{"id":18976,"date":"2026-09-16T20:14:26","date_gmt":"2026-09-16T13:14:26","guid":{"rendered":"https:\/\/droneth.or.th\/product\/nvidia-quadro-rtx8000-passive\/"},"modified":"2026-09-16T20:14:26","modified_gmt":"2026-09-16T13:14:26","slug":"nvidia-quadro-rtx8000-passive","status":"publish","type":"product","link":"https:\/\/droneth.or.th\/zh\/product\/nvidia-quadro-rtx8000-passive\/","title":{"rendered":"NVIDIA Quadro RTX8000 Passive"},"content":{"rendered":"<p><strong>NVIDIA Quadro RTX8000 Passive<\/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>\u0e04\u0e38\u0e13\u0e2a\u0e21\u0e1a\u0e31\u0e15\u0e34 (\u0e44\u0e17\u0e22)<\/strong><\/td>\n<td>Feature (English)<\/td>\n<\/tr>\n<tr>\n<td><strong>\u0e2a\u0e16\u0e32\u0e1b\u0e31\u0e15\u0e22\u0e01\u0e23\u0e23\u0e21 GPU: Turing<\/strong><\/td>\n<td>GPU Architecture: Turing<\/td>\n<\/tr>\n<tr>\n<td><strong>CUDA Cores: 4608<\/strong><\/td>\n<td>CUDA Cores: 4608<\/td>\n<\/tr>\n<tr>\n<td><strong>Tensor Cores: 576<\/strong><\/td>\n<td>Tensor Cores: 576<\/td>\n<\/tr>\n<tr>\n<td><strong>RT Cores: 72<\/strong><\/td>\n<td>RT Cores: 72<\/td>\n<\/tr>\n<tr>\n<td><strong>\u0e2b\u0e19\u0e48\u0e27\u0e22\u0e04\u0e27\u0e32\u0e21\u0e08\u0e4d\u0e32: 48 GB GDDR6 \u0e1e\u0e23\u0e49\u0e2d\u0e21 ECC<\/strong><\/td>\n<td>GPU Memory: 48 GB GDDR6 with ECC<\/td>\n<\/tr>\n<tr>\n<td><strong>Memory Bandwidth: \u0e2a\u0e39\u0e07\u0e2a\u0e38\u0e14 624 GB\/s<\/strong><\/td>\n<td>Memory Bandwidth: up to 624 GB\/s<\/td>\n<\/tr>\n<tr>\n<td><strong>FP32: ~14.9 TFLOPS<\/strong><\/td>\n<td>FP32 Performance: ~14.9 TFLOPS<\/td>\n<\/tr>\n<tr>\n<td><strong>FP16: ~29.9 TFLOPS<\/strong><\/td>\n<td>FP16 Performance: ~29.9 TFLOPS<\/td>\n<\/tr>\n<tr>\n<td><strong>INT8: ~238.9 TOPS<\/strong><\/td>\n<td>INT8 Performance: ~238.9 TOPS<\/td>\n<\/tr>\n<tr>\n<td><strong>Tensor TFLOPS: ~119.4<\/strong><\/td>\n<td>Deep Learning Tensor TFLOPS: ~119.4<\/td>\n<\/tr>\n<tr>\n<td><strong>RTX-OPS: ~80T; Ray Casting: 10 Giga Rays\/s<\/strong><\/td>\n<td>RTX-OPS: ~80T; Rays Cast: 10 Giga Rays\/sec<\/td>\n<\/tr>\n<tr>\n<td><strong>\u0e01\u0e32\u0e23\u0e40\u0e0a\u0e37\u0e48\u0e2d\u0e21\u0e15\u0e48\u0e2d: PCIe 3.0 x16<\/strong><\/td>\n<td>System Interface: PCI Express 3.0 x16<\/td>\n<\/tr>\n<tr>\n<td><strong>\u0e01\u0e32\u0e23\u0e23\u0e30\u0e1a\u0e32\u0e22\u0e04\u0e27\u0e32\u0e21\u0e23\u0e49\u0e2d\u0e19: Passive Heatsink<\/strong><\/td>\n<td>Thermal Solution: Passive Heatsink<\/td>\n<\/tr>\n<tr>\n<td><strong>\u0e01\u0e4d\u0e32\u0e25\u0e31\u0e07\u0e2a\u0e39\u0e07\u0e2a\u0e38\u0e14: ~250 W<\/strong><\/td>\n<td>Max Power Consumption: ~250 W<\/td>\n<\/tr>\n<tr>\n<td><strong>\u0e02\u0e19\u0e32\u0e14\u0e01\u0e32\u0e23\u0e4c\u0e14: Dual-slot, \u0e02\u0e19\u0e32\u0e14\u0e1b\u0e23\u0e30\u0e21\u0e32\u0e13 4.4\u2032\u2032 x 10.5\u2032\u2032<\/strong><\/td>\n<td>Form Factor: Dual-slot, approx. 4.4\u2032\u2032 H \u00d7 10.5\u2032\u2032 L<\/td>\n<\/tr>\n<tr>\n<td><strong>\u0e23\u0e2d\u0e07\u0e23\u0e31\u0e1a NVLink: 100 GB\/s<\/strong><\/td>\n<td>NVLink Interconnect: 100 GB\/s<\/td>\n<\/tr>\n<tr>\n<td><strong>\u0e23\u0e2d\u0e07\u0e23\u0e31\u0e1a Quadro vDWS \u0e41\u0e25\u0e30 display virtualization<\/strong><\/td>\n<td>Supports Quadro vDWS and virtual display outputs<\/td>\n<\/tr>\n<tr>\n<td><strong>Technical detail<\/strong><\/td>\n<td>Turing GPU<\/td>\n<\/tr>\n<tr>\n<td><strong>Technical detail<\/strong><\/td>\n<td>4,608 NVIDIA CUDA Cores<\/td>\n<\/tr>\n<tr>\n<td><strong>Technical detail<\/strong><\/td>\n<td>576 NVIDIA Tensor Cores<\/td>\n<\/tr>\n<tr>\n<td><strong>Technical detail<\/strong><\/td>\n<td>72 NVIDIA RT Cores<\/td>\n<\/tr>\n<tr>\n<td><strong>Technical detail<\/strong><\/td>\n<td>48GB GDDR6 Memory<\/td>\n<\/tr>\n<tr>\n<td><strong>Technical detail<\/strong><\/td>\n<td>Up to 624GB\/s Memory Bandwidth<\/td>\n<\/tr>\n<tr>\n<td><strong>Technical detail<\/strong><\/td>\n<td>14.9 TFLOPS FP32 Performance<\/td>\n<\/tr>\n<tr>\n<td><strong>Technical detail<\/strong><\/td>\n<td>29.9 TFLOPS FP16 Performance<\/td>\n<\/tr>\n<tr>\n<td><strong>Technical detail<\/strong><\/td>\n<td>238.9 TOPS INT8 Performance<\/td>\n<\/tr>\n<tr>\n<td><strong>Technical detail<\/strong><\/td>\n<td>119.4 TFLOPS of Tensor Operation<\/td>\n<\/tr>\n<tr>\n<td><strong>Max. Power Consumption<\/strong><\/td>\n<td>250W<\/td>\n<\/tr>\n<tr>\n<td><strong>Technical detail<\/strong><\/td>\n<td>Support Quadro vDWS<\/td>\n<\/tr>\n<tr>\n<td><strong>Max. Virtual Display Head Resolution<\/strong><\/td>\n<td>4096 x 2160<\/td>\n<\/tr>\n<tr>\n<td><strong>Technical detail<\/strong><\/td>\n<td>NVIDIA Quadro RTX 8000 Passive \u0e04\u0e37\u0e2d\u0e01\u0e23\u0e32\u0e1f\u0e34\u0e01\u0e01\u0e32\u0e23\u0e4c\u0e14\u0e23\u0e30\u0e14\u0e31\u0e1a\u0e21\u0e37\u0e2d\u0e2d\u0e32\u0e0a\u0e35\u0e1e\u0e17\u0e35\u0e48\u0e43\u0e0a\u0e49\u0e2a\u0e16\u0e32\u0e1b\u0e31\u0e15\u0e22\u0e01\u0e23\u0e23\u0e21 Turing \u0e41\u0e25\u0e30\u0e21\u0e35\u0e23\u0e30\u0e1a\u0e1a\u0e23\u0e30\u0e1a\u0e32\u0e22\u0e04\u0e27\u0e32\u0e21\u0e23\u0e49\u0e2d\u0e19\u0e41\u0e1a\u0e1a\u0e1e\u0e32\u0e2a\u0e0b\u0e35\u0e1f (\u0e44\u0e21\u0e48\u0e21\u0e35\u0e1e\u0e31\u0e14\u0e25\u0e21) \u0e23\u0e38\u0e48\u0e19\u0e19\u0e35\u0e49\u0e2d\u0e2d\u0e01\u0e41\u0e1a\u0e1a\u0e21\u0e32\u0e40\u0e1e\u0e37\u0e48\u0e2d\u0e43\u0e0a\u0e49\u0e07\u0e32\u0e19\u0e43\u0e19\u0e28\u0e39\u0e19\u0e22\u0e4c\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e2b\u0e23\u0e37\u0e2d\u0e40\u0e0b\u0e34\u0e23\u0e4c\u0e1f\u0e40\u0e27\u0e2d\u0e23\u0e4c \u0e0b\u0e36\u0e48\u0e07\u0e15\u0e49\u0e2d\u0e07\u0e01\u0e32\u0e23\u0e1b\u0e23\u0e30\u0e2a\u0e34\u0e17\u0e18\u0e34\u0e20\u0e32\u0e1e\u0e2a\u0e39\u0e07\u0e41\u0e15\u0e48\u0e23\u0e1a\u0e01\u0e27\u0e19\u0e40\u0e2a\u0e35\u0e22\u0e07\u0e15\u0e48\u0e4d\u0e32 \u0e23\u0e2d\u0e07\u0e23\u0e31\u0e1a\u0e07\u0e32\u0e19\u0e14\u0e49\u0e32\u0e19 AI, \u0e01\u0e32\u0e23\u0e08\u0e4d\u0e32\u0e25\u0e2d\u0e07\u0e20\u0e32\u0e1e (rendering), \u0e41\u0e25\u0e30 ray tracing \u0e41\u0e1a\u0e1a\u0e40\u0e23\u0e35\u0e22\u0e25\u0e44\u0e17\u0e21\u0e4c \u0e42\u0e14\u0e22\u0e23\u0e2d\u0e07\u0e23\u0e31\u0e1a CUDA, Tensor \u0e41\u0e25\u0e30 RT Cores \u0e43\u0e19\u0e2a\u0e20\u0e32\u0e1e\u0e41\u0e27\u0e14\u0e25\u0e49\u0e2d\u0e21\u0e17\u0e35\u0e48\u0e15\u0e49\u0e2d\u0e07\u0e01\u0e32\u0e23\u0e04\u0e27\u0e32\u0e21\u0e17\u0e19\u0e17\u0e32\u0e19\u0e2a\u0e39\u0e07<\/td>\n<\/tr>\n<tr>\n<td><strong>Technical detail<\/strong><\/td>\n<td>The NVIDIA Quadro RTX 8000 Passive is a professional-grade graphics card built on the Turing architecture with a passive cooling design (fan-less). It\u2019s purpose-built for data center and server environments that require high performance with minimal acoustic interference. It excels in workloads such as AI, rendering, and real-time ray tracing by leveraging CUDA, Tensor, and RT cores, making it ideal for demanding, resilient deployments.<\/td>\n<\/tr>\n<tr>\n<td><strong>Technical detail<\/strong><\/td>\n<td>The Quadro RTX 8000 Passive stands out as a high-end GPU tailored for data center deployment. It offers formidable processing capabilities through its CUDA, Tensor, and RT cores, along with a massive 48 GB of ECC GDDR6 memory and NVLink support for scalable performance. Despite its fan-less design, it brilliantly handles intensive workloads like professional rendering, AI computation, and real-time ray tracing. Balancing silent operation with reliability and computational power, it\u2019s an excellent choice for environments where stability and acoustics matter most.<\/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 Quadro RTX8000 Passive<\/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":19221,"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-18976","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\/18976","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=18976"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/droneth.or.th\/zh\/wp-json\/wp\/v2\/media\/19221"}],"wp:attachment":[{"href":"https:\/\/droneth.or.th\/zh\/wp-json\/wp\/v2\/media?parent=18976"}],"wp:term":[{"taxonomy":"product_brand","embeddable":true,"href":"https:\/\/droneth.or.th\/zh\/wp-json\/wp\/v2\/product_brand?post=18976"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/droneth.or.th\/zh\/wp-json\/wp\/v2\/product_cat?post=18976"},{"taxonomy":"product_tag","embeddable":true,"href":"https:\/\/droneth.or.th\/zh\/wp-json\/wp\/v2\/product_tag?post=18976"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}