"convly-related\">\n<h2><span class=\"ez-toc-section\" id=\"Related_articles\"><\/span>Articoli correlati<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li><a href=\"https:\/\/convly.ai\/it\/deepseek-v4-vs-qwen3-2026\/\">DeepSeek V4 contro Qwen3.7 Max: lo scontro del 2026<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/it\/rx-7900-xtx-vs-rtx-4090-for-ai\/\">AMD RX 7900 XTX contro RTX 4090 per l'IA nel 2026: ROCm pu\u00f2 competere?<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/it\/rtx-5080-vs-rtx-4080-super-for-ai\/\">RTX 5080 contro RTX 4080 Super per l'IA nel 2026: un vero salto generazionale o semplicemente un aggiornamento marginale?<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/it\/rtx-5070-ti-vs-rtx-4070-ti-super-for-ai\/\">RTX 5070 Ti contro RTX 4070 Ti Super per l'IA nel 2026: lo scontro nella fascia media<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/it\/rtx-4090-vs-rtx-3090-for-ai\/\">RTX 4090 contro RTX 3090 per l'IA nel 2026: vale davvero la pena aggiornare?<\/a><\/li>\n<\/ul>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>It&#8217;s the classic 2026 local-AI dilemma: an RTX 5090&#8217;s blistering speed and 32 GB, or a Mac Studio M4 Ultra&#8217;s enormous unified memory. Here&#8217;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":{"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":""}},"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"],"_links":{"self":[{"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/posts\/660","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/comments?post=660"}],"version-history":[{"count":3,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/posts\/660\/revisions"}],"predecessor-version":[{"id":1158,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/posts\/660\/revisions\/1158"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/media\/672"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/media?parent=660"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/categories?post=660"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/tags?post=660"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}