{"id":662,"date":"2026-05-20T20:10:18","date_gmt":"2026-05-20T20:10:18","guid":{"rendered":"https:\/\/convly.ai\/rx-7900-xtx-vs-rtx-4090-for-ai\/"},"modified":"2026-06-19T16:40:00","modified_gmt":"2026-06-19T16:40:00","slug":"rx-7900-xtx-vs-rtx-4090-for-ai","status":"publish","type":"post","link":"https:\/\/convly.ai\/it\/rx-7900-xtx-vs-rtx-4090-for-ai\/","title":{"rendered":"AMD RX 7900 XTX contro RTX 4090 per l'IA nel 2026: ROCm pu\u00f2 competere?"},"content":{"rendered":"<p>On paper, the <strong>AMD RX 7900 XTX<\/strong> looks like a bargain against the <strong>RTX 4090<\/strong>: the same <strong>24 GB of VRAM<\/strong>, similar memory bandwidth, and a price that runs hundreds of dollars lower. For local AI, VRAM is king \u2014 so why doesn&#8217;t everyone buy the AMD card?<\/p>\n<p>One word: <strong>software<\/strong>. This comparison is really CUDA versus ROCm, and that is where the decision is won or lost.<\/p>\n<div class=\"convly-tldr\">\n<h3>Punti chiave<\/h3>\n<ul>\n<li>Both cards have <strong>24 GB VRAM<\/strong> \u2014 they fit the same models.<\/li>\n<li>The RTX 4090 is roughly <strong>1.5\u20131.8x faster<\/strong> in real AI workloads, despite closer raw specs.<\/li>\n<li>The gap is mostly <strong>software<\/strong>: CUDA is mature everywhere; ROCm works but lags in coverage and optimization.<\/li>\n<li>Per <strong>llama.cpp inference<\/strong>, the 7900 XTX is competitive. For <strong>training and exotic libraries<\/strong>, it is frustrating.<\/li>\n<li>Buy the 7900 XTX only if you run inference, on Linux, and value the price saving over speed and simplicity.<\/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-6a38b1493da55\" 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-6a38b1493da55\"  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\/rx-7900-xtx-vs-rtx-4090-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\/rx-7900-xtx-vs-rtx-4090-for-ai\/#The_hardware_is_closer_than_the_results\" >The hardware is closer than the results<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/convly.ai\/it\/rx-7900-xtx-vs-rtx-4090-for-ai\/#Inference_benchmarks\" >Inference benchmarks<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/convly.ai\/it\/rx-7900-xtx-vs-rtx-4090-for-ai\/#Training_and_the_library_problem\" >Training and the library problem<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/convly.ai\/it\/rx-7900-xtx-vs-rtx-4090-for-ai\/#The_Windows_caveat\" >The Windows caveat<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/convly.ai\/it\/rx-7900-xtx-vs-rtx-4090-for-ai\/#Total_cost_of_ownership_what_each_card_really_costs_to_own\" >Total cost of ownership: what each card really costs to own<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/convly.ai\/it\/rx-7900-xtx-vs-rtx-4090-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\/rx-7900-xtx-vs-rtx-4090-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\/rx-7900-xtx-vs-rtx-4090-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>RTX 4090<\/th>\n<th>RX 7900 XTX<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Architettura<\/td>\n<td>Ada Lovelace AD102<\/td>\n<td>RDNA 3 Navi 31<\/td>\n<\/tr>\n<tr>\n<td>Shader units<\/td>\n<td>16,384 CUDA<\/td>\n<td>6,144 stream processors<\/td>\n<\/tr>\n<tr>\n<td>VRAM<\/td>\n<td>24 GB GDDR6X<\/td>\n<td>24 GB GDDR6<\/td>\n<\/tr>\n<tr>\n<td>Larghezza di banda della memoria<\/td>\n<td class=\"convly-vs-winner\">1,008 GB\/s<\/td>\n<td>960 GB\/s<\/td>\n<\/tr>\n<tr>\n<td>AI software stack<\/td>\n<td class=\"convly-vs-winner\">CUDA (mature)<\/td>\n<td>ROCm (improving)<\/td>\n<\/tr>\n<tr>\n<td>TDP<\/td>\n<td>450 W<\/td>\n<td class=\"convly-vs-winner\">355 W<\/td>\n<\/tr>\n<tr>\n<td>Launch price<\/td>\n<td>$1,599<\/td>\n<td class=\"convly-vs-winner\">$999<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2><span class=\"ez-toc-section\" id=\"The_hardware_is_closer_than_the_results\"><\/span>The hardware is closer than the results<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Look only at the spec sheet and the 7900 XTX seems competitive: identical VRAM, near-identical bandwidth, lower power, lower price. AMD&#8217;s RDNA 3 is genuinely capable silicon.<\/p>\n<p>But AI performance is not just silicon \u2014 it is silicon <strong>plus<\/strong> the kernels, compilers, and libraries that drive it. NVIDIA has spent fifteen years building CUDA into the default substrate of every deep-learning framework. AMD&#8217;s <strong>ROCm<\/strong> is real and improving fast, but it is years behind in breadth and in low-level optimization. That gap turns a near-tie on paper into a clear NVIDIA win in practice.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Inference_benchmarks\"><\/span>Inference benchmarks<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<table class=\"convly-vs\">\n<thead>\n<tr>\n<th>Workload<\/th>\n<th>RTX 4090<\/th>\n<th>RX 7900 XTX<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Llama 3 8B Q4 (llama.cpp)<\/td>\n<td class=\"convly-vs-winner\">~140 tok\/s<\/td>\n<td>~95 tok\/s<\/td>\n<\/tr>\n<tr>\n<td>Llama 3 13B-class Q4<\/td>\n<td class=\"convly-vs-winner\">~90 tok\/s<\/td>\n<td>~60 tok\/s<\/td>\n<\/tr>\n<tr>\n<td>SDXL 1024\u00d71024 (30 steps)<\/td>\n<td class=\"convly-vs-winner\">~18 it\/s<\/td>\n<td>~9 it\/s<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Two things stand out. First, in <strong>llama.cpp<\/strong> \u2014 which has a well-optimized ROCm\/Vulkan backend \u2014 the 7900 XTX is respectable, landing within striking distance of the 4090. Second, in <strong>Stable Diffusion<\/strong>, the gap blows open to roughly 2x, because the PyTorch + ROCm path for diffusion models is far less optimized than NVIDIA&#8217;s.<\/p>\n<p>The lesson: AMD&#8217;s deficit is not uniform. It is small where the open-source community has invested heavily and large everywhere else.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Training_and_the_library_problem\"><\/span>Training and the library problem<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Per <strong>fine-tuning and training<\/strong>, the 7900 XTX runs into a harder wall. Many popular libraries \u2014 Flash Attention variants, bitsandbytes quantization, xFormers, and a long tail of research code \u2014 assume CUDA. Some have ROCm forks; many do not, or lag versions behind.<\/p>\n<p>You can train on a 7900 XTX. But you will spend time patching environments, hunting for ROCm-compatible builds, and occasionally discovering that the technique you wanted to try simply has no AMD path yet. On a 4090, that friction is close to zero \u2014 you <code>pip install<\/code> and it works.<\/p>\n<div class=\"convly-procons\">\n<div class=\"pros\">\n<h4>Choose the RX 7900 XTX if<\/h4>\n<ul>\n<li>You run inference, primarily through llama.cpp or Ollama<\/li>\n<li>You are comfortable on Linux and with ROCm setup<\/li>\n<li>The ~$600 price saving genuinely matters to your budget<\/li>\n<\/ul>\n<\/div>\n<div class=\"cons\">\n<h4>Choose the RTX 4090 if<\/h4>\n<ul>\n<li>You fine-tune models or follow cutting-edge research code<\/li>\n<li>You want everything to work on the first try<\/li>\n<li>You do serious Stable Diffusion or video-generation work<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<h2><span class=\"ez-toc-section\" id=\"The_Windows_caveat\"><\/span>The Windows caveat<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>ROCm support on <strong>Windows<\/strong> remains weaker than on Linux. AMD has improved this, but for the smoothest AI experience on a 7900 XTX you should plan to run Linux. The RTX 4090 is fully supported on both. If you are a Windows-only user, the AMD card&#8217;s friction multiplies, and the 4090 becomes the obvious choice.<\/p>\n<p><!--ai-enriched--><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Total_cost_of_ownership_what_each_card_really_costs_to_own\"><\/span>Total cost of ownership: what each card really costs to own<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Benchmarks tell you which card is faster. They do not tell you which one you can actually buy in 2026, what it will cost to run, or whether the price gap is justified for your workload. For a home AI rig, three factors decide that, and none of them appear on a spec sheet.<\/p>\n<p><strong>The VRAM ceiling is identical.<\/strong> Both cards ship with 24 GB, so they hit the same wall. At Q4 quantization, a 24 GB card comfortably runs 27B-to-32B-class models (roughly 17-22 GB on disk, leaving room for context) and is genuinely excellent there. Neither card runs a 70B model natively. To do that you would offload layers to system RAM (slow) or add a second 24 GB card. This matters because it means the RTX 4090 is <em>non<\/em> buying you a bigger model ceiling, only faster tokens within the same ceiling.<\/p>\n<p><strong>Power and PSU costs favor AMD.<\/strong> The RTX 4090 carries a 450W TDP; the RX 7900 XTX sits near 355W, roughly 20% lower. Both also produce sharp transient spikes that briefly exceed those ratings, so board partners recommend an 850W power supply as the floor, stepping up to 1000W if you pair the card with a high-end CPU (think Core i9 or Ryzen 9) or run two GPUs. A workstation that runs inference for hours a day will see the wattage gap show up on the electricity bill, and a 24\/7 server build will feel it most.<\/p>\n<p><strong>Availability and resale tilt the other way.<\/strong> The RTX 4090 is discontinued, with production having ended in late 2024. New stock is scarce and heavily inflated, so most buyers are now in the used market, where prices have stayed high. The RX 7900 XTX is still sold new, typically at a lower price than even a used 4090. That changes the real-world question from &#8220;which is faster&#8221; to &#8220;which can I get, and at what premium.&#8221;<\/p>\n<table class=\"convly-vs\">\n<tr>\n<th>Ownership factor<\/th>\n<th>RX 7900 XTX<\/th>\n<th>RTX 4090<\/th>\n<\/tr>\n<tr>\n<td>VRAM (model ceiling)<\/td>\n<td>24 GB<\/td>\n<td>24 GB<\/td>\n<\/tr>\n<tr>\n<td>Rated power draw<\/td>\n<td>~355W<\/td>\n<td>450 W<\/td>\n<\/tr>\n<tr>\n<td>Alimentatore consigliato<\/td>\n<td>850W+<\/td>\n<td>850-1000W+<\/td>\n<\/tr>\n<tr>\n<td>2026 availability<\/td>\n<td>New, widely stocked<\/td>\n<td>Discontinued, used mostly<\/td>\n<\/tr>\n<tr>\n<td>Price position<\/td>\n<td>Lower<\/td>\n<td>Higher (scarcity premium)<\/td>\n<\/tr>\n<\/table>\n<p>The honest framework: if your workload is pure inference on models that fit in 24 GB and you value lower cost, lower power, and a card you can buy new, the 7900 XTX is the rational pick. Pay the 4090 premium when you specifically need its mature CUDA ecosystem, faster training, or the broadest software compatibility out of the box.<\/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 RX 7900 XTX good for AI in 2026?<\/h3>\n<p>Yes, for inference. With llama.cpp or Ollama on Linux it delivers strong tokens-per-dollar. For training, fine-tuning, or Stable Diffusion, the ROCm software gap makes it noticeably slower and more fragile than an RTX 4090.<\/p>\n<h3>Does ROCm finally match CUDA?<\/h3>\n<p>No, but it has closed the gap meaningfully. ROCm is solid for mainstream inference. It still trails CUDA in library coverage, training optimization, and Windows support. CUDA remains the path of least resistance.<\/p>\n<h3>Is the RX 7900 XTX faster than the RTX 4090?<\/h3>\n<p>No. Despite similar VRAM and bandwidth, the RTX 4090 is roughly 1.5\u20131.8x faster in real AI workloads because of CUDA&#8217;s software maturity. The gap is smallest in llama.cpp and largest in Stable Diffusion.<\/p>\n<h3>Should I buy AMD to save money on a local LLM rig?<\/h3>\n<p>Only if you run inference and use Linux. The 7900 XTX gives you 24 GB for ~$999. But factor in your own time \u2014 ROCm setup and troubleshooting have a real cost that the price tag does not show.<\/p>\n<h3>What size LLM can the RX 7900 XTX and RTX 4090 run?<\/h3>\n<p>Both have 24 GB of VRAM, so they share the same ceiling. At Q4 quantization, that comfortably fits 27B-to-32B-class models with usable context, which covers the vast majority of local AI tasks. A 70B model will not fit natively on either card; you would need to offload layers to system RAM (slow) or run two 24 GB cards. The RTX 4090 is faster, but it does not let you run a larger model than the 7900 XTX.<\/p>\n<h3>What power supply do I need for the RX 7900 XTX or RTX 4090?<\/h3>\n<p>Plan for at least an 850W unit from a reputable brand for either card. Both draw sharp transient spikes well above their rated TDP for fractions of a second, so a marginal PSU can trip protections under load. If you pair the GPU with a high-end CPU, or build a dual-GPU rig, step up to 1000W or more. The 7900 XTX&#8217;s lower 355W draw gives you slightly more headroom, but it is not a reason to skimp on the power supply.<\/p>\n<h3>Is it safe to buy a used RTX 4090 for AI in 2026?<\/h3>\n<p>It can be, but buy carefully because the 4090 is discontinued and the market is dominated by used cards. Many were run hard for mining or AI workloads, so favor sellers with proof of purchase, test the card under sustained load before the return window closes, and inspect the 12VHPWR power connector and its socket for any melting, warping, or discoloration. If the used 4090 price approaches a new card with comparable VRAM, the value case weakens quickly versus a new RX 7900 XTX.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Verdict\"><\/span>Verdict<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Il <strong>RX 7900 XTX<\/strong> is the most genuinely competitive AMD has been for AI in years \u2014 24 GB of VRAM at $999 is a real offer, and for llama.cpp inference on Linux it earns its place. But the <strong>RTX 4090<\/strong> wins this comparison clearly. It is faster, it is universal, and it removes an entire category of software friction. Choose AMD with eyes open: you are buying VRAM-per-dollar and accepting a software tax. Choose NVIDIA and you are buying speed, breadth, and the freedom to never think about your toolchain again.<\/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\/glm-5-2-vs-kimi-k2-7-for-coding-2026\/\">GLM 5.2 contro Kimi K2.7 Code: quale codificatore open source vince?<\/a><\/li>\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\/a100-vs-h100-for-ai\/\">NVIDIA A100 vs H100 for AI in 2026: Still Worth Renting the A100?<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/it\/h100-vs-h200-for-ai\/\">NVIDIA H100 vs H200 for AI in 2026: Is the Memory Upgrade Worth It?<\/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<li><a href=\"https:\/\/convly.ai\/it\/rtx-4060-ti-16gb-vs-rtx-3060-12gb-for-ai\/\">RTX 4060 Ti 16GB vs RTX 3060 12GB for AI in 2026: Best Budget GPU?<\/a><\/li>\n<\/ul>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>The RX 7900 XTX matches the RTX 4090 on VRAM and undercuts it on price. The catch is software: ROCm versus CUDA. Here&#8217;s where AMD&#8217;s flagship genuinely competes \u2014 and where it still doesn&#8217;t.<\/p>","protected":false},"author":1,"featured_media":674,"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":[292,254,256,291,280,294],"class_list":["post-662","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-comparisons","tag-amd-ai","tag-cuda","tag-local-llm","tag-rocm","tag-rtx-4090","tag-rx-7900-xtx"],"_links":{"self":[{"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/posts\/662","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=662"}],"version-history":[{"count":5,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/posts\/662\/revisions"}],"predecessor-version":[{"id":1208,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/posts\/662\/revisions\/1208"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/media\/674"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/media?parent=662"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/categories?post=662"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/tags?post=662"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}