{"id":654,"date":"2026-05-20T20:10:08","date_gmt":"2026-05-20T20:10:08","guid":{"rendered":"https:\/\/convly.ai\/mac-studio-m4-max-vs-m4-ultra-for-ai\/"},"modified":"2026-06-10T05:05:13","modified_gmt":"2026-06-10T05:05:13","slug":"mac-studio-m4-max-vs-m4-ultra-for-ai","status":"publish","type":"post","link":"https:\/\/convly.ai\/it\/mac-studio-m4-max-vs-m4-ultra-for-ai\/","title":{"rendered":"Mac Studio M4 Max contro M4 Ultra per l'IA nel 2026: quale acquistare per modelli linguistici locali (LLM)"},"content":{"rendered":"<p>For running local LLMs, Apple Silicon has a quiet superpower: <strong>memoria unificata<\/strong>. The GPU can address the entire pool, so a Mac Studio with 128 GB or more can load models that would need several discrete GPUs on a PC. Within the Mac Studio line, the choice comes down to two chips: the <strong>M4 Max<\/strong> and the step-up <strong>M4 Ultra<\/strong>.<\/p>\n<p>The short answer: <strong>the M4 Max suits most local-AI users; the M4 Ultra is for those loading the very largest models or wanting the fastest token rates.<\/strong><\/p>\n<div class=\"convly-tldr\">\n<h3>Punti chiave<\/h3>\n<ul>\n<li>Both rely on <strong>memoria unificata<\/strong> \u2014 the GPU can use the whole RAM pool to hold models.<\/li>\n<li>The M4 Ultra is essentially <strong>two M4 Max dies fused<\/strong>: roughly double the GPU cores and memory bandwidth.<\/li>\n<li>The M4 Ultra supports <strong>larger maximum memory<\/strong>, letting it hold bigger models than the M4 Max can.<\/li>\n<li>For LLM inference, the Ultra delivers <strong>noticeably higher tokens-per-second<\/strong> because token generation is bandwidth-bound.<\/li>\n<li>Buy the M4 Max for models up to ~70B quantized; step up to the M4 Ultra for 100B-class models and maximum speed.<\/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-6a38bc1d54ab1\" 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-6a38bc1d54ab1\"  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\/mac-studio-m4-max-vs-m4-ultra-for-ai\/#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\/mac-studio-m4-max-vs-m4-ultra-for-ai\/#Unified_memory_the_Mac_advantage\" >Unified memory: the Mac advantage<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/convly.ai\/it\/mac-studio-m4-max-vs-m4-ultra-for-ai\/#Two_dies_double_the_bandwidth\" >Two dies, double the bandwidth<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/convly.ai\/it\/mac-studio-m4-max-vs-m4-ultra-for-ai\/#MLX_vs_the_PC_ecosystem\" >MLX vs the PC ecosystem<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/convly.ai\/it\/mac-studio-m4-max-vs-m4-ultra-for-ai\/#Which_Mac_Studio_should_you_buy\" >Which Mac Studio should you buy?<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/convly.ai\/it\/mac-studio-m4-max-vs-m4-ultra-for-ai\/#How_much_unified_memory_do_you_actually_need\" >How much unified memory do you actually need?<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/convly.ai\/it\/mac-studio-m4-max-vs-m4-ultra-for-ai\/#FAQ\" >Domande frequenti<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/convly.ai\/it\/mac-studio-m4-max-vs-m4-ultra-for-ai\/#Verdict\" >Verdict<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/convly.ai\/it\/mac-studio-m4-max-vs-m4-ultra-for-ai\/#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>Specifiche<\/th>\n<th>Mac Studio M4 Ultra<\/th>\n<th>Mac Studio M4 Max<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Chip design<\/td>\n<td class=\"convly-vs-winner\">Two M4 Max dies (UltraFusion)<\/td>\n<td>Single M4 Max die<\/td>\n<\/tr>\n<tr>\n<td>GPU cores<\/td>\n<td class=\"convly-vs-winner\">Up to ~80-core<\/td>\n<td>Up to ~40-core<\/td>\n<\/tr>\n<tr>\n<td>Memoria unificata<\/td>\n<td class=\"convly-vs-winner\">Higher maximum<\/td>\n<td>Up to 128 GB<\/td>\n<\/tr>\n<tr>\n<td>Larghezza di banda della memoria<\/td>\n<td class=\"convly-vs-winner\">~2x the M4 Max<\/td>\n<td>~546 GB\/s<\/td>\n<\/tr>\n<tr>\n<td>AI framework<\/td>\n<td>MLX, llama.cpp (Metal)<\/td>\n<td>MLX, llama.cpp (Metal)<\/td>\n<\/tr>\n<tr>\n<td>Power draw<\/td>\n<td>Higher<\/td>\n<td class=\"convly-vs-winner\">Lower<\/td>\n<\/tr>\n<tr>\n<td>Prezzo<\/td>\n<td>Premium<\/td>\n<td class=\"convly-vs-winner\">More affordable<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2><span class=\"ez-toc-section\" id=\"Unified_memory_the_Mac_advantage\"><\/span>Unified memory: the Mac advantage<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>On a PC, a model must fit in a discrete GPU&#8217;s VRAM \u2014 16, 24, or 32 GB. On a Mac, the GPU shares the <strong>entire system memory pool<\/strong>. A 128 GB Mac Studio can therefore load models that would require multiple high-end PC GPUs. This is the single reason Apple Silicon is taken seriously for local AI: capacity that PC desktops reach only with expensive multi-GPU builds.<\/p>\n<p>Both the M4 Max and M4 Ultra share this architecture. The difference is <strong>how much<\/strong> memory you can configure and <strong>quanto \u00e8 veloce<\/strong> the GPU can stream it.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Two_dies_double_the_bandwidth\"><\/span>Two dies, double the bandwidth<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The M4 Ultra is built with Apple&#8217;s <strong>UltraFusion<\/strong> packaging \u2014 two M4 Max dies joined into one chip. In practice that means roughly <strong>double the GPU cores<\/strong> and, crucially, <strong>double the memory bandwidth<\/strong>.<\/p>\n<p>Bandwidth is the number that matters most for LLM inference. Token generation is memory-bound: the chip reads the entire model&#8217;s weights for every token produced. The M4 Ultra&#8217;s wider memory path therefore translates fairly directly into higher tokens-per-second:<\/p>\n<table class=\"convly-vs\">\n<thead>\n<tr>\n<th>Workload<\/th>\n<th>M4 Ultra<\/th>\n<th>M4 Max<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Llama 3 8B (4-bit, MLX)<\/td>\n<td class=\"convly-vs-winner\">Faster<\/td>\n<td>Elevate<\/td>\n<\/tr>\n<tr>\n<td>Llama 3 70B (4-bit)<\/td>\n<td class=\"convly-vs-winner\">Comfortable, faster t\/s<\/td>\n<td>Runs (needs 128 GB), slower<\/td>\n<\/tr>\n<tr>\n<td>100B-class models<\/td>\n<td class=\"convly-vs-winner\">Fits with higher max memory<\/td>\n<td>Limited by 128 GB ceiling<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>We avoid quoting exact tokens-per-second here because real results vary widely with quantization, context length, and framework version \u2014 but the direction is consistent: the Ultra is meaningfully faster, and on the largest models it is the only one with enough memory.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"MLX_vs_the_PC_ecosystem\"><\/span>MLX vs the PC ecosystem<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Both chips run the same software stack: Apple&#8217;s <strong>MLX<\/strong> framework and <strong>llama.cpp<\/strong> with the Metal backend. MLX has matured quickly and is now a genuinely good local-inference path on Apple Silicon.<\/p>\n<p>But be clear about the trade-off versus a PC. The Mac excels at <strong>inferenza<\/strong> of large models thanks to memory capacity. It is weaker for <strong>training and fine-tuning<\/strong>, where the CUDA ecosystem still dominates and many libraries have no Metal path. If your goal is to run big models locally, a Mac Studio is excellent. If your goal is to train them, a PC with NVIDIA GPUs remains the better tool.<\/p>\n<div class=\"convly-procons\">\n<div class=\"pros\">\n<h4>Choose the M4 Ultra if<\/h4>\n<ul>\n<li>You want to run 100B-class models locally<\/li>\n<li>You want the fastest token rates Apple Silicon offers<\/li>\n<li>You run long contexts or multiple models at once<\/li>\n<\/ul>\n<\/div>\n<div class=\"cons\">\n<h4>Choose the M4 Max if<\/h4>\n<ul>\n<li>Your models are up to ~70B quantized \u2014 128 GB handles them<\/li>\n<li>You want the better value and lower power draw<\/li>\n<li>You also want a strong general-purpose creative workstation<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<h2><span class=\"ez-toc-section\" id=\"Which_Mac_Studio_should_you_buy\"><\/span>Which Mac Studio should you buy?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Decide by the largest model you realistically need. For <strong>8B to 70B quantized<\/strong> models \u2014 which covers the overwhelming majority of local-AI use \u2014 an <strong>M4 Max with 128 GB<\/strong> is capable, efficient, and the better value. Step up to the <strong>M4 Ultra<\/strong> only if you specifically intend to run <strong>100B-class models<\/strong>, want the highest possible token rates, or plan to keep several large models resident at once. The Ultra is a specialist&#8217;s machine; the Max is the sensible default.<\/p>\n<p><!--ai-enriched--><\/p>\n<h2><span class=\"ez-toc-section\" id=\"How_much_unified_memory_do_you_actually_need\"><\/span>How much unified memory do you actually need?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The chip matters less than the memory tier you pick, because on Apple Silicon the model has to fit in unified memory or it does not run at usable speed. A useful rule: macOS reserves a slice of RAM for the system, so plan on roughly <strong>70-75% of your unified memory being available for the model<\/strong>. The rest goes to the OS, your apps, and the key-value cache that grows with context length. Size up from there, not down.<\/p>\n<p>Work backwards from the model and quantization you intend to run. At a common 4-bit quant, a model needs roughly half a gigabyte of memory per billion parameters, plus headroom for context. That gives a practical buying ladder:<\/p>\n<ul>\n<li><strong>36-64GB (M4 Max):<\/strong> comfortable for 7B-14B models at full speed and 30B-class models at 4-bit. Ideal for coding assistants, RAG, and everyday local chat.<\/li>\n<li><strong>128GB (M4 Max top spec) or 96GB (M3 Ultra base):<\/strong> the sweet spot for 70B models like Llama 3.3 70B at 4-bit, with room for long context. This is where most serious local-LLM users land.<\/li>\n<li><strong>256GB (M3 Ultra):<\/strong> runs multiple large models at once, or a single 70B at higher precision for better quality.<\/li>\n<li><strong>512GB (M3 Ultra only):<\/strong> the headline tier. It is the one configuration that can load a 671B Mixture-of-Experts model such as DeepSeek R1 at 4-bit locally, which needs roughly 400GB-plus of memory allocated to the GPU.<\/li>\n<\/ul>\n<p>Two honest caveats. First, fitting a model is not the same as running it fast: memory bandwidth and the active-parameter count, not total RAM, set your tokens-per-second. A dense 70B will feel noticeably slower than a sparse MoE that activates only a few billion parameters per token. Second, unified memory is soldered and <strong>cannot be upgraded later<\/strong>, so buy for the largest model you realistically expect to run over the machine&#8217;s life. Under-buying memory is the single most common, and most expensive, mistake Mac Studio AI buyers make.<\/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 M4 Ultra worth it over the M4 Max for AI?<\/h3>\n<p>Only if you need to run very large models (100B-class) or want maximum token speed. For models up to ~70B quantized, the M4 Max with 128 GB is capable and far better value.<\/p>\n<h3>Why is unified memory good for running LLMs?<\/h3>\n<p>Because the GPU can use the entire system RAM pool to hold a model, a Mac avoids the discrete-VRAM limit of PC GPUs. A 128 GB Mac Studio loads models that would need multiple high-end NVIDIA cards.<\/p>\n<h3>Can a Mac Studio train AI models?<\/h3>\n<p>It can, but it is not its strength. Apple Silicon excels at inference of large models. For training and fine-tuning, NVIDIA&#8217;s CUDA ecosystem is far more mature, and many training libraries lack a Metal path.<\/p>\n<h3>M4 Max or M4 Ultra for running Llama 3 70B?<\/h3>\n<p>Both can run a 70B model quantized, provided the M4 Max is configured with 128 GB. The M4 Ultra does it faster, thanks to roughly double the memory bandwidth.<\/p>\n<h3>Wait, does an M4 Ultra Mac Studio actually exist?<\/h3>\n<p>Not as of mid-2026. When Apple refreshed the Mac Studio in March 2025 it paired the M4 Max with an <strong>M3 Ultra<\/strong>, not an M4 Ultra, and never shipped an Ultra-tier M4. So the real-world choice is M4 Max versus M3 Ultra. If you are reading &#8220;M4 Ultra&#8221; in older buying guides, mentally substitute M3 Ultra: it is the chip that scales to 32 CPU cores, 80 GPU cores, 819GB\/s of bandwidth, and up to 512GB of unified memory. A true next-generation Ultra is expected with the M5 Mac Studio, widely rumored for later in 2026.<\/p>\n<h3>What does it cost to run a Mac Studio for AI compared to a PC GPU rig?<\/h3>\n<p>Far less in electricity. An M3 Ultra Mac Studio idles well under 20W and stays under 200W even while serving a huge model like DeepSeek R1, against a PSU rated for roughly 480W. A multi-GPU PC built to hold a comparable model in VRAM can pull several times that under load, plus added cooling. Over years of always-on local inference, the Mac&#8217;s efficiency meaningfully offsets its higher purchase price, plus it runs near-silent and needs no special power circuit.<\/p>\n<h3>Is the Mac Studio&#8217;s memory bandwidth enough for fast local inference?<\/h3>\n<p>For single-user local use, yes. Token generation is bandwidth-bound, and the M4 Max delivers up to 546GB\/s while the M3 Ultra roughly doubles that at 819GB\/s. That is why the Ultra feels markedly faster on large dense models even when both chips can hold the weights. Where Apple Silicon still trails high-end discrete GPUs is raw prompt-processing (prefill) throughput and concurrent multi-user serving, neither of which most desktop AI workflows are bottlenecked on.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Verdict\"><\/span>Verdict<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>For local AI, the Mac Studio&#8217;s appeal is unified memory \u2014 and both the <strong>M4 Max<\/strong> e <strong>M4 Ultra<\/strong> deliver it. The <strong>M4 Max with 128 GB<\/strong> is the right choice for most: it runs models up to 70B quantized, sips power, and doubles as a superb creative workstation. The <strong>M4 Ultra<\/strong> is the answer when you genuinely need to go bigger or faster \u2014 100B-class models and top token rates. Pick by the size of the models you actually plan to run, not by the name of the chip.<\/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\/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>On a Mac, the AI question is unified memory: how much, and how fast. Here&#8217;s how the M4 Max and M4 Ultra Mac Studio configurations compare for running local LLMs.<\/p>","protected":false},"author":1,"featured_media":666,"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":[252,256,250,344,343,299],"class_list":["post-654","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-comparisons","tag-apple-silicon-ai","tag-local-llm","tag-m4-max","tag-m4-ultra","tag-mac-studio","tag-unified-memory"],"_links":{"self":[{"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/posts\/654","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=654"}],"version-history":[{"count":2,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/posts\/654\/revisions"}],"predecessor-version":[{"id":988,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/posts\/654\/revisions\/988"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/media\/666"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/media?parent=654"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/categories?post=654"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/tags?post=654"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}