{"id":18979,"date":"2026-09-16T20:14:26","date_gmt":"2026-09-16T13:14:26","guid":{"rendered":"https:\/\/droneth.or.th\/product\/nvidia-rtx-4000-sff-ada-generation\/"},"modified":"2026-09-16T20:14:26","modified_gmt":"2026-09-16T13:14:26","slug":"nvidia-rtx-4000-sff-ada-generation","status":"publish","type":"product","link":"https:\/\/droneth.or.th\/zh\/product\/nvidia-rtx-4000-sff-ada-generation\/","title":{"rendered":"NVIDIA RTX 4000 SFF Ada Generation"},"content":{"rendered":"<p><strong>NVIDIA RTX 4000 SFF Ada Generation<\/strong> \u0e40\u0e1b\u0e47\u0e19 Professional GPU \u0e08\u0e32\u0e01 <strong>NVIDIA<\/strong> \u0e40\u0e2b\u0e21\u0e32\u0e30\u0e2a\u0e33\u0e2b\u0e23\u0e31\u0e1a\u0e07\u0e32\u0e19 professional visualization, AI inference, CAD, content creation \u0e23\u0e2d\u0e07\u0e23\u0e31\u0e1a\u0e01\u0e32\u0e23\u0e43\u0e0a\u0e49\u0e07\u0e32\u0e19\u0e23\u0e30\u0e14\u0e31\u0e1a\u0e2d\u0e07\u0e04\u0e4c\u0e01\u0e23 \u0e28\u0e39\u0e19\u0e22\u0e4c\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25 \u0e41\u0e25\u0e30\u0e07\u0e32\u0e19\u0e27\u0e34\u0e08\u0e31\u0e22\u0e14\u0e49\u0e32\u0e19 AI\/HPC<\/p>\n<p><strong>\u0e01\u0e32\u0e23\u0e43\u0e0a\u0e49\u0e07\u0e32\u0e19\u0e17\u0e35\u0e48\u0e40\u0e2b\u0e21\u0e32\u0e30\u0e2a\u0e21<\/strong><\/p>\n<ul>\n<li>professional visualization<\/li>\n<li>AI inference<\/li>\n<li>CAD<\/li>\n<li>content creation<\/li>\n<\/ul>\n<p><strong>\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e17\u0e32\u0e07\u0e40\u0e17\u0e04\u0e19\u0e34\u0e04 (Product Specifications)<\/strong><\/p>\n<table style=\"width:100%\">\n<thead>\n<tr>\n<td><strong>\u90e8\u5206<\/strong><\/td>\n<td><strong>\u0e23\u0e32\u0e22\u0e25\u0e30\u0e40\u0e2d\u0e35\u0e22\u0e14\u0e2a\u0e40\u0e1b\u0e04<\/strong><\/td>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>\u0e23\u0e32\u0e22\u0e01\u0e32\u0e23 (Specification)<\/strong><\/td>\n<td>\u0e23\u0e32\u0e22\u0e25\u0e30\u0e40\u0e2d\u0e35\u0e22\u0e14 (\u0e44\u0e17\u0e22) | Description (EN)<\/td>\n<\/tr>\n<tr>\n<td><strong>\u0e2a\u0e16\u0e32\u0e1b\u0e31\u0e15\u0e22\u0e01\u0e23\u0e23\u0e21 (Architecture)<\/strong><\/td>\n<td>Ada Lovelace | Ada Lovelace<\/td>\n<\/tr>\n<tr>\n<td><strong>CUDA\u6838\u5fc3<\/strong><\/td>\n<td>6,144 | 6,144<\/td>\n<\/tr>\n<tr>\n<td><strong>\u5149\u7ebf\u8ffd\u8e2a\u6838\u5fc3<\/strong><\/td>\n<td>48 (3rd-gen) | 48 (3rd-gen)<\/td>\n<\/tr>\n<tr>\n<td><strong>\u5f20\u91cf\u6838\u5fc3<\/strong><\/td>\n<td>192 (4th-gen) | 192 (4th-gen)<\/td>\n<\/tr>\n<tr>\n<td><strong>FP32 Performance<\/strong><\/td>\n<td>19.2 TFLOPS | 19.2 TFLOPS<\/td>\n<\/tr>\n<tr>\n<td><strong>RT Core Performance<\/strong><\/td>\n<td>44.3 TFLOPS | 44.3 TFLOPS<\/td>\n<\/tr>\n<tr>\n<td><strong>\u5f35\u91cf\u6027\u80fd<\/strong><\/td>\n<td>306.8 TFLOPS | 306.8 TFLOPS<\/td>\n<\/tr>\n<tr>\n<td><strong>\u0e2b\u0e19\u0e48\u0e27\u0e22\u0e04\u0e27\u0e32\u0e21\u0e08\u0e4d\u0e32 (Memory)<\/strong><\/td>\n<td>20 GB GDDR6 ECC | 20 GB GDDR6 ECC<\/td>\n<\/tr>\n<tr>\n<td><strong>Memory Interface \/ Bandwidth<\/strong><\/td>\n<td>160\u2010bit, ~320 GB\/s (\u0e1a\u0e32\u0e07\u0e41\u0e2b\u0e25\u0e48\u0e07\u0e41\u0e08\u0e49\u0e07 280) | 160\u2010bit, ~320 GB\/s (some list 280)<\/td>\n<\/tr>\n<tr>\n<td><strong>TDP \/ \u0e43\u0e0a\u0e49\u0e1e\u0e25\u0e31\u0e07\u0e07\u0e32\u0e19<\/strong><\/td>\n<td>70 W (\u0e44\u0e21\u0e48\u0e15\u0e49\u0e2d\u0e07\u0e43\u0e0a\u0e49\u0e1e\u0e25\u0e31\u0e07\u0e07\u0e32\u0e19\u0e40\u0e2a\u0e23\u0e34\u0e21) | 70 W (No auxiliary power required)<\/td>\n<\/tr>\n<tr>\n<td><strong>Interface<\/strong><\/td>\n<td>PCIe 4.0 x16 | PCIe 4.0 x16<\/td>\n<\/tr>\n<tr>\n<td><strong>Display Outputs<\/strong><\/td>\n<td>4\u00d7 mini DisplayPort 1.4a | 4\u00d7 mini DisplayPort 1.4a<\/td>\n<\/tr>\n<tr>\n<td><strong>\u0e1f\u0e2d\u0e23\u0e4c\u0e21\u0e41\u0e1f\u0e04\u0e40\u0e15\u0e2d\u0e23\u0e4c (Form Factor)<\/strong><\/td>\n<td>2.7\u2032\u2032 \u00d7 6.6\u2032\u2032, dual-slot, low-profile | 2.7\u2032\u2032 \u00d7 6.6\u2032\u2032, dual-slot, low-profile<\/td>\n<\/tr>\n<tr>\n<td><strong>\u0e23\u0e30\u0e1a\u0e1a\u0e23\u0e30\u0e1a\u0e32\u0e22\u0e04\u0e27\u0e32\u0e21\u0e23\u0e49\u0e2d\u0e19<\/strong><\/td>\n<td>Active Fansink | Active Fansink<\/td>\n<\/tr>\n<tr>\n<td><strong>\u652f\u6301<\/strong><\/td>\n<td>VR\/XR, Frame Lock, Quadro Sync II | VR\/XR, Frame Lock, Quadro Sync II<\/td>\n<\/tr>\n<tr>\n<td><strong>NVENC \/ NVDEC<\/strong><\/td>\n<td>2\u00d7 (\u0e23\u0e27\u0e21 AV1 encode\/decode) | 2\u00d7 (incl. AV1 encode\/decode)<\/td>\n<\/tr>\n<tr>\n<td><strong>Technical detail<\/strong><\/td>\n<td>Ada Lovelace GPU architecture<\/td>\n<\/tr>\n<tr>\n<td><strong>Technical detail<\/strong><\/td>\n<td>6,144 CUDA Cores<\/td>\n<\/tr>\n<tr>\n<td><strong>Technical detail<\/strong><\/td>\n<td>192 Tensor Cores<\/td>\n<\/tr>\n<tr>\n<td><strong>Technical detail<\/strong><\/td>\n<td>48 RT Cores<\/td>\n<\/tr>\n<tr>\n<td><strong>Technical detail<\/strong><\/td>\n<td>20GB GDDR6 Memory with ECC<\/td>\n<\/tr>\n<tr>\n<td><strong>\u5185\u5b58\u5e26\u5bbd<\/strong><\/td>\n<td>280 GB\/s<\/td>\n<\/tr>\n<tr>\n<td><strong>Max. Power Consumption<\/strong><\/td>\n<td>70W<\/td>\n<\/tr>\n<tr>\n<td><strong>Graphics Bus<\/strong><\/td>\n<td>PCI-E 4.0 x16<\/td>\n<\/tr>\n<tr>\n<td><strong>\u663e\u793a\u63a5\u53e3<\/strong><\/td>\n<td>mDP 1.4a (4)<\/td>\n<\/tr>\n<tr>\n<td><strong>Technical detail<\/strong><\/td>\n<td>NVIDIA RTX 4000 SFF Ada Generation \u0e04\u0e37\u0e2d\u0e01\u0e32\u0e23\u0e4c\u0e14\u0e01\u0e23\u0e32\u0e1f\u0e34\u0e01\u0e40\u0e27\u0e34\u0e23\u0e4c\u0e01\u0e2a\u0e40\u0e15\u0e0a\u0e31\u0e19\u0e02\u0e19\u0e32\u0e14\u0e01\u0e30\u0e17\u0e31\u0e14\u0e23\u0e31\u0e14 (Small Form Factor) \u0e17\u0e35\u0e48\u0e43\u0e0a\u0e49\u0e2a\u0e16\u0e32\u0e1b\u0e31\u0e15\u0e22\u0e01\u0e23\u0e23\u0e21 Ada Lovelace \u0e43\u0e2b\u0e21\u0e48\u0e25\u0e48\u0e32\u0e2a\u0e38\u0e14 \u0e1c\u0e2a\u0e32\u0e19\u0e40\u0e17\u0e04\u0e42\u0e19\u0e42\u0e25\u0e22\u0e35\u0e23\u0e30\u0e14\u0e31\u0e1a\u0e2a\u0e39\u0e07\u0e17\u0e31\u0e49\u0e07 CUDA, RT Core \u0e41\u0e25\u0e30 Tensor Core \u0e1e\u0e23\u0e49\u0e2d\u0e21\u0e2b\u0e19\u0e48\u0e27\u0e22\u0e04\u0e27\u0e32\u0e21\u0e08\u0e4d\u0e32 20 GB GDDR6 ECC \u0e43\u0e2b\u0e49\u0e1e\u0e25\u0e31\u0e07\u0e43\u0e19\u0e01\u0e32\u0e23\u0e40\u0e23\u0e19\u0e40\u0e14\u0e2d\u0e23\u0e4c AI \u0e41\u0e25\u0e30\u0e01\u0e23\u0e32\u0e1f\u0e34\u0e01\u0e17\u0e35\u0e48\u0e40\u0e2b\u0e19\u0e37\u0e2d\u0e0a\u0e31\u0e49\u0e19\u0e43\u0e19\u0e23\u0e39\u0e1b\u0e41\u0e1a\u0e1a\u0e17\u0e35\u0e48\u0e1b\u0e23\u0e30\u0e2b\u0e22\u0e31\u0e14\u0e1e\u0e37\u0e49\u0e19\u0e17\u0e35\u0e48\u0e41\u0e25\u0e30\u0e43\u0e0a\u0e49\u0e44\u0e1f\u0e40\u0e1e\u0e35\u0e22\u0e07 70 W \u0e40\u0e17\u0e48\u0e32\u0e19\u0e31\u0e49\u0e19<\/td>\n<\/tr>\n<tr>\n<td><strong>Technical detail<\/strong><\/td>\n<td>The NVIDIA RTX 4000 SFF Ada Generation is a compact (Small Form Factor) workstation GPU built on the Ada Lovelace architecture. It combines advanced CUDA, RT, and Tensor Cores with 20 GB of ECC-enabled GDDR6 memory, delivering high-performance graphics, rendering, and AI capabilities while maintaining a compact design and a low 70 W power envelope<\/td>\n<\/tr>\n<tr>\n<td><strong>Technical detail<\/strong><\/td>\n<td>The NVIDIA RTX 4000 SFF Ada Generation delivers high-end workstation-grade performance in a compact form factor. Built for demanding workflows such as 3D modeling, engineering, VR, and AI-driven rendering, it features 20 GB of ECC GDDR6 memory and leverages Ada Lovelace architecture with modern RT and Tensor core enhancements. Despite its power, it maintains a low 70 W consumption with efficient cooling\u2014making it the ideal choice for small form factor workstations that require uncompromised power in a compact footprint.<\/td>\n<\/tr>\n<tr>\n<td><strong>Technical detail<\/strong><\/td>\n<td>Condition &amp; Warranty<\/td>\n<\/tr>\n<tr>\n<td><strong>Technical detail<\/strong><\/td>\n<td>\u2714 Fully tested and 100% working \u2714 Secure packaging for safe shipping \u2714 Warranty available (subject to store policy)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u0e2a\u0e19\u0e43\u0e08\u0e2a\u0e2d\u0e1a\u0e16\u0e32\u0e21\u0e23\u0e32\u0e04\u0e32 \u0e2a\u0e40\u0e1b\u0e04 \u0e2b\u0e23\u0e37\u0e2d\u0e02\u0e2d\u0e43\u0e1a\u0e40\u0e2a\u0e19\u0e2d\u0e23\u0e32\u0e04\u0e32 \u0e15\u0e34\u0e14\u0e15\u0e48\u0e2d\u0e44\u0e14\u0e49\u0e17\u0e35\u0e48 <strong>LINE: @metaxr<\/strong><\/p>","protected":false},"excerpt":{"rendered":"<p><strong>NVIDIA RTX 4000 SFF Ada Generation<\/strong> \u0e40\u0e1b\u0e47\u0e19 Professional GPU \u0e08\u0e32\u0e01 <strong>NVIDIA<\/strong> \u0e40\u0e2b\u0e21\u0e32\u0e30\u0e2a\u0e33\u0e2b\u0e23\u0e31\u0e1a\u0e07\u0e32\u0e19 professional visualization, AI inference, CAD, content creation \u0e23\u0e2d\u0e07\u0e23\u0e31\u0e1a\u0e01\u0e32\u0e23\u0e43\u0e0a\u0e49\u0e07\u0e32\u0e19\u0e23\u0e30\u0e14\u0e31\u0e1a\u0e2d\u0e07\u0e04\u0e4c\u0e01\u0e23 \u0e28\u0e39\u0e19\u0e22\u0e4c\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25 \u0e41\u0e25\u0e30\u0e07\u0e32\u0e19\u0e27\u0e34\u0e08\u0e31\u0e22\u0e14\u0e49\u0e32\u0e19 AI\/HPC<\/p>\n<div style=\"width: 100%; text-align: center; padding: 24px 0;\"><a style=\"display: inline-block; background-color: #06c755; color: #ffffff; font-size: 16px; font-weight: bold; text-decoration: none; padding: 14px 32px; border-radius: 50px; box-shadow: 0 4px 12px rgba(6,199,85,0.4); white-space: nowrap;\" href=\"https:\/\/page.line.me\/682mqqmh?openQrModal=true\" target=\"_blank\" rel=\"noopener\">\u0e2a\u0e2d\u0e1a\u0e16\u0e32\u0e21\u0e23\u0e32\u0e04\u0e32 \/ \u0e2a\u0e31\u0e48\u0e07\u0e0b\u0e37\u0e49\u0e2d LINE: @metaxr<\/a><\/div>","protected":false},"featured_media":19224,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"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-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":"","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-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":"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":""},"mobile":{"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":""}}},"product_brand":[],"product_cat":[392,384],"product_tag":[237,397,396],"class_list":{"0":"post-18979","1":"product","2":"type-product","3":"status-publish","4":"has-post-thumbnail","6":"product_cat-professional-gpu","7":"product_cat-gpu-ai-server","8":"product_tag-ai-","9":"product_tag-gpu","10":"product_tag-nvidia","11":"desktop-align-left","12":"tablet-align-left","13":"mobile-align-left","15":"first","16":"instock","17":"shipping-taxable","18":"product-type-simple"},"acf":[],"_links":{"self":[{"href":"https:\/\/droneth.or.th\/zh\/wp-json\/wp\/v2\/product\/18979","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/droneth.or.th\/zh\/wp-json\/wp\/v2\/product"}],"about":[{"href":"https:\/\/droneth.or.th\/zh\/wp-json\/wp\/v2\/types\/product"}],"replies":[{"embeddable":true,"href":"https:\/\/droneth.or.th\/zh\/wp-json\/wp\/v2\/comments?post=18979"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/droneth.or.th\/zh\/wp-json\/wp\/v2\/media\/19224"}],"wp:attachment":[{"href":"https:\/\/droneth.or.th\/zh\/wp-json\/wp\/v2\/media?parent=18979"}],"wp:term":[{"taxonomy":"product_brand","embeddable":true,"href":"https:\/\/droneth.or.th\/zh\/wp-json\/wp\/v2\/product_brand?post=18979"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/droneth.or.th\/zh\/wp-json\/wp\/v2\/product_cat?post=18979"},{"taxonomy":"product_tag","embeddable":true,"href":"https:\/\/droneth.or.th\/zh\/wp-json\/wp\/v2\/product_tag?post=18979"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}