{"id":23207,"date":"2025-09-16T14:36:57","date_gmt":"2025-09-16T07:36:57","guid":{"rendered":"https:\/\/vnso.vn\/?p=23207"},"modified":"2025-09-16T17:25:56","modified_gmt":"2025-09-16T10:25:56","slug":"nvidia-a100-vs-v100-so-sanh-chi-tiet-gpu-ai","status":"publish","type":"post","link":"https:\/\/vnso.vn\/en\/nvidia-a100-vs-v100-so-sanh-chi-tiet-gpu-ai\/","title":{"rendered":"NVIDIA A100 vs. V100: So s\u00e1nh chi ti\u1ebft GPU AI"},"content":{"rendered":"<p style=\"text-align: justify;\">Trong c\u00e1c GPU AI ph\u1ed5 bi\u1ebfn hi\u1ec7n nay, NVIDIA A100 v\u00e0 V100 l\u00e0 hai c\u00e1i t\u00ean n\u1ed5i b\u1eadt, th\u01b0\u1eddng \u0111\u01b0\u1ee3c \u0111em ra so s\u00e1nh khi doanh nghi\u1ec7p hay nh\u00e0 nghi\u00ean c\u1ee9u c\u1ea7n l\u1ef1a ch\u1ecdn h\u1ea1 t\u1ea7ng t\u00ednh to\u00e1n m\u1ea1nh m\u1ebd. M\u1ed9t b\u00ean l\u00e0 V100 \u2013 GPU Volta t\u1eebng th\u1ed1ng tr\u1ecb giai \u0111o\u1ea1n tr\u01b0\u1edbc, m\u1ed9t b\u00ean l\u00e0 A100 \u2013 th\u1ebf h\u1ec7 Ampere v\u1edbi hi\u1ec7u su\u1ea5t v\u01b0\u1ee3t tr\u1ed9i cho m\u00f4 h\u00ecnh AI hi\u1ec7n \u0111\u1ea1i.<\/p>\n<p style=\"text-align: justify;\"><em>Xem qua b\u00e0i vi\u1ebft sau<\/em> \u0111\u1ec3 t\u00ecm hi\u1ec3u chi ti\u1ebft s\u1ef1 kh\u00e1c bi\u1ec7t gi\u1eefa A100 v\u00e0 V100, t\u1eeb ki\u1ebfn tr\u00fac, b\u1ed9 nh\u1edb \u0111\u1ebfn hi\u1ec7u n\u0103ng th\u1ef1c t\u1ebf, gi\u00fap b\u1ea1n c\u00f3 g\u00f3c nh\u00ecn r\u00f5 r\u00e0ng tr\u01b0\u1edbc khi \u0111\u1ea7u t\u01b0.<\/p>\n<p style=\"text-align: justify;\" data-start=\"182\" data-end=\"604\">&gt;&gt;&gt; \u0110\u0103ng k\u00fd ngay <a href=\"https:\/\/gpu.vnso.vn\/\"><strong>Server AI\/GPU, Cloud GPU VNSO<\/strong><\/a> \u2013 <em>t\u01b0 v\u1ea5n,<\/em><em> b\u00e1o gi\u00e1 &amp; d\u00f9ng th\u1eed mi\u1ec5n ph\u00ed<\/em>!<\/p>\n<p 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type=\"checkbox\" name=\"your-service\" value=\"C\u00e1c d\u1ecbch v\u1ee5 kh\u00e1c\" \/><span class=\"wpcf7-list-item-label\">C\u00e1c d\u1ecbch v\u1ee5 kh\u00e1c<\/span><\/span><span class=\"wpcf7-list-item last\"><input type=\"checkbox\" name=\"your-service\" value=\"T\u01b0 v\u1ea5n\" \/><span class=\"wpcf7-list-item-label\">T\u01b0 v\u1ea5n<\/span><\/span><\/span><\/span>\n\t<\/p>\n<\/div>\n<div class=\"m-auto text-center\">\n\t<p><input class=\"wpcf7-form-control wpcf7-submit has-spinner\" type=\"submit\" value=\"Sign Up Now\" \/>\n\t<\/p>\n<\/div><div class=\"wpcf7-response-output\" aria-hidden=\"true\"><\/div>\n<input type=\"hidden\" name=\"trp-form-language\" value=\"en\"\/><\/form>\n<\/div>\n<\/p>\n<h2 style=\"text-align: justify;\" data-start=\"5253\" data-end=\"5426\">1. Ki\u1ebfn th\u1ee9c v\u1ec1 NVIDIA A100 v\u00e0 V100 b\u1ea1n n\u00ean bi\u1ebft<\/h2>\n<p style=\"text-align: justify;\">V100 l\u00e0 anh c\u1ea3 m\u1edf \u0111\u01b0\u1eddng cho GPU AI hi\u1ec7n \u0111\u1ea1i, c\u00f2n A100 l\u00e0 th\u1ebf h\u1ec7 k\u1ebf ti\u1ebfp m\u1ea1nh m\u1ebd h\u01a1n, \u0111\u01b0\u1ee3c thi\u1ebft k\u1ebf \u0111\u1ec3 \u0111\u00e1p \u1ee9ng nhu c\u1ea7u hu\u1ea5n luy\u1ec7n m\u00f4 h\u00ecnh AI quy m\u00f4 c\u1ef1c l\u1edbn.<\/p>\n<p style=\"text-align: justify;\" data-start=\"35\" data-end=\"295\"><strong data-start=\"35\" data-end=\"50\">NVIDIA Tesla V100 Tensor Core GPU<\/strong><\/p>\n<p style=\"text-align: justify;\" data-start=\"35\" data-end=\"295\">V100 \u0111\u01b0\u1ee3c NVIDIA gi\u1edbi thi\u1ec7u n\u0103m 2017, d\u1ef1a tr\u00ean ki\u1ebfn tr\u00fac Volta. \u0110\u00e2y l\u00e0 GPU trung t\u00e2m d\u1eef li\u1ec7u \u0111\u1ea7u ti\u00ean c\u1ee7a NVIDIA t\u00edch h\u1ee3p Tensor Core, t\u1ed1i \u01b0u cho t\u00ednh to\u00e1n song song, deep learning, v\u00e0 HPC. V100 t\u1eebng l\u00e0 ti\u00eau chu\u1ea9n v\u00e0ng cho hu\u1ea5n luy\u1ec7n AI v\u00e0 si\u00eau m\u00e1y t\u00ednh.<\/p>\n<p style=\"text-align: justify;\"><strong>NVIDIA A100 Tensor Core GPU<\/strong><\/p>\n<p style=\"text-align: justify;\">A100 ra m\u1eaft n\u0103m 2020, thu\u1ed9c th\u1ebf h\u1ec7 Ampere. \u0110\u00e2y l\u00e0 b\u1ea3n n\u00e2ng c\u1ea5p to\u00e0n di\u1ec7n so v\u1edbi V100, c\u00f3 nhi\u1ec1u CUDA core h\u01a1n, b\u1ed9 nh\u1edb HBM2e l\u1edbn h\u01a1n (40\u201380GB), b\u0103ng th\u00f4ng g\u1ea5p g\u1ea7n 2 l\u1ea7n, v\u00e0 Tensor Core th\u1ebf h\u1ec7 m\u1edbi h\u1ed7 tr\u1ee3 \u0111\u1ecbnh d\u1ea1ng TF32, BF16 c\u00f9ng c\u00f4ng ngh\u1ec7 MIG (Multi-Instance GPU). A100 hi\u1ec7n l\u00e0 GPU ch\u1ee7 l\u1ef1c trong nhi\u1ec1u trung t\u00e2m d\u1eef li\u1ec7u AI, cloud v\u00e0 si\u00eau m\u00e1y t\u00ednh.<\/p>\n<p><strong>&gt;&gt;&gt; C\u00f3 th\u1ec3 b\u1ea1n quan t\u00e2m: <a href=\"https:\/\/vnso.vn\/en\/thue-server-gpu-nvidia-a100-pcie-40gb-chi-voi-32k-d-gio\/\">Thu\u00ea Server GPU NVIDIA A100 PCIe 40GB ch\u1ec9 v\u1edbi 32K \u0111\/gi\u1edd<\/a><\/strong><\/p>\n<p><a href=\"https:\/\/gpu.vnso.vn\/\"><img fetchpriority=\"high\" decoding=\"async\" class=\"aligncenter wp-image-22987 size-full\" src=\"https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/Gia-cua-Server-AI-NVIDIA-DGX-A100-trong-tuong-lai.jpg\" alt=\"Th\u00f4ng s\u1ed1 k\u1ef9 thu\u1eadt NVIDIA A100 v\u00e0 V100\" width=\"1200\" height=\"624\" srcset=\"https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/Gia-cua-Server-AI-NVIDIA-DGX-A100-trong-tuong-lai.jpg 1200w, https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/Gia-cua-Server-AI-NVIDIA-DGX-A100-trong-tuong-lai-800x416.jpg 800w, https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/Gia-cua-Server-AI-NVIDIA-DGX-A100-trong-tuong-lai-1024x532.jpg 1024w, https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/Gia-cua-Server-AI-NVIDIA-DGX-A100-trong-tuong-lai-768x399.jpg 768w, https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/Gia-cua-Server-AI-NVIDIA-DGX-A100-trong-tuong-lai-18x9.jpg 18w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" \/><\/a><\/p>\n<h2 style=\"text-align: justify;\">2. Th\u00f4ng s\u1ed1 k\u1ef9 thu\u1eadt NVIDIA A100 v\u00e0 V100<\/h2>\n<h3 style=\"text-align: justify;\">NVIDIA V100<\/h3>\n<table style=\"height: 575px;\" width=\"717\">\n<thead>\n<tr>\n<th>Technical specifications<\/th>\n<th>V100 PCIe<\/th>\n<th>V100 SXM2<\/th>\n<th>V100S PCIe<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Ki\u1ebfn tr\u00fac GPU<\/td>\n<td>NVIDIA Volta<\/td>\n<td>NVIDIA Volta<\/td>\n<td>NVIDIA Volta<\/td>\n<\/tr>\n<tr>\n<td>S\u1ed1 nh\u00e2n Tensor (Tensor Cores)<\/td>\n<td>640<\/td>\n<td>640<\/td>\n<td>640<\/td>\n<\/tr>\n<tr>\n<td>S\u1ed1 nh\u00e2n CUDA (CUDA Cores)<\/td>\n<td>5,120<\/td>\n<td>5,120<\/td>\n<td>5,120<\/td>\n<\/tr>\n<tr>\n<td>Hi\u1ec7u n\u0103ng d\u1ea5u ch\u1ea5m \u0111\u1ed9ng k\u00e9p (FP64)<\/td>\n<td>7 TFLOPS<\/td>\n<td>7.8 TFLOPS<\/td>\n<td>8.2 TFLOPS<\/td>\n<\/tr>\n<tr>\n<td>Hi\u1ec7u n\u0103ng d\u1ea5u ch\u1ea5m \u0111\u1ed9ng \u0111\u01a1n (FP32)<\/td>\n<td>14 TFLOPS<\/td>\n<td>15.7 TFLOPS<\/td>\n<td>16.4 TFLOPS<\/td>\n<\/tr>\n<tr>\n<td>Hi\u1ec7u n\u0103ng Tensor<\/td>\n<td>112 TFLOPS<\/td>\n<td>125 TFLOPS<\/td>\n<td>130 TFLOPS<\/td>\n<\/tr>\n<tr>\n<td>B\u1ed9 nh\u1edb GPU<\/td>\n<td>32 GB \/ 16 GB HBM2<\/td>\n<td>32 GB HBM2<\/td>\n<td>32 GB HBM2<\/td>\n<\/tr>\n<tr>\n<td>B\u0103ng th\u00f4ng b\u1ed9 nh\u1edb<\/td>\n<td>900 GB\/s<\/td>\n<td>1134 GB\/s<\/td>\n<td>1134 GB\/s<\/td>\n<\/tr>\n<tr>\n<td>ECC (S\u1eeda l\u1ed7i b\u1ed9 nh\u1edb)<\/td>\n<td>C\u00f3<\/td>\n<td>C\u00f3<\/td>\n<td>C\u00f3<\/td>\n<\/tr>\n<tr>\n<td>B\u0103ng th\u00f4ng li\u00ean k\u1ebft<\/td>\n<td>32 GB\/s<\/td>\n<td>300 GB\/s<\/td>\n<td>32 GB\/s<\/td>\n<\/tr>\n<tr>\n<td>Giao ti\u1ebfp h\u1ec7 th\u1ed1ng<\/td>\n<td>PCIe Gen3<\/td>\n<td>NVIDIA NVLink\u2122<\/td>\n<td>PCIe Gen3<\/td>\n<\/tr>\n<tr>\n<td>Ki\u1ec3u d\u00e1ng (Form Factor)<\/td>\n<td>PCIe Full Height\/Length<\/td>\n<td>SXM2<\/td>\n<td>PCIe Full Height\/Length<\/td>\n<\/tr>\n<tr>\n<td>C\u00f4ng su\u1ea5t ti\u00eau th\u1ee5 t\u1ed1i \u0111a<\/td>\n<td>250 W<\/td>\n<td>300 W<\/td>\n<td>250 W<\/td>\n<\/tr>\n<tr>\n<td>Gi\u1ea3i ph\u00e1p t\u1ea3n nhi\u1ec7t<\/td>\n<td>Th\u1ee5 \u0111\u1ed9ng (Passive)<\/td>\n<td>Th\u1ee5 \u0111\u1ed9ng (Passive)<\/td>\n<td>Th\u1ee5 \u0111\u1ed9ng (Passive)<\/td>\n<\/tr>\n<tr>\n<td>H\u1ed7 tr\u1ee3 API t\u00ednh to\u00e1n<\/td>\n<td>CUDA, DirectCompute, OpenCL\u2122, OpenACC\u00ae<\/td>\n<td>CUDA, DirectCompute, OpenCL\u2122, OpenACC\u00ae<\/td>\n<td>CUDA, DirectCompute, OpenCL\u2122, OpenACC\u00ae<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h3 style=\"text-align: justify;\" data-start=\"182\" data-end=\"604\">NVIDIA A100<\/h3>\n<table style=\"height: 606px;\" width=\"716\">\n<thead>\n<tr>\n<th>Technical specifications<\/th>\n<th>A100 80GB PCIe<\/th>\n<th>A100 80GB SXM<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>FP64<\/td>\n<td>9.7 TFLOPS<\/td>\n<td>9.7 TFLOPS<\/td>\n<\/tr>\n<tr>\n<td>FP64 Tensor Core<\/td>\n<td>19.5 TFLOPS<\/td>\n<td>19.5 TFLOPS<\/td>\n<\/tr>\n<tr>\n<td>FP32<\/td>\n<td>19.5 TFLOPS<\/td>\n<td>19.5 TFLOPS<\/td>\n<\/tr>\n<tr>\n<td>Tensor Float 32 (TF32)<\/td>\n<td>156 TFLOPS<\/td>\n<td>312 TFLOPS*<\/td>\n<\/tr>\n<tr>\n<td>BFloat16 Tensor Core<\/td>\n<td>312 TFLOPS<\/td>\n<td>624 TFLOPS*<\/td>\n<\/tr>\n<tr>\n<td>FP16 Tensor Core<\/td>\n<td>312 TFLOPS<\/td>\n<td>624 TFLOPS*<\/td>\n<\/tr>\n<tr>\n<td>INT8 Tensor Core<\/td>\n<td>624 TOPS<\/td>\n<td>1248 TOPS*<\/td>\n<\/tr>\n<tr>\n<td>B\u1ed9 nh\u1edb GPU<\/td>\n<td>80GB HBM2e<\/td>\n<td>80GB HBM2e<\/td>\n<\/tr>\n<tr>\n<td>B\u0103ng th\u00f4ng b\u1ed9 nh\u1edb<\/td>\n<td>1.935 GB\/s<\/td>\n<td>2.039 GB\/s<\/td>\n<\/tr>\n<tr>\n<td>C\u00f4ng su\u1ea5t t\u1ed1i \u0111a (TDP)<\/td>\n<td>300W<\/td>\n<td>400W***<\/td>\n<\/tr>\n<tr>\n<td>Multi-Instance GPU (MIG)<\/td>\n<td>T\u1ed1i \u0111a 7 ph\u00e2n v\u00f9ng @ 10GB<\/td>\n<td>T\u1ed1i \u0111a 7 ph\u00e2n v\u00f9ng @ 10GB<\/td>\n<\/tr>\n<tr>\n<td>Ki\u1ec3u d\u00e1ng (Form Factor)<\/td>\n<td>PCIe (2 khe t\u1ea3n nhi\u1ec7t kh\u00ed ho\u1eb7c 1 khe t\u1ea3n nhi\u1ec7t n\u01b0\u1edbc)<\/td>\n<td>SXM<\/td>\n<\/tr>\n<tr>\n<td>Li\u00ean k\u1ebft (Interconnect)<\/td>\n<td>NVLink Bridge cho 2 GPU: 600 GB\/sPCIe Gen4: 64 GB\/s<\/td>\n<td>NVLink: 600 GB\/sPCIe Gen4: 64 GB\/s<\/td>\n<\/tr>\n<tr>\n<td>T\u00f9y ch\u1ecdn m\u00e1y ch\u1ee7<\/td>\n<td>\u0110\u1ed1i t\u00e1c v\u00e0 h\u1ec7 th\u1ed1ng NVIDIA-Certified (1\u20138 GPU)<\/td>\n<td>NVIDIA HGX\u2122 A100 Partner v\u00e0 h\u1ec7 th\u1ed1ng NVIDIA-Certified v\u1edbi 4, 8, 16 GPUNVIDIA DGX\u2122 A100 (8 GPU)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p style=\"text-align: justify;\" data-start=\"182\" data-end=\"604\">(*<em data-start=\"1141\" data-end=\"1266\">) V\u1edbi sparsity (k\u1ef9 thu\u1eadt th\u01b0a trong m\u00f4 h\u00ecnh AI).<br data-start=\"1194\" data-end=\"1197\" \/>(**) SXM4 GPUs c\u00f3 th\u1ec3 d\u00f9ng NVLink Bridge \u0111\u1ec3 gh\u00e9p t\u1ed1i \u0111a 2 GPU.<br data-start=\"1259\" data-end=\"1262\" \/>(***<\/em>) Phi\u00ean b\u1ea3n 400W cho c\u1ea5u h\u00ecnh ti\u00eau chu\u1ea9n. HGX A100-80GB CTS c\u00f3 th\u1ec3 h\u1ed7 tr\u1ee3 TDP l\u00ean \u0111\u1ebfn 500W.<\/p>\n<h2 style=\"text-align: justify;\" data-start=\"182\" data-end=\"604\">3. Ph\u00e2n t\u00edch, so s\u00e1nh chi ti\u1ebft NVIDIA A100 v\u00e0 V100<\/h2>\n<p style=\"text-align: justify;\" data-start=\"63\" data-end=\"353\">Ph\u1ea7n n\u00e0y s\u1ebd ph\u00e2n t\u00edch s\u00e2u c\u00e1c kh\u00e1c bi\u1ec7t ki\u1ebfn tr\u00fac, kh\u1ea3 n\u0103ng t\u00ednh to\u00e1n, b\u1ed9 nh\u1edb\/b\u0103ng th\u00f4ng, Tensor Core &amp; \u0111\u1ecbnh d\u1ea1ng s\u1ed1, li\u00ean k\u1ebft nhi\u1ec1u GPU, hi\u1ec7u n\u0103ng th\u1ef1c t\u1ebf, \u0111i\u1ec7n\/\u1ed5n \u0111\u1ecbnh v\u1eadn h\u00e0nh v\u00e0 \u1ee9ng d\u1ee5ng ph\u00f9 h\u1ee3p gi\u1eefa 2 GPU datacenter n\u00e0y. M\u00ecnh s\u1ebd n\u00eau s\u1ed1 li\u1ec7u ch\u00ednh v\u00e0 gi\u1ea3i th\u00edch \u00fd ngh\u0129a th\u1ef1c t\u1ebf c\u1ee7a ch\u00fang.<\/p>\n<p style=\"text-align: justify;\" data-start=\"390\" data-end=\"814\">A100 (Ampere, ra 2020) l\u00e0 n\u00e2ng c\u1ea5p ki\u1ebfn tr\u00fac to\u00e0n di\u1ec7n so v\u1edbi V100 (Volta, ra 2017): nhi\u1ec1u nh\u00e2n h\u01a1n, b\u1ed9 nh\u1edb l\u1edbn h\u01a1n\/b\u0103ng th\u00f4ng cao h\u01a1n, Tensor Core th\u1ebf h\u1ec7 m\u1edbi h\u1ed7 tr\u1ee3 TF32\/BF16\/FP64-Tensor, v\u00e0 t\u00ednh n\u0103ng ph\u00e2n v\u00f9ng MIG \u2014 n\u00ean A100 m\u1ea1nh h\u01a1n \u0111\u00e1ng k\u1ec3 cho c\u1ea3 training m\u00f4 h\u00ecnh l\u1edbn l\u1eabn HPC; V100 v\u1eabn l\u00e0 GPU Volta m\u1ea1nh, \u1ed5n \u0111\u1ecbnh, ph\u00f9 h\u1ee3p cho workloads v\u1eeba\/nh\u1ecf ho\u1eb7c n\u01a1i chi ph\u00ed l\u00e0 y\u1ebfu t\u1ed1 quy\u1ebft \u0111\u1ecbnh.<\/p>\n<h3 style=\"text-align: justify;\" data-start=\"821\" data-end=\"875\">Ki\u1ebfn tr\u00fac &amp; silicon (quy tr\u00ecnh, die, transistor)<\/h3>\n<p style=\"text-align: justify;\">Quy tr\u00ecnh nh\u1ecf h\u01a1n + transistor nhi\u1ec1u h\u01a1n =&gt; kh\u1ea3 n\u0103ng t\u00edch h\u1ee3p Tensor Core th\u1ebf h\u1ec7 m\u1edbi, logic FP64 tensor, nhi\u1ec1u b\u1ed9 nh\u1edb on-chip h\u01a1n, d\u1eabn t\u1edbi hi\u1ec7u n\u0103ng per-GPU cao h\u01a1n.<\/p>\n<p style=\"text-align: justify;\" data-start=\"878\" data-end=\"1116\">&#8211; A100 (GA100) \u0111\u01b0\u1ee3c s\u1ea3n xu\u1ea5t tr\u00ean ti\u1ebfn tr\u00ecnh TSMC N7 (7 nm), die l\u1edbn v\u1edbi ~54.2 t\u1ec9 transistor (die ~826 mm\u00b2 trong b\u00e1o c\u00e1o NVIDIA). \u0110i\u1ec1u n\u00e0y cho ph\u00e9p t\u00edch h\u1ee3p nhi\u1ec1u l\u00f5i, b\u1ed9 nh\u1edb v\u00e0 logic tensor ph\u1ee9c t\u1ea1p.<\/p>\n<p style=\"text-align: justify;\" data-start=\"1119\" data-end=\"1381\">&#8211; V100 (GV100) l\u00e0 vi ki\u1ebfn tr\u00fac Volta, s\u1ea3n xu\u1ea5t tr\u00ean quy tr\u00ecnh ~12 nm, l\u00e0 b\u01b0\u1edbc \u0111\u1ea7u \u0111\u01b0a Tensor Core v\u00e0o datacenter. So s\u00e1nh tr\u1ef1c ti\u1ebfp: A100 l\u00e0 th\u1ebf h\u1ec7 nh\u1ecf h\u01a1n v\u1ec1 nm, nhi\u1ec1u transistor h\u01a1n \u2014 n\u1ec1n t\u1ea3ng cho hi\u1ec7u n\u0103ng\/capacity cao h\u01a1n.<\/p>\n<p data-start=\"1119\" data-end=\"1381\"><strong>&gt;&gt;&gt; Xem th\u00eam: GPU <a href=\"https:\/\/vnso.vn\/en\/thue-nvidia-rtx-5880-ada-48gb-gddr6-voi-ecc\/\">NVIDIA RTX 5880 Ada 48GB GDDR6 v\u1edbi ECC<\/a><\/strong><\/p>\n<p data-start=\"1119\" data-end=\"1381\"><a href=\"https:\/\/gpu.vnso.vn\/\"><img decoding=\"async\" class=\"aligncenter wp-image-23041 size-full\" src=\"https:\/\/vnso.vn\/wp-content\/uploads\/2025\/09\/NVIDIA-A100-Tang-toc-Inference-training-so-voi-v100.jpg\" alt=\"NVIDIA-A100-T\u0103ng-t\u1ed1c-Inference-training-so-v\u1edbi-v100\" width=\"1200\" height=\"624\" srcset=\"https:\/\/vnso.vn\/wp-content\/uploads\/2025\/09\/NVIDIA-A100-Tang-toc-Inference-training-so-voi-v100.jpg 1200w, https:\/\/vnso.vn\/wp-content\/uploads\/2025\/09\/NVIDIA-A100-Tang-toc-Inference-training-so-voi-v100-800x416.jpg 800w, https:\/\/vnso.vn\/wp-content\/uploads\/2025\/09\/NVIDIA-A100-Tang-toc-Inference-training-so-voi-v100-1024x532.jpg 1024w, https:\/\/vnso.vn\/wp-content\/uploads\/2025\/09\/NVIDIA-A100-Tang-toc-Inference-training-so-voi-v100-768x399.jpg 768w, https:\/\/vnso.vn\/wp-content\/uploads\/2025\/09\/NVIDIA-A100-Tang-toc-Inference-training-so-voi-v100-18x9.jpg 18w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" \/><\/a><\/p>\n<h3 style=\"text-align: justify;\" data-start=\"1572\" data-end=\"1649\">C\u1ea5u h\u00ecnh nh\u00e2n t\u00ednh (CUDA cores, SMs, Tensor Cores) v\u00e0 hi\u1ec7u n\u0103ng c\u1ef1c \u0111\u1ea1i<\/h3>\n<p style=\"text-align: justify;\" data-start=\"1652\" data-end=\"1861\">&#8211; V100: th\u01b0\u1eddng c\u1ea5u h\u00ecnh ~5120 CUDA cores (t\u00f9y bi\u1ebfn theo phi\u00ean b\u1ea3n), 640 Tensor Cores (th\u1ebf h\u1ec7 Volta). Th\u00f4ng s\u1ed1 n\u00e0y cho V100 s\u1ee9c m\u1ea1nh l\u1edbn cho FP16\/mixed-precision \u0111\u1eddi tr\u01b0\u1edbc.<\/p>\n<p style=\"text-align: justify;\" data-start=\"1864\" data-end=\"2192\">&#8211; A100: ~6912 CUDA cores (GA100), Tensor Cores th\u1ebf h\u1ec7 3 (th\u1ef1c thi nhi\u1ec1u \u0111\u1ecbnh d\u1ea1ng: TF32\/BF16\/FP16 v\u00e0 h\u1ed7 tr\u1ee3 FP64 Tensor ops). A100 b\u00e1o c\u00e1o c\u00e1c m\u1ee9c peak Tensor TFLOPS r\u1ea5t cao (v\u00ed d\u1ee5 TF32\/FP16\/BF16 c\u00f3 c\u00e1c m\u1ee9c peak kh\u00e1c nhau, v\u00e0 v\u1edbi sparsity c\u00f3 th\u1ec3 nh\u00e2n \u0111\u00f4i th\u00f4ng l\u01b0\u1ee3ng trong tr\u01b0\u1eddng h\u1ee3p h\u1ed7 tr\u1ee3).<\/p>\n<p style=\"text-align: justify;\" data-start=\"2194\" data-end=\"2381\">Tensor Core \u0111\u1eddi m\u1edbi c\u1ee7a A100 kh\u00f4ng ch\u1ec9 t\u0103ng throughput m\u00e0 c\u00f2n m\u1edf r\u1ed9ng \u0111\u1ecbnh d\u1ea1ng s\u1ed1 (TF32, BF16) v\u00e0 th\u00eam kh\u1ea3 n\u0103ng x\u1eed l\u00fd FP64 b\u1eb1ng Tensor Cores (t\u0103ng hi\u1ec7u n\u0103ng HPC so v\u1edbi V100).<\/p>\n<p data-start=\"2194\" data-end=\"2381\"><strong>&gt;&gt;&gt; Xem th\u00eam: <a href=\"https:\/\/vnso.vn\/en\/nvidia-a100-pcie-40gb-thong-so-ky-thuat-gpu-vnso\/\">NVIDIA A100 PCIe 40GB &#8211; th\u00f4ng s\u1ed1 k\u1ef9 thu\u1eadt GPU VNSO<\/a><\/strong><\/p>\n<h3 style=\"text-align: justify;\" data-start=\"2388\" data-end=\"2446\">B\u1ed9 nh\u1edb (capacity) v\u00e0 b\u0103ng th\u00f4ng \u2014 \u0111i\u1ec3m kh\u00e1c bi\u1ec7t l\u1edbn<\/h3>\n<p style=\"text-align: justify;\" data-start=\"2449\" data-end=\"2565\">&#8211; V100: 16 GB ho\u1eb7c 32 GB HBM2; b\u0103ng th\u00f4ng ~900 GB\/s (t\u00f9y module v\u00e0 phi\u00ean b\u1ea3n).<\/p>\n<p style=\"text-align: justify;\" data-start=\"2568\" data-end=\"2833\">&#8211; A100: c\u00f3 c\u00e1c phi\u00ean b\u1ea3n 40 GB (HBM2) v\u00e0 80 GB (HBM2e); phi\u00ean b\u1ea3n 80 GB \u0111\u1ea1t b\u0103ng th\u00f4ng r\u1ea5t cao (NVIDIA n\u00eau t\u1edbi ~2 TB\/s cho A100 80GB; 40GB m\u1eabu th\u01b0\u1eddng ghi ~1.5\u20131.6 TB\/s t\u00f9y c\u1ea5u h\u00ecnh). \u0110\u00e2y l\u00e0 b\u01b0\u1edbc nh\u1ea3y l\u1edbn v\u1ec1 capacity v\u00e0 bandwidth.<\/p>\n<p style=\"text-align: justify;\" data-start=\"2835\" data-end=\"3089\">B\u0103ng th\u00f4ng nh\u1edb l\u1edbn v\u00e0 capacity cao gi\u00fap A100 x\u1eed l\u00fd batch l\u1edbn h\u01a1n, m\u00f4 h\u00ecnh l\u1edbn h\u01a1n m\u00e0 kh\u00f4ng ph\u1ea3i t\u00e1ch m\u00f4 h\u00ecnh ra nhi\u1ec1u GPU, gi\u1ea3m overhead giao ti\u1ebfp v\u00e0 I\/O. V\u1edbi V100, m\u00f4 h\u00ecnh th\u1eadt l\u1edbn th\u01b0\u1eddng g\u1eb7p gi\u1edbi h\u1ea1n memory v\u00e0 c\u1ea7n sharding\/ph\u00e2n m\u1ea3nh nhi\u1ec1u h\u01a1n.<\/p>\n<div id=\"attachment_22930\" style=\"width: 1210px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/gpu.vnso.vn\/\"><img decoding=\"async\" aria-describedby=\"caption-attachment-22930\" class=\"wp-image-22930 size-full\" src=\"https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/Bang-AI-Training-A100-nhanh-hon-den-3-lan-V100.jpg\" alt=\"B\u1ea3ng AI Training, A100 nhanh h\u01a1n \u0111\u1ebfn 3 l\u1ea7n V100\" width=\"1200\" height=\"624\" srcset=\"https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/Bang-AI-Training-A100-nhanh-hon-den-3-lan-V100.jpg 1200w, https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/Bang-AI-Training-A100-nhanh-hon-den-3-lan-V100-800x416.jpg 800w, https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/Bang-AI-Training-A100-nhanh-hon-den-3-lan-V100-1024x532.jpg 1024w, https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/Bang-AI-Training-A100-nhanh-hon-den-3-lan-V100-768x399.jpg 768w, https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/Bang-AI-Training-A100-nhanh-hon-den-3-lan-V100-18x9.jpg 18w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" \/><\/a><p id=\"caption-attachment-22930\" class=\"wp-caption-text\"><em>B\u1ea3ng AI Training, A100 nhanh h\u01a1n \u0111\u1ebfn 3 l\u1ea7n V100<\/em><\/p><\/div>\n<h3 style=\"text-align: justify;\" data-start=\"3096\" data-end=\"3148\">\u0110\u1ecbnh d\u1ea1ng s\u1ed1, Tensor Core n\u00e2ng cao v\u00e0 sparsity<\/h3>\n<p style=\"text-align: justify;\">TF32 cho ph\u00e9p m\u00e3 ngu\u1ed3n FP32 c\u0169 ch\u1ea1y nhanh h\u01a1n tr\u00ean A100 m\u00e0 kh\u00f4ng \u0111\u1ed5i nhi\u1ec1u logic; BF16 r\u1ea5t h\u1eefu \u00edch cho training m\u1ea1ng l\u1edbn (gi\u1eef \u1ed5n \u0111\u1ecbnh s\u1ed1 h\u1ecdc). Sparsity c\u00f3 l\u1ee3i cho inference\/quantized models n\u1ebfu pipeline h\u1ed7 tr\u1ee3.<\/p>\n<p style=\"text-align: justify;\" data-start=\"3151\" data-end=\"3280\">&#8211; V100: Tensor Cores ban \u0111\u1ea7u t\u1ed1i \u01b0u cho FP16 mixed precision; FP32 v\u1eabn do CUDA cores x\u1eed l\u00fd.<\/p>\n<p style=\"text-align: justify;\" data-start=\"3283\" data-end=\"3732\">&#8211; A100: h\u1ed7 tr\u1ee3 TF32 (TensorFloat-32) \u2014 m\u1ed9t \u0111\u1ecbnh d\u1ea1ng trung gian gi\u00fap t\u0103ng t\u1ed1c workloads FP32 m\u00e0 kh\u00f4ng c\u1ea7n ch\u1ec9nh code nhi\u1ec1u; h\u1ed7 tr\u1ee3 BF16, FP16, INT8, INT4; h\u01a1n n\u1eefa A100 c\u00f3 FP64 Tensor Core instructions (IEEE-compliant) gi\u00fap t\u0103ng \u0111\u00e1ng k\u1ec3 hi\u1ec7u n\u0103ng FP64 cho HPC. A100 c\u00f2n h\u1ed7 tr\u1ee3 sparsity (c\u1ea5u tr\u00fac th\u01b0a) \u2014 khi m\u00f4 h\u00ecnh \u0111\u01b0\u1ee3c sparsify theo chu\u1ea9n c\u1ee7a NVIDIA, throughput c\u00f3 th\u1ec3 t\u0103ng g\u1ea7n g\u1ea5p \u0111\u00f4i cho m\u1ed9t s\u1ed1 lo\u1ea1i t\u00ednh to\u00e1n.<\/p>\n<h3 style=\"text-align: justify;\" data-start=\"3962\" data-end=\"4028\">Multi-GPU interconnect (NVLink \/ NVSwitch) v\u00e0 kh\u1ea3 n\u0103ng scale<\/h3>\n<p style=\"text-align: justify;\" data-start=\"4031\" data-end=\"4245\">&#8211; V100: NVLink th\u1ebf h\u1ec7 Volta, c\u00f3 throughput l\u1edbn h\u01a1n PCIe; h\u1ec7 th\u1ed1ng nhi\u1ec1u V100 c\u00f3 th\u1ec3 d\u00f9ng NVLink \u0111\u1ec3 t\u0103ng b\u0103ng th\u00f4ng GPU-GPU (d\u1ea1ng up to ~300 GB\/s aggregate t\u00f9y c\u1ea5u h\u00ecnh server).<\/p>\n<p style=\"text-align: justify;\" data-start=\"4248\" data-end=\"4525\">&#8211; A100: h\u1ed7 tr\u1ee3 NVLink th\u1ebf h\u1ec7 m\u1edbi (k\u1ebft h\u1ee3p NVSwitch) \u2014 cho ph\u00e9p li\u00ean k\u1ebft nhi\u1ec1u A100 (v\u00ed d\u1ee5 8\u201316 GPUs) v\u1edbi throughput n\u1ed9i b\u1ed9 r\u1ea5t l\u1edbn; NVIDIA n\u00eau kh\u1ea3 n\u0103ng t\u1edbi ~600 GB\/s aggregate trong c\u1ea5u h\u00ecnh HGX\/NVSwitch, gi\u00fap scale multi-GPU hi\u1ec7u qu\u1ea3 h\u01a1n.<\/p>\n<p style=\"text-align: justify;\" data-start=\"4527\" data-end=\"4709\">\u00dd ngh\u0129a: khi hu\u1ea5n luy\u1ec7n m\u00f4 h\u00ecnh ph\u00e2n t\u00e1n (data-parallel \/ model-parallel), b\u0103ng th\u00f4ng interconnect c\u00e0ng l\u1edbn c\u00e0ng gi\u1ea3m overhead gradient sync; A100 c\u00f3 l\u1ee3i th\u1ebf khi scale l\u00ean nhi\u1ec1u GPU.<\/p>\n<div id=\"attachment_23012\" style=\"width: 1210px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/gpu.vnso.vn\/\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-23012\" class=\"wp-image-23012 size-full\" src=\"https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/HPC-\u2014-Hieu-suat-cao-hon-11-lan-so-voi-V100-va-gap-8-lan-so-voi-T4.jpg\" alt=\"HPC \u2014 Hi\u1ec7u su\u1ea5t cao h\u01a1n 1,1 l\u1ea7n so v\u1edbi V100 v\u00e0 g\u1ea5p 8 l\u1ea7n so v\u1edbi T4.\" width=\"1200\" height=\"624\" srcset=\"https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/HPC-\u2014-Hieu-suat-cao-hon-11-lan-so-voi-V100-va-gap-8-lan-so-voi-T4.jpg 1200w, https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/HPC-\u2014-Hieu-suat-cao-hon-11-lan-so-voi-V100-va-gap-8-lan-so-voi-T4-800x416.jpg 800w, https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/HPC-\u2014-Hieu-suat-cao-hon-11-lan-so-voi-V100-va-gap-8-lan-so-voi-T4-1024x532.jpg 1024w, https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/HPC-\u2014-Hieu-suat-cao-hon-11-lan-so-voi-V100-va-gap-8-lan-so-voi-T4-768x399.jpg 768w, https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/HPC-\u2014-Hieu-suat-cao-hon-11-lan-so-voi-V100-va-gap-8-lan-so-voi-T4-18x9.jpg 18w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" \/><\/a><p id=\"caption-attachment-23012\" class=\"wp-caption-text\"><em>HPC \u2014 Hi\u1ec7u su\u1ea5t A100 cao h\u01a1n 1,1 l\u1ea7n so v\u1edbi V100 v\u00e0 g\u1ea5p 8 l\u1ea7n so v\u1edbi T4.<\/em><\/p><\/div>\n<h3 style=\"text-align: justify;\" data-start=\"4716\" data-end=\"4769\">Multi-Instance GPU (MIG) \u2014 ph\u00e2n v\u00f9ng t\u00e0i nguy\u00ean<\/h3>\n<p style=\"text-align: justify;\" data-start=\"4772\" data-end=\"5117\">A100 c\u00f3 MIG \u2014 c\u00f3 th\u1ec3 partition 1 GPU th\u00e0nh t\u1ed1i \u0111a 7 instance \u0111\u1ed9c l\u1eadp, m\u1ed7i instance c\u00f3 compute, cache, HBM ri\u00eang, b\u1ea3o \u0111\u1ea3m isolation v\u00e0 QoS. \u0110i\u1ec1u n\u00e0y h\u1eefu \u00edch cho multi-tenant cloud ho\u1eb7c workloads nh\u1ecf c\u1ea7n nhi\u1ec1u phi\u00ean song song. K\u00edch ho\u1ea1t MIG c\u1ea7n thi\u1ebft l\u1eadp driver\/kh\u1edfi \u0111\u1ed9ng l\u1ea1i GPU (v\u00e0 c\u00f3 v\u00e0i l\u01b0u \u00fd v\u1eadn h\u00e0nh).<\/p>\n<p style=\"text-align: justify;\" data-start=\"5119\" data-end=\"5226\">V100 kh\u00f4ng c\u00f3 MIG native. N\u1ebfu workload b\u1ea1n c\u00f3 nhi\u1ec1u job nh\u1ecf, A100 cho hi\u1ec7u su\u1ea5t s\u1eed d\u1ee5ng t\u00e0i nguy\u00ean t\u1ed1t h\u01a1n.<\/p>\n<div id=\"attachment_23009\" style=\"width: 1210px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/gpu.vnso.vn\/\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-23009\" class=\"wp-image-23009 size-full\" src=\"https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/Suy-luan-AI-voi-NVIDIA-A30-\u2014-Toc-do-gap-3-lan-V100-trong-hoi-thoai-AI-thoi-gian-thuc.jpg\" alt=\"Suy lu\u1eadn AI v\u1edbi NVIDIA A30 \u2014 T\u1ed1c \u0111\u1ed9 g\u1ea5p 3 l\u1ea7n V100 trong h\u1ed9i tho\u1ea1i AI th\u1eddi gian th\u1ef1c.\" width=\"1200\" height=\"624\" srcset=\"https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/Suy-luan-AI-voi-NVIDIA-A30-\u2014-Toc-do-gap-3-lan-V100-trong-hoi-thoai-AI-thoi-gian-thuc.jpg 1200w, https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/Suy-luan-AI-voi-NVIDIA-A30-\u2014-Toc-do-gap-3-lan-V100-trong-hoi-thoai-AI-thoi-gian-thuc-800x416.jpg 800w, https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/Suy-luan-AI-voi-NVIDIA-A30-\u2014-Toc-do-gap-3-lan-V100-trong-hoi-thoai-AI-thoi-gian-thuc-1024x532.jpg 1024w, https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/Suy-luan-AI-voi-NVIDIA-A30-\u2014-Toc-do-gap-3-lan-V100-trong-hoi-thoai-AI-thoi-gian-thuc-768x399.jpg 768w, https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/Suy-luan-AI-voi-NVIDIA-A30-\u2014-Toc-do-gap-3-lan-V100-trong-hoi-thoai-AI-thoi-gian-thuc-18x9.jpg 18w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" \/><\/a><p id=\"caption-attachment-23009\" class=\"wp-caption-text\"><em>Suy lu\u1eadn AI v\u1edbi NVIDIA A30 \u2014 T\u1ed1c \u0111\u1ed9 g\u1ea5p 3 l\u1ea7n V100 trong h\u1ed9i tho\u1ea1i AI th\u1eddi gian th\u1ef1c.<\/em><\/p><\/div>\n<h3 style=\"text-align: justify;\" data-start=\"5233\" data-end=\"5282\">Hi\u1ec7u n\u0103ng th\u1ef1c t\u1ebf \u2014 benchmark v\u00e0 t\u1ef7 l\u1ec7 t\u0103ng<\/h3>\n<p style=\"text-align: justify;\" data-start=\"5285\" data-end=\"5683\">Benchmarks c\u00f4ng khai (v\u00ed d\u1ee5 Lambda Labs) cho th\u1ea5y A100 th\u01b0\u1eddng nhanh h\u01a1n V100 t\u1eeb ~2x \u0111\u1ebfn &gt;3x t\u00f9y workload: v\u1edbi FP32 v\u00e0 training CNNs th\u00f4ng th\u01b0\u1eddng A100 ~2.1x; v\u1edbi large NLP models con s\u1ed1 cao h\u01a1n; v\u1edbi mixed precision v\u00e0 khi t\u1eadn d\u1ee5ng Tensor Cores m\u1edbi th\u00ec hi\u1ec7u n\u0103ng v\u01b0\u1ee3t tr\u1ed9i h\u01a1n n\u1eefa. Con s\u1ed1 c\u1ee5 th\u1ec3 ph\u1ee5 thu\u1ed9c: model, batch size, IO bottlenecks, multi-GPU scaling.<\/p>\n<p style=\"text-align: justify;\" data-start=\"5687\" data-end=\"5922\">V\u1ec1 FP64 (HPC): A100 c\u00f3 FP64 Tensor Core mode ~19.5 TFLOPS (peak) trong khi V100 kho\u1ea3ng ~7.8 TFLOPS FP64 \u2014 ngh\u0129a l\u00e0 A100 c\u1ea3i thi\u1ec7n l\u1edbn cho workload double-precision (nhi\u1ec1u \u1ee9ng d\u1ee5ng HPC h\u01b0\u1edfng l\u1ee3i).<\/p>\n<p style=\"text-align: justify;\" data-start=\"5924\" data-end=\"6179\">Nh\u1eadn x\u00e9t th\u1ef1c d\u1ee5ng trong workloads training l\u1edbn (GPT-class, BERT-large, ResNet l\u1edbn), A100 th\u01b0\u1eddng gi\u1ea3m th\u1eddi gian hu\u1ea5n luy\u1ec7n \u0111\u00e1ng k\u1ec3; nh\u01b0ng \u0111\u1ec3 t\u1eadn d\u1ee5ng \u0111\u01b0\u1ee3c A100, h\u1ec7 th\u1ed1ng ph\u1ea3i tr\u00e1nh c\u00e1c bottleneck kh\u00e1c (\u0111\u1ecdc\/ghi d\u1eef li\u1ec7u, CPU, NVLink c\u1ea5u h\u00ecnh, I\/O storage).<\/p>\n<h3 style=\"text-align: justify;\" data-start=\"6186\" data-end=\"6232\">\u0110i\u1ec7n n\u0103ng, TDP, form factors v\u00e0 v\u1eadn h\u00e0nh<\/h3>\n<p style=\"text-align: justify;\" data-start=\"6235\" data-end=\"6467\">&#8211; TDP: V100 th\u01b0\u1eddng \u1edf ~300 W (phi\u00ean b\u1ea3n SXM\/PCIe kh\u00e1c nhau), A100 tu\u1ef3 form (PCIe vs SXM) c\u00f3 TDP cao h\u01a1n (SXM kho\u1ea3ng 400 W). \u0110i\u1ec1u n\u00e0y \u1ea3nh h\u01b0\u1edfng chi ph\u00ed \u0111i\u1ec7n v\u00e0 l\u00e0m m\u00e1t khi tri\u1ec3n khai quy m\u00f4 l\u1edbn.<\/p>\n<p style=\"text-align: justify;\" data-start=\"6470\" data-end=\"6647\">&#8211; Form factor: c\u1ea3 hai c\u00f3 phi\u00ean b\u1ea3n PCIe v\u00e0 module SXM (server\/HGX). SXM cho NVLink\/NVSwitch t\u1ed1c \u0111\u1ed9 cao \u2014 th\u01b0\u1eddng th\u1ea5y trong DGX\/HGX servers.<\/p>\n<p style=\"text-align: justify;\" data-start=\"6649\" data-end=\"6755\">V\u1ec1 v\u1eadn h\u00e0nh A100 y\u00eau c\u1ea7u h\u1ec7 th\u1ed1ng ngu\u1ed3n\/thermal t\u1ed1t h\u01a1n; \u0111\u1ed5i l\u1ea1i hi\u1ec7u n\u0103ng\/nh\u1ecbp \u0111\u1ed9 c\u00f4ng vi\u1ec7c \u0111\u01b0\u1ee3c c\u1ea3i thi\u1ec7n.<\/p>\n<h3 style=\"text-align: justify;\" data-start=\"6762\" data-end=\"6800\">Ph\u1ea7n m\u1ec1m, t\u01b0\u01a1ng th\u00edch v\u00e0 t\u1ed1i \u01b0u<\/h3>\n<p style=\"text-align: justify;\" data-start=\"6803\" data-end=\"7085\">Compute capability: V100 l\u00e0 compute capability ~7.0, A100 l\u00e0 ~8.0 \u2014 driver v\u00e0 CUDA toolkit hi\u1ec7n \u0111\u1ea1i h\u1ed7 tr\u1ee3 c\u1ea3 hai, nh\u01b0ng \u0111\u1ec3 t\u1eadn d\u1ee5ng TF32\/BF16\/MIG\/Ampere-specific optimizations c\u1ea7n CUDA \/ cuDNN \/ framework (PyTorch, TensorFlow) phi\u00ean b\u1ea3n m\u1edbi.<\/p>\n<p style=\"text-align: justify;\" data-start=\"7089\" data-end=\"7345\">Migration: code vi\u1ebft cho V100 th\u01b0\u1eddng ch\u1ea1y tr\u00ean A100, nh\u01b0ng \u0111\u1ec3 \u0111\u1ea1t t\u1ed1c \u0111\u1ed9 t\u1ed1i \u0111a c\u1ea7n c\u1eadp nh\u1eadt framework v\u00e0 \u0111\u00f4i khi thay \u0111\u1ed5i ch\u1ebf \u0111\u1ed9 mixed-precision (d\u00f9ng autocast, BF16\/TensorCore config). TF32 thi\u1ebft k\u1ebf cho vi\u1ec7c kh\u00f4ng ph\u1ea3i s\u1eeda code nh\u01b0ng v\u1eabn mang l\u1ee3i t\u1ed1c \u0111\u1ed9.<\/p>\n<div id=\"attachment_22839\" style=\"width: 1210px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/gpu.vnso.vn\/\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-22839\" class=\"wp-image-22839 size-full\" src=\"https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/So-sanh-NVIDIA-A100-va-NVIDIA-V100-GPU.jpg\" alt=\"So s\u00e1nh NVIDIA A100 v\u00e0 NVIDIA V100 GPU\" width=\"1200\" height=\"624\" srcset=\"https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/So-sanh-NVIDIA-A100-va-NVIDIA-V100-GPU.jpg 1200w, https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/So-sanh-NVIDIA-A100-va-NVIDIA-V100-GPU-800x416.jpg 800w, https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/So-sanh-NVIDIA-A100-va-NVIDIA-V100-GPU-1024x532.jpg 1024w, https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/So-sanh-NVIDIA-A100-va-NVIDIA-V100-GPU-768x399.jpg 768w, https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/So-sanh-NVIDIA-A100-va-NVIDIA-V100-GPU-18x9.jpg 18w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" \/><\/a><p id=\"caption-attachment-22839\" class=\"wp-caption-text\"><em>So s\u00e1nh chi ti\u1ebft, t\u1ed5ng th\u1ec3 NVIDIA A100 v\u00e0 NVIDIA V100 GPU<\/em><\/p><\/div>\n<h2 style=\"text-align: justify;\" data-start=\"8823\" data-end=\"8873\">4. C\u00e1c l\u01b0u \u00fd khi quy\u1ebft \u0111\u1ecbnh tri\u1ec3n khai NVIDIA GPU<\/h2>\n<p style=\"text-align: justify;\" data-start=\"8877\" data-end=\"9061\">H\u1ea1 t\u1ea7ng xung quanh (CPU, NVLink topology, storage I\/O) ph\u1ea3i \u0111\u1ee7 \u0111\u1ec3 kh\u00f4ng l\u00e0m bottleneck A100 \u2014 n\u1ebfu I\/O\/CPU k\u00e9m th\u00ec A100 kh\u00f4ng th\u1ec3 ph\u00e1t huy h\u1ebft.<\/p>\n<p style=\"text-align: justify;\" data-start=\"9065\" data-end=\"9196\">Chi ph\u00ed \u0111i\u1ec7n v\u00e0 l\u00e0m m\u00e1t: t\u00ednh to\u00e1n chi ph\u00ed v\u1eadn h\u00e0nh (PUE) khi nh\u00e2n s\u1ed1 GPU l\u00ean quy m\u00f4 l\u1edbn.<\/p>\n<p style=\"text-align: justify;\" data-start=\"9200\" data-end=\"9329\">Ph\u1ea7n m\u1ec1m: c\u1ea7n CUDA\/cuDNN\/PyTorch\/TensorFlow phi\u00ean b\u1ea3n m\u1edbi \u0111\u1ec3 t\u1eadn d\u1ee5ng TF32\/BF16 v\u00e0 MIG.<\/p>\n<p style=\"text-align: justify;\" data-start=\"9333\" data-end=\"9460\">N\u1ebfu b\u1ea1n l\u00e0 cloud provider ho\u1eb7c workload \u0111a-tenant: MIG tr\u00ean A100 l\u00e0 l\u1ee3i th\u1ebf chi\u1ebfn l\u01b0\u1ee3c.<\/p>\n<h3 style=\"text-align: justify;\" data-start=\"7352\" data-end=\"7395\">Khi n\u00e0o ch\u1ecdn V100<\/h3>\n<p style=\"text-align: justify;\" data-start=\"7398\" data-end=\"7523\">&#8211; Ng\u00e2n s\u00e1ch \u0111\u1ea7u t\u01b0 h\u1ea1n ch\u1ebf, nhu c\u1ea7u l\u00e0 training\/inference \u1edf quy m\u00f4 v\u1eeba ho\u1eb7c workloads kh\u00f4ng y\u00eau c\u1ea7u memory\/bandwidth r\u1ea5t l\u1edbn.<\/p>\n<p style=\"text-align: justify;\" data-start=\"7526\" data-end=\"7620\">&#8211; H\u1ea1 t\u1ea7ng hi\u1ec7n t\u1ea1i \u0111\u00e3 t\u1ed1i \u01b0u cho V100 (server, NVLink topology), v\u00e0 vi\u1ec7c n\u00e2ng c\u1ea5p l\u1edbn t\u1ed1n k\u00e9m.<\/p>\n<p style=\"text-align: justify;\" data-start=\"7623\" data-end=\"7737\">&#8211; Mu\u1ed1n hi\u1ec7u qu\u1ea3 chi ph\u00ed khi workload ch\u1ee7 y\u1ebfu inference nh\u1ecf ho\u1eb7c experiments.<\/p>\n<h3 style=\"text-align: justify;\" data-start=\"7744\" data-end=\"7787\">Khi n\u00e0o ch\u1ecdn A100<\/h3>\n<p style=\"text-align: justify;\" data-start=\"7790\" data-end=\"7926\">&#8211; Hu\u1ea5n luy\u1ec7n m\u00f4 h\u00ecnh very-large (NLP, CV) ho\u1eb7c HPC double-precision n\u1eb7ng \u2014 c\u1ea7n memory l\u1edbn, b\u0103ng th\u00f4ng cao v\u00e0 throughput Tensor Core m\u1edbi.<\/p>\n<p style=\"text-align: justify;\" data-start=\"7929\" data-end=\"8004\">&#8211; M\u00f4i tr\u01b0\u1eddng Cloud\/Service provider c\u1ea7n chia s\u1ebb GPU gi\u1eefa nhi\u1ec1u kh\u00e1ch (MIG).<\/p>\n<p style=\"text-align: justify;\" data-start=\"8007\" data-end=\"8173\">&#8211; Mu\u1ed1n t\u1ed1i \u01b0u cost\/time-to-train cho pipeline s\u1ea3n xu\u1ea5t (khi th\u1eddi gian hu\u1ea5n luy\u1ec7n gi\u1ea3m th\u00ec ROI nhanh h\u01a1n d\u00f9 chi ph\u00ed GPU cao h\u01a1n).<\/p>\n<h3 style=\"text-align: justify;\" data-start=\"8180\" data-end=\"8261\">M\u1ed9t v\u00e0i con s\u1ed1 tr\u1ecdng t\u00e2m<\/h3>\n<p style=\"text-align: justify;\" data-start=\"8264\" data-end=\"8481\">&#8211; A100: h\u1ed7 tr\u1ee3 40\/80 GB HBM2\/ HBM2e; A100 80GB n\u00eau b\u0103ng th\u00f4ng t\u1edbi ~2 TB\/s; GA100 ~54.2B transistor; A100 peak tensor TFLOPS (TF32\/FP16\/BF16) \u1edf c\u00e1c m\u1ee9c cao (t\u00f9y sparisty enable).<\/p>\n<p style=\"text-align: justify;\" data-start=\"8484\" data-end=\"8632\">&#8211; V100: 16\/32 GB HBM2; memory bandwidth ~900 GB\/s; 5120 CUDA cores; 640 Tensor Cores; FP64 peak ~7.8 TFLOPS.<\/p>\n<p style=\"text-align: justify;\" data-start=\"8635\" data-end=\"8816\">Benchmarks: A100 th\u01b0\u1eddng nhanh h\u01a1n V100 ~2\u00d7\u20133\u00d7 (trong nhi\u1ec1u b\u00e0i test training FP32\/mixed) nh\u01b0ng con s\u1ed1 c\u1ee5 th\u1ec3 thay \u0111\u1ed5i theo b\u00e0i to\u00e1n v\u00e0 scale.<\/p>\n<h3 style=\"text-align: justify;\" data-start=\"9467\" data-end=\"9498\">T\u00f3m l\u1ea1i<\/h3>\n<p style=\"text-align: justify;\" data-start=\"9501\" data-end=\"9682\">N\u1ebfu m\u1ee5c ti\u00eau l\u00e0 hi\u1ec7u n\u0103ng t\u1ed1i \u0111a cho m\u00f4 h\u00ecnh l\u1edbn v\u00e0 HPC (double-precision), ho\u1eb7c mu\u1ed1n ch\u00ednh s\u00e1ch ph\u00e2n v\u00f9ng GPU cho multi-tenant, ch\u1ecdn A100. N\u1ebfu b\u1ea1n c\u1ea7n hi\u1ec7u n\u0103ng t\u1ed1t nh\u01b0ng kinh ph\u00ed h\u1ea1n ch\u1ebf, ho\u1eb7c h\u1ec7 sinh th\u00e1i hi\u1ec7n t\u1ea1i \u0111\u00e3 d\u1ef1a tr\u00ean V100, V100 v\u1eabn l\u00e0 l\u1ef1a ch\u1ecdn h\u1ee3p l\u00fd.<\/p>\n<h2 style=\"text-align: justify;\" data-start=\"9501\" data-end=\"9682\">5. Nh\u00e0 cung c\u1ea5p Server GPU \/ AI, Cloud GPU h\u00e0ng \u0111\u1ea7u Vi\u1ec7t Nam<\/h2>\n<p style=\"text-align: justify;\" data-start=\"0\" data-end=\"285\">VNSO mang \u0111\u1ebfn h\u1ec7 sinh th\u00e1i GPU, m\u00e1y ch\u1ee7 AI v\u00e0 h\u1ea1 t\u1ea7ng Cloud tr\u1ecdn g\u00f3i, \u0111\u00e1p \u1ee9ng m\u1ecdi nhu c\u1ea7u c\u1ee7a doanh nghi\u1ec7p c\u0169ng nh\u01b0 c\u00e1c vi\u1ec7n nghi\u00ean c\u1ee9u t\u1ea1i Vi\u1ec7t Nam. Kh\u00e1ch h\u00e0ng khi l\u1ef1a ch\u1ecdn VNSO s\u1ebd \u0111\u01b0\u1ee3c ti\u1ebfp c\u1eadn n\u1ec1n t\u1ea3ng c\u00f4ng ngh\u1ec7 t\u1ed1i \u01b0u cho AI, \u0111i k\u00e8m d\u1ecbch v\u1ee5 h\u1ed7 tr\u1ee3 k\u1ef9 thu\u1eadt chuy\u00ean s\u00e2u v\u00e0 t\u1eadn t\u00e2m.<\/p>\n<p style=\"text-align: justify;\" data-start=\"287\" data-end=\"642\" data-is-last-node=\"\" data-is-only-node=\"\">Ch\u00fang t\u00f4i cam k\u1ebft tri\u1ec3n khai nhanh ch\u00f3ng, minh b\u1ea1ch v\u1edbi \u0111\u1ea7y \u0111\u1ee7 CO\/CQ ch\u00ednh h\u00e3ng cho m\u00e1y ch\u1ee7, GPU v\u00e0 si\u00eau m\u00e1y ch\u1ee7 ngay t\u1ea1i Vi\u1ec7t Nam. H\u1ec7 th\u1ed1ng v\u1eadn h\u00e0nh \u1ed5n \u0111\u1ecbnh, b\u1ea3o m\u1eadt cao, \u0111\u1ea3m b\u1ea3o hi\u1ec7u qu\u1ea3 cho m\u1ecdi d\u1ef1 \u00e1n AI t\u1eeb th\u1eed nghi\u1ec7m \u0111\u1ebfn tri\u1ec3n khai quy m\u00f4 l\u1edbn. \u0110\u1eb7c bi\u1ec7t, \u0111\u1ed9i ng\u0169 k\u1ef9 s\u01b0 tr\u1ef1c 24\/7 lu\u00f4n s\u1eb5n s\u00e0ng \u0111\u1ed3ng h\u00e0nh v\u00e0 h\u1ed7 tr\u1ee3 kh\u00e1ch h\u00e0ng trong t\u1eebng b\u01b0\u1edbc tri\u1ec3n khai.<\/p>\n<div id=\"attachment_22983\" style=\"width: 1210px\" class=\"wp-caption aligncenter\"><a href=\"https:\/\/gpu.vnso.vn\/\"><img loading=\"lazy\" decoding=\"async\" aria-describedby=\"caption-attachment-22983\" class=\"size-full wp-image-22983\" src=\"https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/8x-NVIDIA-A100-Tensor-Core-80GB-GPU-tai-VNSO.jpg\" alt=\"8x NVIDIA A100 Tensor Core 80GB GPU t\u1ea1i VNSO\" width=\"1200\" height=\"624\" srcset=\"https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/8x-NVIDIA-A100-Tensor-Core-80GB-GPU-tai-VNSO.jpg 1200w, https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/8x-NVIDIA-A100-Tensor-Core-80GB-GPU-tai-VNSO-800x416.jpg 800w, https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/8x-NVIDIA-A100-Tensor-Core-80GB-GPU-tai-VNSO-1024x532.jpg 1024w, https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/8x-NVIDIA-A100-Tensor-Core-80GB-GPU-tai-VNSO-768x399.jpg 768w, https:\/\/vnso.vn\/wp-content\/uploads\/2025\/08\/8x-NVIDIA-A100-Tensor-Core-80GB-GPU-tai-VNSO-18x9.jpg 18w\" sizes=\"(max-width: 1200px) 100vw, 1200px\" \/><\/a><p id=\"caption-attachment-22983\" class=\"wp-caption-text\"><em>8x NVIDIA A100 SXM Tensor Core 80GB GPU t\u1ea1i VNSO<\/em><\/p><\/div>\n<h3 style=\"text-align: justify;\" data-start=\"287\" data-end=\"642\">Thu\u00ea ngay Server GPU NVIDIA V100 ch\u1ec9 14.000 VN\u0110\/gi\u1edd<\/h3>\n<p>Gi\u00e1 thu\u00ea: 9.800.000 VN\u0110\/th\u00e1ng<br \/>\nCPU: 2 x Intel Xeon E5 Series<br \/>\nRAM: 32 GB<br \/>\nDisk: 240GB SSD NVMe<br \/>\nGPU: 01 x Nvidia Tesla V100 32GB<br \/>\nNetwork: 200Mbps<\/p>\n<h3 style=\"text-align: justify;\">Thu\u00ea ngay Server GPU NVIDIA A100 ch\u1ec9 30.000 VN\u0110\/gi\u1edd<\/h3>\n<p style=\"text-align: justify;\">Tesla V100 32GB<br \/>\nCPU: 2 x Intel Xeon Gold Series<br \/>\nRAM: 128 GB<br \/>\nDisk: 1TB SSD NVMe<br \/>\nGPU: 01 x A100 40GB PCIe ho\u1eb7c x A100 80GB PCIe<br \/>\nNetwork: 500Mbps<\/p>\n<p style=\"text-align: justify;\" data-start=\"389\" data-end=\"444\"><em>NVIDIA A100 40GB GDDR6 Tensor Core<\/em><\/p>\n<p style=\"text-align: justify;\" data-start=\"447\" data-end=\"512\">&#8211; Gi\u00e1 thu\u00ea 1 th\u00e1ng: <strong>1,5 USD\/gi\u1edd<\/strong> \u2192 kho\u1ea3ng 28.080.000 VN\u0110\/th\u00e1ng<\/p>\n<p style=\"text-align: justify;\" data-start=\"515\" data-end=\"593\">&#8211; Gi\u00e1 thu\u00ea 12 th\u00e1ng: <strong>1,125 USD\/gi\u1edd<\/strong> \u2192 kho\u1ea3ng 252.720.000 VN\u0110\/n\u0103m (gi\u1ea3m 25%)<\/p>\n<p style=\"text-align: justify;\" data-start=\"595\" data-end=\"649\"><em>NVIDIA A100 80GB GDDR6 Tensor Core<\/em><\/p>\n<p style=\"text-align: justify;\" data-start=\"652\" data-end=\"717\">&#8211; Gi\u00e1 thu\u00ea 1 th\u00e1ng: <strong>2,4 USD\/gi\u1edd<\/strong> \u2192 kho\u1ea3ng 44.352.000 VN\u0110\/th\u00e1ng<\/p>\n<p style=\"text-align: justify;\" data-start=\"720\" data-end=\"796\">&#8211; Gi\u00e1 thu\u00ea 12 th\u00e1ng: <strong>1,8 USD\/gi\u1edd<\/strong> \u2192 kho\u1ea3ng 404.352.000 VN\u0110\/n\u0103m (gi\u1ea3m 25%)<\/p>\n<p style=\"text-align: justify;\" data-start=\"720\" data-end=\"796\"><em>T\u1ea5t c\u1ea3 c\u00e1c th\u00f4ng s\u1ed1 k\u1ef9 thu\u1eadt, c\u1ea5u h\u00ecnh tr\u00ean \u0111\u1ec1u c\u00f3 th\u1ec3 thay \u0111\u1ed5i theo \u0111\u00fang nhu c\u1ea7u c\u1ee7a b\u1ea1n.<\/em><\/p>\n<p style=\"text-align: justify;\" data-start=\"182\" data-end=\"604\">&gt;&gt;&gt; \u0110\u0103ng k\u00fd ngay <a href=\"https:\/\/gpu.vnso.vn\/\"><strong>Server AI\/GPU, Cloud GPU VNSO<\/strong><\/a> \u2013 <em>t\u01b0 v\u1ea5n,<\/em><em> b\u00e1o gi\u00e1 &amp; d\u00f9ng th\u1eed mi\u1ec5n ph\u00ed<\/em>!<\/p>\n<p style=\"text-align: justify;\" data-start=\"5253\" data-end=\"5426\">\n<div class=\"wpcf7 no-js\" id=\"wpcf7-f15879-o2\" lang=\"en-US\" dir=\"ltr\">\n<div class=\"screen-reader-response\"><p role=\"status\" aria-live=\"polite\" aria-atomic=\"true\"><\/p> <ul><\/ul><\/div>\n<form action=\"\/en\/wp-json\/wp\/v2\/posts\/23207#wpcf7-f15879-o2\" method=\"post\" class=\"wpcf7-form init\" aria-label=\"Contact form\" novalidate=\"novalidate\" data-status=\"init\" data-trp-original-action=\"\/en\/wp-json\/wp\/v2\/posts\/23207#wpcf7-f15879-o2\">\n<div style=\"display: none;\">\n<input type=\"hidden\" name=\"_wpcf7\" value=\"15879\" \/>\n<input type=\"hidden\" name=\"_wpcf7_version\" value=\"5.9.5\" \/>\n<input type=\"hidden\" name=\"_wpcf7_locale\" value=\"en_US\" \/>\n<input type=\"hidden\" name=\"_wpcf7_unit_tag\" value=\"wpcf7-f15879-o2\" \/>\n<input type=\"hidden\" name=\"_wpcf7_container_post\" value=\"0\" \/>\n<input type=\"hidden\" name=\"_wpcf7_posted_data_hash\" value=\"\" \/>\n<input type=\"hidden\" name=\"_wpcf7_recaptcha_response\" value=\"\" \/>\n<\/div>\n<style>\n .popup-template-wrap .wpcf7-list-item {\n display: inline-block;\n margin: 0 1em 0 0;\n width: 127px;\n}\n<\/style>\n<div class=\"d-flex gap-3\">\n\t<p><label> <span class=\"wpcf7-form-control-wrap\" data-name=\"your-name\"><input size=\"40\" class=\"wpcf7-form-control wpcf7-text wpcf7-validates-as-required\" autocomplete=\"name\" aria-required=\"true\" aria-invalid=\"false\" placeholder=\"Your name\/company (*)\" value=\"\" type=\"text\" name=\"your-name\" \/><\/span> <\/label>\n\t<\/p>\n\t<p><label> <span class=\"wpcf7-form-control-wrap\" data-name=\"your-phone\"><input size=\"40\" class=\"wpcf7-form-control wpcf7-tel wpcf7-validates-as-required wpcf7-text wpcf7-validates-as-tel\" aria-required=\"true\" aria-invalid=\"false\" placeholder=\"S\u1ed1 \u0111i\u1ec7n tho\u1ea1i (*)\" value=\"\" type=\"tel\" name=\"your-phone\" \/><\/span> <\/label>\n\t<\/p>\n<\/div>\n<p><label> <span class=\"wpcf7-form-control-wrap\" data-name=\"your-email\"><input size=\"40\" class=\"wpcf7-form-control wpcf7-email wpcf7-validates-as-required wpcf7-text wpcf7-validates-as-email\" autocomplete=\"email\" aria-required=\"true\" aria-invalid=\"false\" placeholder=\"Email c\u00f4ng ty\/c\u00e1 nh\u00e2n (*)\" value=\"\" type=\"email\" name=\"your-email\" \/><\/span> <\/label>\n<\/p>\n<div style=\"text-align: left;\">\n\t<p><label class=\"label-select text-left\"><b> Service you're interested in:<\/b> (Choose one) <\/label><br \/>\n<span class=\"wpcf7-form-control-wrap\" data-name=\"your-service\"><span class=\"wpcf7-form-control wpcf7-checkbox wpcf7-validates-as-required wpcf7-exclusive-checkbox\"><span class=\"wpcf7-list-item first\"><input type=\"checkbox\" name=\"your-service\" 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ho\u1eb7c thu\u00ea NVIDIA A100 v\u00e0 V100<\/b><\/h2>\n<p style=\"text-align: justify;\">\u0110\u1ec3 t\u00ecm hi\u1ec3u th\u00f4ng tin v\u1ec1 c\u00e1c gi\u1ea3i ph\u00e1p \u0110i\u1ec7n to\u00e1n \u0111\u00e1m m\u00e2y, chuy\u1ec3n \u0111\u1ed5i s\u1ed1, m\u00e1y ch\u1ee7 \u1ea3o VPS, Server, m\u00e1y ch\u1ee7 v\u1eadt l\u00fd, CDN\u2026 Qu\u00fd kh\u00e1ch vui l\u00f2ng li\u00ean h\u1ec7 ch\u00fang t\u00f4i theo th\u00f4ng tin d\u01b0\u1edbi \u0111\u00e2y:<\/p>\n<p style=\"text-align: justify;\"><strong>VNSO TECHNOLOGY CO., LTD \u2013 SINCE 2015<\/strong><\/p>\n<p style=\"text-align: justify;\">\u2013 Website:\u00a0<a href=\"https:\/\/vnso.vn\/en\/\">https:\/\/vnso.vn\/<\/a><br \/>\n\u2013 Fanpage:\u00a0<a href=\"https:\/\/www.facebook.com\/VNSO.VN\/\">Facebook<\/a>\u00a0|\u00a0<a href=\"https:\/\/www.linkedin.com\/company\/vnso-technology\/\">LinkedIn<\/a>\u00a0|\u00a0<a href=\"https:\/\/www.youtube.com\/@vnsotechnology\">YouTube<\/a>\u00a0|\u00a0<a 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H\u1ed3 Ch\u00ed Minh<br \/>\n\u2013 VPGD \u0110\u00e0 N\u1eb5ng: 30 Nguy\u1ec5n H\u1eefu Th\u1ecd, Ph\u01b0\u1eddng H\u1ea3i Ch\u00e2u, \u0110\u00e0 N\u1eb5ng<br \/>\n\u2013 VPGD H\u00e0 N\u1ed9i: 132 V\u0169 Ph\u1ea1m H\u00e0m, Ph\u01b0\u1eddng Y\u00ean H\u00f2a, H\u00e0 N\u1ed9i<\/p>","protected":false},"excerpt":{"rendered":"<p>Trong c\u00e1c GPU AI ph\u1ed5 bi\u1ebfn hi\u1ec7n nay, NVIDIA A100 v\u00e0 V100 l\u00e0 hai c\u00e1i t\u00ean n\u1ed5i b\u1eadt, th\u01b0\u1eddng \u0111\u01b0\u1ee3c \u0111em ra so s\u00e1nh khi doanh nghi\u1ec7p hay nh\u00e0 nghi\u00ean c\u1ee9u c\u1ea7n l\u1ef1a ch\u1ecdn h\u1ea1 t\u1ea7ng t\u00ednh to\u00e1n m\u1ea1nh m\u1ebd. M\u1ed9t b\u00ean l\u00e0 V100 \u2013 GPU Volta t\u1eebng th\u1ed1ng tr\u1ecb giai \u0111o\u1ea1n tr\u01b0\u1edbc, m\u1ed9t b\u00ean [&hellip;]<\/p>","protected":false},"author":6,"featured_media":23214,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[533,538],"tags":[511,556,582],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v20.3 (Yoast SEO v22.8) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>NVIDIA A100 vs. V100: So s\u00e1nh chi ti\u1ebft GPU AI<\/title>\n<meta name=\"description\" content=\"So s\u00e1nh chi ti\u1ebft NVIDIA A100 v\u00e0 V100: ph\u00e2n t\u00edch ki\u1ebfn tr\u00fac, b\u1ed9 nh\u1edb v\u00e0 hi\u1ec7u n\u0103ng AI\/HPC \u0111\u1ec3 ch\u1ecdn GPU ph\u00f9 h\u1ee3p cho d\u1ef1 \u00e1n c\u1ee7a b\u1ea1n ngay h\u00f4m nay.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" 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