{"id":18687,"date":"2026-09-16T20:11:08","date_gmt":"2026-09-16T13:11:08","guid":{"rendered":"https:\/\/droneth.or.th\/product\/nvidia-a30\/"},"modified":"2026-09-16T20:11:08","modified_gmt":"2026-09-16T13:11:08","slug":"nvidia-a30","status":"publish","type":"product","link":"https:\/\/droneth.or.th\/ko\/product\/nvidia-a30\/","title":{"rendered":"NVIDIA A30"},"content":{"rendered":"<p><strong>NVIDIA A30<\/strong> \u0e40\u0e1b\u0e47\u0e19 Data Center GPU \/ Accelerator \u0e08\u0e32\u0e01 <strong>NVIDIA<\/strong> \u0e40\u0e2b\u0e21\u0e32\u0e30\u0e2a\u0e33\u0e2b\u0e23\u0e31\u0e1a\u0e07\u0e32\u0e19 AI training, AI inference, HPC, virtual workstation \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>AI training<\/li>\n<li>AI inference<\/li>\n<li>HPC<\/li>\n<li>virtual workstation<\/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>\ubd80\ubd84<\/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>\u0e2a\u0e16\u0e32\u0e1b\u0e31\u0e15\u0e22\u0e01\u0e23\u0e23\u0e21 GPU<\/strong><\/td>\n<td>NVIDIA Ampere<\/td>\n<\/tr>\n<tr>\n<td><strong>CUDA \ucf54\uc5b4<\/strong><\/td>\n<td>3,804<\/td>\n<\/tr>\n<tr>\n<td><strong>Tensor Cores (Gen 3)<\/strong><\/td>\n<td>224<\/td>\n<\/tr>\n<tr>\n<td><strong>\u0e1b\u0e23\u0e30\u0e2a\u0e34\u0e17\u0e18\u0e34\u0e20\u0e32\u0e1e FP64<\/strong><\/td>\n<td>5.2 TFLOPS<\/td>\n<\/tr>\n<tr>\n<td><strong>FP64 \u0e40\u0e17\u0e19\u0e40\u0e0b\u0e2d\u0e23\u0e4c\u0e04\u0e2d\u0e23\u0e4c<\/strong><\/td>\n<td>10.3 TFLOPS<\/td>\n<\/tr>\n<tr>\n<td><strong>FP32<\/strong><\/td>\n<td>10.3 TFLOPS<\/td>\n<\/tr>\n<tr>\n<td><strong>TF32 \u0e40\u0e17\u0e19\u0e40\u0e0b\u0e2d\u0e23\u0e4c\u0e04\u0e2d\u0e23\u0e4c<\/strong><\/td>\n<td>82 TFLOPS (165 TFLOPS with sparsity)<\/td>\n<\/tr>\n<tr>\n<td><strong>BFLOAT16 \/ FP16 (Tensor)<\/strong><\/td>\n<td>165 TFLOPS (330 TFLOPS with sparsity)<\/td>\n<\/tr>\n<tr>\n<td><strong>INT8 (Tensor)<\/strong><\/td>\n<td>330 TOPS (661 TOPS with sparsity)<\/td>\n<\/tr>\n<tr>\n<td><strong>INT4 (Tensor)<\/strong><\/td>\n<td>661 TOPS (1,321 TOPS with sparsity)<\/td>\n<\/tr>\n<tr>\n<td><strong>\u0e2b\u0e19\u0e48\u0e27\u0e22\u0e04\u0e27\u0e32\u0e21\u0e08\u0e4d\u0e32 GPU<\/strong><\/td>\n<td>24 GB HBM2 (ECC)<\/td>\n<\/tr>\n<tr>\n<td><strong>\u0e41\u0e1a\u0e19\u0e14\u0e4c\u0e27\u0e34\u0e14\u0e17\u0e4c\u0e2b\u0e19\u0e48\u0e27\u0e22\u0e04\u0e27\u0e32\u0e21\u0e08\u0e4d\u0e32<\/strong><\/td>\n<td>933 GB\/s<\/td>\n<\/tr>\n<tr>\n<td><strong>\u0e01\u0e32\u0e23\u0e40\u0e0a\u0e37\u0e48\u0e2d\u0e21\u0e15\u0e48\u0e2d\u0e23\u0e30\u0e1a\u0e1a<\/strong><\/td>\n<td>PCIe Gen4 \u00d716 (64 GB\/s), \u0e23\u0e2d\u0e07\u0e23\u0e31\u0e1a NVLink (200 GB\/s, optional)<\/td>\n<\/tr>\n<tr>\n<td><strong>MIG Support<\/strong><\/td>\n<td>Up to 4 instances (6 GB each, 12 GB each, or full 24 GB)<\/td>\n<\/tr>\n<tr>\n<td><strong>vGPU Support<\/strong><\/td>\n<td>Yes (eg. NVIDIA AI Enterprise, vCS)<\/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>Passive<\/td>\n<\/tr>\n<tr>\n<td><strong>\u0e01\u0e4d\u0e32\u0e25\u0e31\u0e07\u0e44\u0e1f\u0e2a\u0e39\u0e07\u0e2a\u0e38\u0e14 (TDP)<\/strong><\/td>\n<td>165 W<\/td>\n<\/tr>\n<tr>\n<td><strong>Form Factor<\/strong><\/td>\n<td>Dual-slot, full-height, full-length PCIe card<\/td>\n<\/tr>\n<tr>\n<td><strong>Media Engines<\/strong><\/td>\n<td>1 optical flow accelerator (OFA), 1 JPEG decoder (NVJPEG), 4 video decoders (NVDEC)<\/td>\n<\/tr>\n<tr>\n<td><strong>ECC Memory<\/strong><\/td>\n<td>Yes<\/td>\n<\/tr>\n<tr>\n<td><strong>Technical detail<\/strong><\/td>\n<td>NVIDIA Ampere GPU architecture<\/td>\n<\/tr>\n<tr>\n<td><strong>Technical detail<\/strong><\/td>\n<td>24GB HBM2 Memory<\/td>\n<\/tr>\n<tr>\n<td><strong>Max. Power Consumption<\/strong><\/td>\n<td>165W<\/td>\n<\/tr>\n<tr>\n<td><strong>Interconnect Bus<\/strong><\/td>\n<td>PCIe Gen. 4: 64GB\/s 3rd NVLink: 200GB\/s<\/td>\n<\/tr>\n<tr>\n<td><strong>Multi-instance GPU (MIG)<\/strong><\/td>\n<td>4 GPU instances @ 6GB each 2 GPU instances @ 12GB each 1 GPU instance @ 24GB<\/td>\n<\/tr>\n<tr>\n<td><strong>Technical detail<\/strong><\/td>\n<td>Virtual GPU (vGPU) software support<\/td>\n<\/tr>\n<tr>\n<td><strong>Technical detail<\/strong><\/td>\n<td>NVIDIA A30 \u0e04\u0e37\u0e2d\u0e01\u0e32\u0e23\u0e4c\u0e14 GPU \u0e2a\u0e4d\u0e32\u0e2b\u0e23\u0e31\u0e1a\u0e28\u0e39\u0e19\u0e22\u0e4c\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e17\u0e35\u0e48\u0e27\u0e32\u0e07\u0e15\u0e4d\u0e32\u0e41\u0e2b\u0e19\u0e48\u0e07\u0e43\u0e19\u0e2b\u0e21\u0e27\u0e14 mainstream enterprise servers \u0e42\u0e14\u0e22\u0e43\u0e0a\u0e49\u0e2a\u0e16\u0e32\u0e1b\u0e31\u0e15\u0e22\u0e01\u0e23\u0e23\u0e21 Ampere \u0e17\u0e35\u0e48\u0e17\u0e23\u0e07\u0e1e\u0e25\u0e31\u0e07 \u0e1e\u0e23\u0e49\u0e2d\u0e21 Tensor Core \u0e23\u0e38\u0e48\u0e19\u0e17\u0e35\u0e48\u0e2a\u0e32\u0e21\u0e41\u0e25\u0e30\u0e23\u0e30\u0e1a\u0e1a Multi\u2010Instance GPU (MIG) \u0e17\u0e35\u0e48\u0e0a\u0e48\u0e27\u0e22\u0e41\u0e1a\u0e48\u0e07\u0e01\u0e32\u0e23\u0e4c\u0e14\u0e2d\u0e2d\u0e01\u0e40\u0e1b\u0e47\u0e19\u0e2b\u0e25\u0e32\u0e22\u0e2a\u0e48\u0e27\u0e19\u0e40\u0e1e\u0e37\u0e48\u0e2d\u0e23\u0e2d\u0e07\u0e23\u0e31\u0e1a\u0e01\u0e32\u0e23\u0e43\u0e0a\u0e49\u0e07\u0e32\u0e19\u0e1e\u0e23\u0e49\u0e2d\u0e21\u0e01\u0e31\u0e19\u0e2d\u0e22\u0e48\u0e32\u0e07\u0e21\u0e35\u0e1b\u0e23\u0e30\u0e2a\u0e34\u0e17\u0e18\u0e34\u0e20\u0e32\u0e1e \u0e40\u0e2b\u0e21\u0e32\u0e30\u0e2d\u0e22\u0e48\u0e32\u0e07\u0e22\u0e34\u0e48\u0e07\u0e2a\u0e4d\u0e32\u0e2b\u0e23\u0e31\u0e1a\u0e07\u0e32\u0e19\u0e1d\u0e36\u0e01\u0e42\u0e21\u0e40\u0e14\u0e25 AI, \u0e01\u0e32\u0e23\u0e1b\u0e23\u0e30\u0e21\u0e27\u0e25\u0e1c\u0e25\u0e1b\u0e23\u0e30\u0e2a\u0e34\u0e17\u0e18\u0e34\u0e20\u0e32\u0e1e\u0e2a\u0e39\u0e07 (HPC) \u0e41\u0e25\u0e30\u0e01\u0e32\u0e23\u0e43\u0e0a\u0e49\u0e07\u0e32\u0e19 AI inference \u0e02\u0e19\u0e32\u0e14\u0e43\u0e2b\u0e0d\u0e48\u0e17\u0e35\u0e48\u0e15\u0e49\u0e2d\u0e07\u0e01\u0e32\u0e23\u0e04\u0e27\u0e32\u0e21\u0e17\u0e19\u0e17\u0e32\u0e19\u0e41\u0e25\u0e30\u0e1b\u0e23\u0e30\u0e2a\u0e34\u0e17\u0e18\u0e34\u0e20\u0e32\u0e1e\u0e2a\u0e39\u0e07\u0e43\u0e19\u0e2a\u0e20\u0e32\u0e1e\u0e41\u0e27\u0e14\u0e25\u0e49\u0e2d\u0e21\u0e01\u0e32\u0e23\u0e17\u0e4d\u0e32\u0e07\u0e32\u0e19\u0e40\u0e0a\u0e34\u0e07\u0e2d\u0e07\u0e04\u0e4c\u0e01\u0e23<\/td>\n<\/tr>\n<tr>\n<td><strong>Technical detail<\/strong><\/td>\n<td>NVIDIA A30 \u0e1b\u0e23\u0e30\u0e01\u0e2d\u0e1a\u0e14\u0e49\u0e27\u0e22 3,804 CUDA Cores \u0e41\u0e25\u0e30 224 Tensor Cores \u0e23\u0e38\u0e48\u0e19\u0e17\u0e35\u0e48 3 \u0e23\u0e2d\u0e07\u0e23\u0e31\u0e1a\u0e2b\u0e25\u0e32\u0e22\u0e04\u0e27\u0e32\u0e21\u0e41\u0e21\u0e48\u0e19\u0e22\u0e4d\u0e32\u0e17\u0e32\u0e07\u0e04\u0e13\u0e34\u0e15\u0e28\u0e32\u0e2a\u0e15\u0e23\u0e4c \u0e40\u0e0a\u0e48\u0e19 FP64, TF32, FP16, BFLOAT16, INT8 \u0e41\u0e25\u0e30 INT4 \u0e42\u0e14\u0e22\u0e43\u0e2b\u0e49\u0e1b\u0e23\u0e30\u0e2a\u0e34\u0e17\u0e18\u0e34\u0e20\u0e32\u0e1e\u0e2a\u0e39\u0e07\u0e2a\u0e38\u0e14\u0e43\u0e19 Tensor Core (\u0e40\u0e0a\u0e48\u0e19 TF32 \u0e2a\u0e39\u0e07\u0e16\u0e36\u0e07 165 TFLOPS \u0e01\u0e31\u0e1a sparsity) \u0e21\u0e32\u0e1e\u0e23\u0e49\u0e2d\u0e21\u0e2b\u0e19\u0e48\u0e27\u0e22\u0e04\u0e27\u0e32\u0e21\u0e08\u0e4d\u0e32 HBM2 \u0e02\u0e19\u0e32\u0e14 24 GB \u0e41\u0e1a\u0e19\u0e14\u0e4c\u0e27\u0e34\u0e14\u0e17\u0e4c\u0e16\u0e36\u0e07 933 GB\/s \u0e23\u0e2d\u0e07\u0e23\u0e31\u0e1a MIG \u0e41\u0e22\u0e01\u0e01\u0e32\u0e23\u0e4c\u0e14\u0e40\u0e1b\u0e47\u0e19 4 \u0e2a\u0e48\u0e27\u0e19 \u0e23\u0e30\u0e1a\u0e1a\u0e23\u0e30\u0e1a\u0e32\u0e22\u0e04\u0e27\u0e32\u0e21\u0e23\u0e49\u0e2d\u0e19\u0e41\u0e1a\u0e1a passive \u0e43\u0e0a\u0e49\u0e1e\u0e25\u0e31\u0e07\u0e07\u0e32\u0e19\u0e2a\u0e39\u0e07\u0e2a\u0e38\u0e14\u0e40\u0e1e\u0e35\u0e22\u0e07 165 W \u0e1e\u0e23\u0e49\u0e2d\u0e21 PCIe Gen4 \u0e41\u0e25\u0e30\u0e23\u0e2d\u0e07\u0e23\u0e31\u0e1a NVLink \u0e2a\u0e4d\u0e32\u0e2b\u0e23\u0e31\u0e1a\u0e01\u0e32\u0e23\u0e2a\u0e37\u0e48\u0e2d\u0e2a\u0e32\u0e23\u0e23\u0e30\u0e2b\u0e27\u0e48\u0e32\u0e07 GPU \u0e15\u0e48\u0e2d\u0e22\u0e2d\u0e14\u0e1b\u0e23\u0e30\u0e2a\u0e34\u0e17\u0e18\u0e34\u0e20\u0e32\u0e1e\u0e07\u0e32\u0e19\u0e2b\u0e19\u0e31\u0e01\u0e43\u0e19\u0e28\u0e39\u0e19\u0e22\u0e4c\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25<\/td>\n<\/tr>\n<tr>\n<td><strong>Technical detail<\/strong><\/td>\n<td>The NVIDIA A30 is a data-center GPU targeted at mainstream enterprise servers. Built on the Ampere architecture with third-generation Tensor Cores and Multi\u2010Instance GPU (MIG) capabilities, it enables simultaneous, isolated workloads on a single card. It is ideal for AI training, high-performance computing (HPC), and large-scale AI inference tasks requiring reliability, performance, and efficient resource utilization.<\/td>\n<\/tr>\n<tr>\n<td><strong>Technical detail<\/strong><\/td>\n<td>The NVIDIA A30 features 3,804 CUDA Cores and 224 third-generation Tensor Cores, supporting various numeric precisions including FP64, TF32, FP16, BFLOAT16, INT8, and INT4. It delivers high Tensor Core performance (e.g., TF32 up to 165 TFLOPS with sparsity) and includes 24 GB of HBM2 memory with 933 GB\/s bandwidth. It supports MIG (up to four isolated instances), passive cooling, up to 165 W power draw, PCIe Gen4 interface, and optional NVLink connectivity for multi-GPU communication\u2014making it an efficient and versatile choice for enterprise-scale AI and HPC workloads<\/td>\n<\/tr>\n<tr>\n<td><strong>Technical detail<\/strong><\/td>\n<td>The NVIDIA A30 is a versatile data-center GPU that delivers both AI and high-performance compute capabilities in a compact, efficient package. Equipped with third-gen Tensor Cores handling everything from FP64 to INT4, and MIG for partitioned, multi-tenant workloads, it maximizes resource usage. Its passive cooling, low 165 W power draw, and fast connectivity via PCIe Gen4 and optional NVLink make it a cost-efficient, high-performance solution for organizations accelerating AI, HPC, or inference workflows.<\/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>\u0e02\u0e49\u0e2d\u0e21\u0e39\u0e25\u0e2d\u0e49\u0e32\u0e07\u0e2d\u0e34\u0e07\u0e08\u0e32\u0e01\u0e1c\u0e39\u0e49\u0e1c\u0e25\u0e34\u0e15: <a href=\"https:\/\/www.nvidia.com\/en-us\/data-center\/a30\/\" target=\"_blank\" rel=\"nofollow noopener\">NVIDIA<\/a><\/p>\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 A30<\/strong> \u0e40\u0e1b\u0e47\u0e19 Data Center GPU \/ Accelerator \u0e08\u0e32\u0e01 <strong>NVIDIA<\/strong> \u0e40\u0e2b\u0e21\u0e32\u0e30\u0e2a\u0e33\u0e2b\u0e23\u0e31\u0e1a\u0e07\u0e32\u0e19 AI training, AI inference, HPC, virtual workstation \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":19187,"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":[388,384],"product_tag":[237,397,396],"class_list":{"0":"post-18687","1":"product","2":"type-product","3":"status-publish","4":"has-post-thumbnail","6":"product_cat-data-center-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\/ko\/wp-json\/wp\/v2\/product\/18687","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/droneth.or.th\/ko\/wp-json\/wp\/v2\/product"}],"about":[{"href":"https:\/\/droneth.or.th\/ko\/wp-json\/wp\/v2\/types\/product"}],"replies":[{"embeddable":true,"href":"https:\/\/droneth.or.th\/ko\/wp-json\/wp\/v2\/comments?post=18687"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/droneth.or.th\/ko\/wp-json\/wp\/v2\/media\/19187"}],"wp:attachment":[{"href":"https:\/\/droneth.or.th\/ko\/wp-json\/wp\/v2\/media?parent=18687"}],"wp:term":[{"taxonomy":"product_brand","embeddable":true,"href":"https:\/\/droneth.or.th\/ko\/wp-json\/wp\/v2\/product_brand?post=18687"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/droneth.or.th\/ko\/wp-json\/wp\/v2\/product_cat?post=18687"},{"taxonomy":"product_tag","embeddable":true,"href":"https:\/\/droneth.or.th\/ko\/wp-json\/wp\/v2\/product_tag?post=18687"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}