{"id":24370,"date":"2026-04-15T11:24:43","date_gmt":"2026-04-15T04:24:43","guid":{"rendered":"https:\/\/vnso.vn\/?p=24370"},"modified":"2026-04-17T12:03:11","modified_gmt":"2026-04-17T05:03:11","slug":"nvidia-a100-la-gi","status":"publish","type":"post","link":"https:\/\/vnso.vn\/en\/nvidia-a100-la-gi\/","title":{"rendered":"NVIDIA A100 l\u00e0 g\u00ec? Ph\u00e2n t\u00edch chi ti\u1ebft t\u1eeb A-Z"},"content":{"rendered":"

NVIDIA A100 l\u00e0 GPU datacenter do NVIDIA ph\u00e1t tri\u1ec3n, chuy\u00ean d\u00f9ng cho AI, machine learning v\u00e0 t\u00ednh to\u00e1n hi\u1ec7u n\u0103ng cao (HPC). \u0110\u00e2y l\u00e0 m\u1ed9t trong nh\u1eefng GPU ph\u1ed5 bi\u1ebfn nh\u1ea5t trong c\u00e1c h\u1ec7 th\u1ed1ng AI enterprise v\u00e0 cloud nh\u1edd kh\u1ea3 n\u0103ng x\u1eed l\u00fd song song m\u1ea1nh v\u00e0 b\u0103ng th\u00f4ng b\u1ed9 nh\u1edb r\u1ea5t cao.<\/p>\n

V\u1eady v\u00ec sao GPU A100 n\u00e0y xu\u1ea5t hi\u1ec7n trong h\u1ea7u h\u1ebft h\u1ea1 t\u1ea7ng AI enterprise v\u00e0 cloud? C\u00f9ng VNSO<\/a> t\u00ecm hi\u1ec3u t\u1ea5t c\u1ea3 nh\u1eefng g\u00ec b\u1ea1n c\u1ea7n bi\u1ebft<\/strong> v\u1ec1 GPU NVIDIA A100, \u0111i v\u00e0o chi ti\u1ebft t\u1eeb ki\u1ebfn th\u1ee9c n\u1ec1n t\u1ea3ng \u0111\u1ebfn \u1ee9ng d\u1ee5ng th\u1ef1c t\u1ebf.<\/p>\n

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NVIDIA A100 l\u00e0 g\u00ec? N\u1eafm nhanh th\u00f4ng tin c\u1ea7n bi\u1ebft<\/h2>\n

NVIDIA A100 l\u00e0 GPU chuy\u00ean d\u1ee5ng cho trung t\u00e2m d\u1eef li\u1ec7u, n\u1ebfu CPU x\u1eed l\u00fd t\u1eebng t\u00e1c v\u1ee5 theo th\u1ee9 t\u1ef1, th\u00ec GPU AI nh\u01b0 A100 c\u00f3 th\u1ec3 x\u1eed l\u00fd h\u00e0ng ngh\u00ecn ph\u00e9p to\u00e1n c\u00f9ng l\u00fac. A100 \u0111\u01b0\u1ee3c ph\u00e1t tri\u1ec3n b\u1edfi NVIDIA<\/span><\/span> v\u00e0 ra m\u1eaft v\u00e0o n\u0103m 2020. \u0110\u00e2y l\u00e0 d\u00f2ng GPU thu\u1ed9c nh\u00f3m \u201cData center accelerator<\/strong>\u201d, \u0111\u01b0\u1ee3c thi\u1ebft k\u1ebf \u0111\u1ec3 x\u1eed l\u00fd c\u00e1c t\u00e1c v\u1ee5 t\u00ednh to\u00e1n hi\u1ec7u n\u0103ng cao nh\u01b0 tr\u00ed tu\u1ec7 nh\u00e2n t\u1ea1o (AI), machine learning, ph\u00e2n t\u00edch d\u1eef li\u1ec7u l\u1edbn v\u00e0 m\u00f4 ph\u1ecfng khoa h\u1ecdc.<\/p>\n

Kh\u00e1c v\u1edbi GPU ph\u1ed5 th\u00f4ng, A100 t\u1eadp trung v\u00e0o kh\u1ea3 n\u0103ng x\u1eed l\u00fd AI song song v\u1edbi quy m\u00f4 l\u1edbn. A100 \u0111\u01b0\u1ee3c x\u00e2y d\u1ef1ng tr\u00ean ki\u1ebfn tr\u00fac Ampere<\/a>, h\u1ed7 tr\u1ee3 nhi\u1ec1u \u0111\u1ecbnh d\u1ea1ng, hu\u1ea5n luy\u1ec7n ho\u1eb7c tri\u1ec3n khai m\u00f4 h\u00ecnh AI nhanh ch\u00f3ng.<\/p>\n

M\u1ed9t \u0111i\u1ec3m \u0111\u00e1ng ch\u00fa \u00fd l\u00e0 A100 kh\u00f4ng ho\u1ea1t \u0111\u1ed9ng \u0111\u1ed9c l\u1eadp nh\u01b0 GPU c\u00e1 nh\u00e2n, m\u00e0 th\u01b0\u1eddng \u0111\u01b0\u1ee3c tri\u1ec3n khai trong h\u1ec7 th\u1ed1ng server ho\u1eb7c cloud. Nh\u1edd ki\u1ebfn tr\u00fac t\u1ed1i \u01b0u cho AI, A100 tr\u1edf th\u00e0nh n\u1ec1n t\u1ea3ng quan tr\u1ecdng trong c\u00e1c h\u1ea1 t\u1ea7ng AI enterprise. C\u00e1c nh\u00e0 cung c\u1ea5p cloud l\u1edbn \u0111\u1ec1u s\u1eed d\u1ee5ng GPU n\u00e0y \u0111\u1ec3 tri\u1ec3n khai d\u1ecbch v\u1ee5 AI, t\u1eeb x\u1eed l\u00fd ng\u00f4n ng\u1eef t\u1ef1 nhi\u00ean \u0111\u1ebfn ph\u00e2n t\u00edch d\u1eef li\u1ec7u th\u1eddi gian th\u1ef1c.<\/p>\n

\"So<\/a><\/p>\n

Gi\u1ea3i th\u00edch 2 phi\u00ean b\u1ea3n c\u1ee7a GPU NVIDIA A100 – PCIe v\u00e0 SXM<\/h3>\n
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GPU NVIDIA A100 c\u00f3 2 phi\u00ean b\u1ea3n ch\u00ednh \u0111\u01b0\u1ee3c g\u1ecdi l\u00e0 PCIe v\u00e0 SXM. Hai phi\u00ean b\u1ea3n c\u1ee7a NVIDIA A100 kh\u00e1c nhau ch\u1ee7 y\u1ebfu \u1edf c\u00e1ch l\u1eafp \u0111\u1eb7t, hi\u1ec7u n\u0103ng v\u00e0 m\u1ee5c \u0111\u00edch s\u1eed d\u1ee5ng. PCIe ph\u00f9 h\u1ee3p cho tri\u1ec3n khai ph\u1ed5 th\u00f4ng, d\u1ec5 m\u1edf r\u1ed9ng t\u1eebng ph\u1ea7n.<\/p>\n

SXM h\u01b0\u1edbng \u0111\u1ebfn h\u1ec7 th\u1ed1ng AI quy m\u00f4 l\u1edbn, c\u1ea7n hi\u1ec7u n\u0103ng t\u1ed1i \u0111a v\u00e0 k\u1ebft n\u1ed1i GPU t\u1ed1c \u0111\u1ed9 cao. Ngo\u00e0i ra NVIDIA c\u00f2n c\u00f3 2 b\u1ea3n c\u00f3 VRAM kh\u00e1c nhau, NVIDIA A100 40GB<\/strong><\/a> v\u00e0 80GB<\/a>.<\/strong><\/p>\n

>>> Xem th\u00eam Top 5 l\u00fd do b\u1ea1n n\u00ean \u0111\u1ea7u t\u01b0 v\u00e0o NVIDIA A100 40GB GPU<\/a><\/strong><\/p>\n

A100 PCIe l\u00e0 g\u00ec<\/strong><\/p>\n

Phi\u00ean b\u1ea3n PCIe s\u1eed d\u1ee5ng khe c\u1eafm PCIe ti\u00eau chu\u1ea9n tr\u00ean server, t\u01b0\u01a1ng t\u1ef1 GPU th\u00f4ng th\u01b0\u1eddng. T\u01b0\u01a1ng th\u00edch r\u1ed9ng v\u1edbi nhi\u1ec1u h\u1ec7 th\u1ed1ng. Hi\u1ec7u n\u0103ng v\u00e0 b\u0103ng th\u00f4ng th\u1ea5p h\u01a1n so v\u1edbi SXM. C\u00f4ng su\u1ea5t kho\u1ea3ng 250W. Ph\u00f9 h\u1ee3p cho doanh nghi\u1ec7p c\u1ea7n tri\u1ec3n khai linh ho\u1ea1t, chi ph\u00ed h\u1ee3p l\u00fd ho\u1eb7c workload v\u1eeba ph\u1ea3i.<\/p>\n

A100 SXM l\u00e0 g\u00ec<\/strong><\/p>\n

Phi\u00ean b\u1ea3n SXM s\u1eed d\u1ee5ng module g\u1eafn tr\u1ef1c ti\u1ebfp l\u00ean mainboard chuy\u00ean d\u1ee5ng c\u1ee7a server. Thi\u1ebft k\u1ebf n\u00e0y cho ph\u00e9p GPU ho\u1ea1t \u0111\u1ed9ng \u1edf c\u00f4ng su\u1ea5t cao h\u01a1n, kho\u1ea3ng 400W. SXM h\u1ed7 tr\u1ee3 NVLink<\/a> \u0111\u1ea7y \u0111\u1ee7 v\u1edbi b\u0103ng th\u00f4ng cao h\u01a1n, gi\u00fap nhi\u1ec1u GPU k\u1ebft n\u1ed1i ch\u1eb7t ch\u1ebd. Nhi\u1ec1u GPU c\u00f3 th\u1ec3 k\u1ebft n\u1ed1i v\u1edbi nhau th\u00f4ng qua NVLink \u0111\u1ec3 t\u1ea1o th\u00e0nh c\u1ee5m t\u00ednh to\u00e1n l\u1edbn.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n

B\u1ea3ng th\u00f4ng s\u1ed1 k\u1ef9 thu\u1eadt NVIDIA A100<\/h2>\n

D\u01b0\u1edbi \u0111\u00e2y l\u00e0 b\u1ea3ng t\u1ed5ng h\u1ee3p th\u00f4ng s\u1ed1 c\u1ea5u h\u00ecnh chi ti\u1ebft c\u1ee7a GPU NVIDIA A100<\/b>, cung c\u1ea5p c\u00e1i nh\u00ecn to\u00e0n di\u1ec7n v\u1ec1 hi\u1ec7u su\u1ea5t t\u00ednh to\u00e1n, b\u0103ng th\u00f4ng b\u1ed9 nh\u1edb v\u00e0 c\u00e1c chu\u1ea9n k\u1ebft n\u1ed1i tr\u00ean c\u1ea3 hai phi\u00ean b\u1ea3n PCIe v\u00e0 SXM.<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Th\u00f4ng s\u1ed1<\/strong><\/td>\nA100 80GB PCIe<\/strong><\/td>\nA100 80GB SXM<\/strong><\/td>\n<\/tr>\n<\/thead>\n
FP64<\/b><\/span><\/td>\n9.7 TFLOPS<\/span><\/td>\n9.7 TFLOPS<\/span><\/td>\n<\/tr>\n
FP64 Tensor Core<\/b><\/span><\/td>\n19.5 TFLOPS<\/span><\/td>\n19.5 TFLOPS<\/span><\/td>\n<\/tr>\n
FP32<\/b><\/span><\/td>\n19.5 TFLOPS<\/span><\/td>\n19.5 TFLOPS<\/span><\/td>\n<\/tr>\n
Tensor Float 32 (TF32)<\/b><\/span><\/td>\n156 TFLOPS | 312 TFLOPS*<\/span><\/td>\n156 TFLOPS | 312 TFLOPS*<\/span><\/td>\n<\/tr>\n
BFLOAT16 Tensor Core<\/b><\/span><\/td>\n312 TFLOPS | 624 TFLOPS*<\/span><\/td>\n312 TFLOPS | 624 TFLOPS*<\/span><\/td>\n<\/tr>\n
FP16 Tensor Core<\/b><\/span><\/td>\n312 TFLOPS | 624 TFLOPS*<\/span><\/td>\n312 TFLOPS | 624 TFLOPS*<\/span><\/td>\n<\/tr>\n
INT8 Tensor Core<\/b><\/span><\/td>\n624 TOPS | 1248 TOPS*<\/span><\/td>\n624 TOPS | 1248 TOPS*<\/span><\/td>\n<\/tr>\n
B\u1ed9 nh\u1edb GPU<\/b><\/span><\/td>\n80GB HBM2e<\/span><\/td>\n80GB HBM2e<\/span><\/td>\n<\/tr>\n
B\u0103ng th\u00f4ng b\u1ed9 nh\u1edb<\/b><\/span><\/td>\n1,935 GB\/s<\/span><\/td>\n2,039 GB\/s<\/span><\/td>\n<\/tr>\n
C\u00f4ng su\u1ea5t t\u1ecfa nhi\u1ec7t t\u1ed1i \u0111a (TDP)<\/b><\/span><\/td>\n300W<\/span><\/td>\n400W***<\/span><\/td>\n<\/tr>\n
C\u00f4ng ngh\u1ec7 \u0110a ph\u00e2n th\u00e2n (MIG)<\/b><\/span><\/td>\nL\u00ean \u0111\u1ebfn 7 MIG @ 10GB<\/span><\/td>\nL\u00ean \u0111\u1ebfn 7 MIG @ 10GB<\/span><\/td>\n<\/tr>\n
\u0110\u1ecbnh d\u1ea1ng (Form Factor)<\/b><\/span><\/td>\nPCIe 2-khe (slot t\u1ea3n nhi\u1ec7t kh\u00ed) ho\u1eb7c 1-khe <\/span>t\u1ea3n nhi\u1ec7t l\u1ecfng)<\/span><\/td>\nSXM<\/span><\/td>\n<\/tr>\n
K\u1ebft n\u1ed1i li\u00ean th\u00f4ng (Interconnect)<\/b><\/span><\/td>\n\n

NVLink Bridge (cho 2 GPU): 600GB\/s **
\nPCIe Gen4: 64GB\/s<\/p>\n<\/td>\n

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NVLink: 600GB\/s
\nPCIe Gen4: 64GB\/s<\/p>\n<\/td>\n<\/tr>\n

L\u1ef1a ch\u1ecdn m\u00e1y ch\u1ee7<\/b><\/span><\/td>\nC\u00e1c h\u1ec7 th\u1ed1ng Partner & NVIDIA Certified\u2122 (t\u1eeb 1-8 GPU)<\/span><\/td>\n\n

NVIDIA HGX\u2122 A100 (4, 8, ho\u1eb7c 16 GPU)
\nNVIDIA DGX\u2122 A100 (8 GPU)<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

<\/h3>\n

Ch\u00fa th\u00edch b<\/strong>\u1ea3ng th\u00f4ng s\u1ed1 k\u1ef9 thu\u1eadt NVIDIA A100<\/h3>\n<\/div>\n
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(*<\/b>) V\u1edbi t\u00ednh n\u0103ng Sparsity,<\/b> t\u0103ng t\u1ed1c hi\u1ec7u n\u0103ng th\u00f4ng qua vi\u1ec7c khai th\u00e1c c\u00e1c tr\u1ecdng s\u1ed1 b\u1eb1ng kh\u00f4ng trong m\u1ea1ng. C\u01a1 ch\u1ebf sparsity, gi\u00fap t\u0103ng t\u1ed1c x\u1eed l\u00fd AI b\u1eb1ng c\u00e1ch b\u1ecf qua c\u00e1c ph\u1ea7n t\u1eed kh\u00f4ng c\u1ea7n thi\u1ebft trong m\u00f4 h\u00ecnh. Trong m\u1ed9t s\u1ed1 workload, k\u1ef9 thu\u1eadt n\u00e0y c\u00f3 th\u1ec3 gi\u00fap t\u0103ng hi\u1ec7u n\u0103ng inference l\u00ean \u0111\u1ebfn g\u1ea7n g\u1ea5p \u0111\u00f4i m\u00e0 kh\u00f4ng l\u00e0m gi\u1ea3m \u0111\u1ed9 ch\u00ednh x\u00e1c \u0111\u00e1ng k\u1ec3.<\/p>\n

(**<\/b>) \u0110\u1ed1i v\u1edbi b\u1ea3n PCIe, k\u1ebft n\u1ed1i NVLink Bridge ch\u1ec9 h\u1ed7 tr\u1ee3 t\u1ed1i \u0111a 2 GPU. \u0110\u1ed1i v\u1edbi b\u1ea3n SXM4, k\u1ebft n\u1ed1i th\u00f4ng qua bo m\u1ea1ch ch\u1ee7 server HGX A100.<\/p>\n

(***) M\u1ee9c 400W l\u00e0 c\u1ea5u h\u00ecnh ti\u00eau chu\u1ea9n.<\/i>\u00a0Phi\u00ean b\u1ea3n HGX A100-80GB CTS (Gi\u1ea3i ph\u00e1p t\u1ea3n nhi\u1ec7t t\u00f9y ch\u1ec9nh) c\u00f3 th\u1ec3 h\u1ed7 tr\u1ee3 TDP l\u00ean \u0111\u1ebfn 500W<\/b>.<\/p>\n

\"Structured<\/a>

\u1ea2nh minh h\u1ecda Structured Sparsity trong ki\u1ebfn tr\u00fac Ampere<\/p><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n

Hi\u1ec7u su\u1ea5t n\u1ed5i b\u1eadt c\u1ee7a NVIDIA A100<\/h2>\n

Hi\u1ec7u n\u0103ng AI \u0111\u1ea1t ~312 TFLOPS, t\u0103ng t\u1ed1c training v\u00e0 inference<\/h3>\n

A100 \u0111\u01b0\u1ee3c thi\u1ebft k\u1ebf xoay quanh Tensor Core th\u1ebf h\u1ec7 3, cho ph\u00e9p x\u1eed l\u00fd c\u00e1c ph\u00e9p to\u00e1n ma tr\u1eadn v\u1edbi hi\u1ec7u su\u1ea5t r\u1ea5t cao. \u1ede \u0111\u1ecbnh d\u1ea1ng TF32, GPU n\u00e0y \u0111\u1ea1t kho\u1ea3ng 312 TFLOPS, trong khi v\u1edbi FP16 v\u00e0 k\u1ef9 thu\u1eadt sparsity, hi\u1ec7u n\u0103ng th\u1ef1c t\u1ebf trong m\u1ed9t s\u1ed1 b\u00e0i to\u00e1n c\u00f3 th\u1ec3 t\u0103ng g\u1ea7n g\u1ea5p \u0111\u00f4i.<\/p>\n

Trong m\u00f4i tr\u01b0\u1eddng production, \u0111i\u1ec1u n\u00e0y t\u00e1c \u0111\u1ed9ng tr\u1ef1c ti\u1ebfp \u0111\u1ebfn th\u1eddi gian hu\u1ea5n luy\u1ec7n m\u00f4 h\u00ecnh. C\u00e1c workload tr\u01b0\u1edbc \u0111\u00e2y c\u1ea7n nhi\u1ec1u ng\u00e0y ho\u1eb7c nhi\u1ec1u tu\u1ea7n c\u00f3 th\u1ec3 r\u00fat ng\u1eafn \u0111\u00e1ng k\u1ec3 khi tri\u1ec3n khai tr\u00ean c\u1ee5m A100. V\u1edbi inference, GPU n\u00e0y gi\u00fap t\u0103ng throughput, ngh\u0129a l\u00e0 c\u00f9ng m\u1ed9t h\u1ec7 th\u1ed1ng c\u00f3 th\u1ec3 ph\u1ee5c v\u1ee5 nhi\u1ec1u ng\u01b0\u1eddi d\u00f9ng h\u01a1n trong c\u00f9ng th\u1eddi gian.<\/p>\n

\"NVIDIA-A100-Hi\u1ec7u-n\u0103ng-AI-Inference-cao-h\u01a1n-t\u1edbi-249-l\u1ea7n-so-v\u1edbi-CPU\"<\/a><\/p>\n

B\u1ed9 nh\u1edb HBM2e dung l\u01b0\u1ee3ng l\u1edbn, b\u0103ng th\u00f4ng g\u1ea7n 2 TB\/s<\/h3>\n

A100 s\u1eed d\u1ee5ng b\u1ed9 nh\u1edb HBM2e v\u1edbi hai phi\u00ean b\u1ea3n 40GB v\u00e0 80GB. B\u0103ng th\u00f4ng b\u1ed9 nh\u1edb \u0111\u1ea1t kho\u1ea3ng 1.6 TB\/s \u0111\u1ebfn g\u1ea7n 2 TB\/s, cao h\u01a1n nhi\u1ec1u so v\u1edbi GPU ph\u1ed5 th\u00f4ng v\u1ed1n ch\u1ec9 \u1edf m\u1ee9c v\u00e0i tr\u0103m GB\/s.<\/p>\n

Trong c\u00e1c m\u00f4 h\u00ecnh AI l\u1edbn, \u0111\u1eb7c bi\u1ec7t l\u00e0 LLM, l\u01b0\u1ee3ng d\u1eef li\u1ec7u c\u1ea7n truy xu\u1ea5t li\u00ean t\u1ee5c r\u1ea5t l\u1edbn. Khi b\u0103ng th\u00f4ng th\u1ea5p, GPU ph\u1ea3i ch\u1edd d\u1eef li\u1ec7u, g\u00e2y l\u00e3ng ph\u00ed t\u00e0i nguy\u00ean compute. A100 gi\u00fap GPU lu\u00f4n ho\u1ea1t \u0111\u1ed9ng \u1edf m\u1ee9c hi\u1ec7u su\u1ea5t cao.<\/p>\n

Tensor Core th\u1ebf h\u1ec7 3 h\u1ed7 tr\u1ee3 nhi\u1ec1u \u0111\u1ecbnh d\u1ea1ng t\u00ednh to\u00e1n<\/h3>\n

Tensor Core tr\u00ean A100 h\u1ed7 tr\u1ee3 c\u00e1c \u0111\u1ecbnh d\u1ea1ng FP16, BF16 v\u00e0 TF32. TF32 cho ph\u00e9p gi\u1eef \u0111\u1ed9 ch\u00ednh x\u00e1c g\u1ea7n FP32 nh\u01b0ng t\u1ed1c \u0111\u1ed9 x\u1eed l\u00fd nhanh h\u01a1n \u0111\u00e1ng k\u1ec3, ph\u00f9 h\u1ee3p cho training m\u00e0 kh\u00f4ng c\u1ea7n \u0111i\u1ec1u ch\u1ec9nh nhi\u1ec1u.<\/p>\n

BF16 \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng r\u1ed9ng r\u00e3i trong c\u00e1c h\u1ec7 th\u1ed1ng AI hi\u1ec7n \u0111\u1ea1i do c\u00e2n b\u1eb1ng t\u1ed1t gi\u1eefa hi\u1ec7u n\u0103ng v\u00e0 \u0111\u1ed9 ch\u00ednh x\u00e1c. Nh\u1edd h\u1ed7 tr\u1ee3 nhi\u1ec1u \u0111\u1ecbnh d\u1ea1ng, A100 linh ho\u1ea1t khi tri\u1ec3n khai nhi\u1ec1u lo\u1ea1i m\u00f4 h\u00ecnh kh\u00e1c nhau, t\u1eeb NLP \u0111\u1ebfn computer vision.<\/p>\n

\"B\u1ea3ng<\/a>

B\u1ea3ng hi\u1ec7u su\u1ea5t HPC (T\u00ednh to\u00e1n hi\u1ec7u su\u1ea5t cao – High Performance Computing)<\/p><\/div>\n

Multi-Instance GPU (MIG) – Chia nh\u1ecf hi\u1ec7u n\u0103ng GPU<\/h3>\n

M\u1ed9t GPU A100 c\u00f3 th\u1ec3 chia th\u00e0nh t\u1ed1i \u0111a 7 instance \u0111\u1ed9c l\u1eadp. Ngh\u0129a l\u00e0 m\u1ed7i instance c\u00f3 t\u00e0i nguy\u00ean ri\u00eang v\u1ec1 b\u1ed9 nh\u1edb, cache v\u00e0 compute, ho\u1ea1t \u0111\u1ed9ng nh\u01b0 m\u1ed9t GPU ri\u00eang bi\u1ec7t. \u0110i\u1ec1u n\u00e0y cho ph\u00e9p GPU ch\u1ea1y song song li\u00ean t\u1ee5c m\u00e0 kh\u00f4ng \u1ea3nh h\u01b0\u1edfng l\u1eabn nhau.<\/p>\n

Trong m\u00f4i tr\u01b0\u1eddng cloud ho\u1eb7c doanh nghi\u1ec7p, \u0111i\u1ec1u n\u00e0y gi\u00fap ph\u00e2n b\u1ed5 t\u00e0i nguy\u00ean ch\u00ednh x\u00e1c theo nhu c\u1ea7u. M\u1ed9t workload nh\u1ecf kh\u00f4ng c\u1ea7n chi\u1ebfm to\u00e0n b\u1ed9 GPU, t\u1eeb \u0111\u00f3 t\u0103ng hi\u1ec7u su\u1ea5t s\u1eed d\u1ee5ng t\u1ed5ng th\u1ec3 v\u00e0 gi\u1ea3m chi ph\u00ed v\u1eadn h\u00e0nh.<\/p>\n

NVLink b\u0103ng th\u00f4ng 600 GB\/s, m\u1edf r\u1ed9ng \u0111a GPU hi\u1ec7u qu\u1ea3<\/h3>\n

A100 h\u1ed7 tr\u1ee3 NVLink v\u1edbi t\u1ed5ng b\u0103ng th\u00f4ng l\u00ean \u0111\u1ebfn 600 GB\/s gi\u1eefa c\u00e1c GPU. \u0110\u00e2y l\u00e0 y\u1ebfu t\u1ed1 quan tr\u1ecdng khi x\u00e2y d\u1ef1ng h\u1ec7 th\u1ed1ng nhi\u1ec1u GPU. Trong c\u00e1c m\u00f4 h\u00ecnh l\u1edbn, d\u1eef li\u1ec7u c\u1ea7n \u0111\u01b0\u1ee3c trao \u0111\u1ed5i li\u00ean t\u1ee5c gi\u1eefa c\u00e1c GPU. NVLink gi\u00fap gi\u1ea3m \u0111\u1ed9 tr\u1ec5 v\u00e0 t\u0103ng t\u1ed1c \u0111\u1ed9 truy\u1ec1n d\u1eef li\u1ec7u, t\u1eeb \u0111\u00f3 c\u1ea3i thi\u1ec7n hi\u1ec7u n\u0103ng training khi scale l\u00ean nhi\u1ec1u GPU.<\/p>\n

\"NVSwitch-NVIDIA-DGX-A100\"<\/a><\/p>\n

Kh\u1ea3 n\u0103ng m\u1edf r\u1ed9ng v\u1edbi NVSwitch trong h\u1ec7 th\u1ed1ng l\u1edbn<\/h3>\n

Khi k\u1ebft h\u1ee3p v\u1edbi NVSwitch<\/a>, c\u00e1c GPU A100 c\u00f3 th\u1ec3 k\u1ebft n\u1ed1i th\u00e0nh m\u1ed9t c\u1ee5m l\u1edbn v\u1edbi ki\u1ebfn tr\u00fac g\u1ea7n nh\u01b0 h\u1ee3p nh\u1ea5t. Trong c\u00e1c h\u1ec7 th\u1ed1ng enterprise, c\u1ee5m n\u00e0y c\u00f3 th\u1ec3 m\u1edf r\u1ed9ng l\u00ean h\u00e0ng ch\u1ee5c ho\u1eb7c h\u00e0ng tr\u0103m GPU.<\/p>\n

\u0110i\u1ec1u n\u00e0y \u0111\u1eb7c bi\u1ec7t quan tr\u1ecdng v\u1edbi c\u00e1c m\u00f4 h\u00ecnh AI quy m\u00f4 l\u1edbn, n\u01a1i m\u1ed9t GPU \u0111\u01a1n l\u1ebb kh\u00f4ng \u0111\u1ee7 t\u00e0i nguy\u00ean. Kh\u1ea3 n\u0103ng scale tuy\u1ebfn t\u00ednh gi\u00fap doanh nghi\u1ec7p t\u0103ng n\u0103ng l\u1ef1c x\u1eed l\u00fd theo nhu c\u1ea7u th\u1ef1c t\u1ebf.<\/p>\n

Hi\u1ec7u su\u1ea5t cao trong HPC v\u00e0 ph\u00e2n t\u00edch d\u1eef li\u1ec7u<\/h3>\n

A100 kh\u00f4ng ch\u1ec9 ph\u1ee5c v\u1ee5 AI m\u00e0 c\u00f2n r\u1ea5t m\u1ea1nh trong HPC. GPU n\u00e0y c\u00f3 th\u1ec3 t\u0103ng t\u1ed1c c\u00e1c b\u00e0i to\u00e1n m\u00f4 ph\u1ecfng v\u1eadt l\u00fd, t\u00e0i ch\u00ednh \u0111\u1ecbnh l\u01b0\u1ee3ng v\u00e0 khoa h\u1ecdc d\u1eef li\u1ec7u. Khi k\u1ebft h\u1ee3p v\u1edbi n\u1ec1n t\u1ea3ng CUDA v\u00e0 c\u00e1c th\u01b0 vi\u1ec7n t\u1ed1i \u01b0u, hi\u1ec7u n\u0103ng x\u1eed l\u00fd c\u00f3 th\u1ec3 nhanh h\u01a1n CPU nhi\u1ec1u l\u1ea7n, \u0111\u1eb7c bi\u1ec7t v\u1edbi c\u00e1c b\u00e0i to\u00e1n c\u1ea7n t\u00ednh to\u00e1n song song quy m\u00f4 l\u1edbn.<\/p>\n

S\u1ed1 l\u01b0\u1ee3ng CUDA Core c\u1ee7a A100 \u0111\u1ea1t kho\u1ea3ng 6912 nh\u00e2n, k\u1ebft h\u1ee3p v\u1edbi Tensor Core chuy\u00ean d\u1ee5ng gi\u00fap t\u0103ng t\u1ed1c m\u1ea1nh c\u00e1c ph\u00e9p to\u00e1n deep learning. Ngo\u00e0i ra, c\u00f4ng ngh\u1ec7 NVLink cho ph\u00e9p nhi\u1ec1u GPU k\u1ebft n\u1ed1i v\u1edbi nhau v\u1edbi b\u0103ng th\u00f4ng l\u00ean \u0111\u1ebfn 600 GB\/s, t\u1ea1o th\u00e0nh m\u1ed9t h\u1ec7 th\u1ed1ng t\u00ednh to\u00e1n h\u1ee3p nh\u1ea5t.<\/p>\n

\"NVIDIA-A100-T\u0103ng-t\u1ed1c-Inference-training-so-v\u1edbi-v100\"<\/a><\/p>\n

T\u1ed1i \u01b0u inference v\u1edbi throughput cao, \u0111\u1ed9 tr\u1ec5 th\u1ea5p<\/h3>\n

Trong c\u00e1c h\u1ec7 th\u1ed1ng AI production, inference l\u00e0 giai \u0111o\u1ea1n ti\u00eau t\u1ed1n t\u00e0i nguy\u00ean nhi\u1ec1u nh\u1ea5t. A100 \u0111\u01b0\u1ee3c t\u1ed1i \u01b0u \u0111\u1ec3 x\u1eed l\u00fd nhi\u1ec1u request c\u00f9ng l\u00fac v\u1edbi \u0111\u1ed9 tr\u1ec5 th\u1ea5p.<\/p>\n

M\u1ed9t h\u1ec7 th\u1ed1ng s\u1eed d\u1ee5ng A100 c\u00f3 th\u1ec3 ph\u1ee5c v\u1ee5 h\u00e0ng ngh\u00ecn \u0111\u1ebfn h\u00e0ng ch\u1ee5c ngh\u00ecn request m\u1ed7i gi\u00e2y, t\u00f9y v\u00e0o m\u00f4 h\u00ecnh v\u00e0 c\u1ea5u h\u00ecnh. \u0110i\u1ec1u n\u00e0y gi\u00fap c\u00e1c \u1ee9ng d\u1ee5ng nh\u01b0 chatbot, AI vision ho\u1eb7c h\u1ec7 th\u1ed1ng g\u1ee3i \u00fd ho\u1ea1t \u0111\u1ed9ng \u1ed5n \u0111\u1ecbnh khi l\u01b0\u1ee3ng ng\u01b0\u1eddi d\u00f9ng t\u0103ng. Trong c\u00e1c h\u1ec7 th\u1ed1ng l\u1edbn, h\u00e0ng ch\u1ee5c ho\u1eb7c h\u00e0ng tr\u0103m A100 c\u00f3 th\u1ec3 ho\u1ea1t \u0111\u1ed9ng c\u00f9ng l\u00fac nh\u01b0 m\u1ed9t \u201csi\u00eau m\u00e1y t\u00ednh AI\u201d, ph\u1ee5c v\u1ee5 training ho\u1eb7c inference \u1edf quy m\u00f4 doanh nghi\u1ec7p.<\/p>\n

Hi\u1ec7u qu\u1ea3 n\u0103ng l\u01b0\u1ee3ng v\u00e0 chi ph\u00ed v\u1eadn h\u00e0nh<\/h3>\n

A100 mang l\u1ea1i hi\u1ec7u n\u0103ng cao tr\u00ean m\u1ed7i watt \u0111i\u1ec7n ti\u00eau th\u1ee5 so v\u1edbi c\u00e1c th\u1ebf h\u1ec7 tr\u01b0\u1edbc. Trong m\u00f4i tr\u01b0\u1eddng datacenter, \u0111i\u1ec7n n\u0103ng l\u00e0 m\u1ed9t trong nh\u1eefng chi ph\u00ed l\u1edbn nh\u1ea5t. Nh\u1edd x\u1eed l\u00fd nhanh h\u01a1n v\u00e0 t\u1eadn d\u1ee5ng t\u00e0i nguy\u00ean t\u1ed1t h\u01a1n, doanh nghi\u1ec7p c\u00f3 th\u1ec3 gi\u1ea3m t\u1ed5ng chi ph\u00ed s\u1edf h\u1eefu theo th\u1eddi gian. \u0110\u00e2y l\u00e0 y\u1ebfu t\u1ed1 quan tr\u1ecdng khi tri\u1ec3n khai AI \u1edf quy m\u00f4 l\u1edbn v\u00e0 d\u00e0i h\u1ea1n.<\/p>\n

V\u1edbi n\u1ec1n t\u1ea3ng hi\u1ec7u n\u0103ng nh\u01b0 v\u1eady, A100 kh\u00f4ng ch\u1ec9 m\u1ea1nh v\u1ec1 l\u00fd thuy\u1ebft m\u00e0 c\u00f2n \u0111\u01b0\u1ee3c tri\u1ec3n khai r\u1ed9ng r\u00e3i trong nhi\u1ec1u b\u00e0i to\u00e1n th\u1ef1c t\u1ebf.<\/p>\n

\"NVIDIA<\/a><\/p>\n

NVIDIA A100 \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 l\u00e0m g\u00ec?<\/h2>\n

Trong th\u1ef1c t\u1ebf, A100 kh\u00f4ng ch\u1ec9 ph\u1ee5c v\u1ee5 m\u1ed9t l\u0129nh v\u1ef1c duy nh\u1ea5t m\u00e0 l\u00e0 n\u1ec1n t\u1ea3ng chung cho nhi\u1ec1u h\u1ec7 th\u1ed1ng quan tr\u1ecdng. D\u01b0\u1edbi \u0111\u00e2y l\u00e0 m\u1ed9t s\u1ed1 c\u00f4ng d\u1ee5ng ch\u00ednh v\u00e0 ph\u1ed5 bi\u1ebfn c\u1ee7a GPU NVIDIA A100<\/p>\n

Trong AI v\u00e0 machine learning, A100 \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 hu\u1ea5n luy\u1ec7n c\u00e1c m\u00f4 h\u00ecnh l\u1edbn nh\u01b0 transformer ho\u1eb7c LLM. V\u1edbi kh\u1ea3 n\u0103ng x\u1eed l\u00fd song song v\u00e0 b\u0103ng th\u00f4ng cao, th\u1eddi gian training c\u00f3 th\u1ec3 gi\u1ea3m t\u1eeb nhi\u1ec1u tu\u1ea7n xu\u1ed1ng c\u00f2n v\u00e0i ng\u00e0y, th\u1eadm ch\u00ed v\u00e0i gi\u1edd t\u00f9y quy m\u00f4 h\u1ec7 th\u1ed1ng.<\/p>\n

Trong giai \u0111o\u1ea1n v\u1eadn h\u00e0nh, A100 \u0111\u00f3ng vai tr\u00f2 quan tr\u1ecdng trong inference. \u0110\u00e2y l\u00e0 b\u01b0\u1edbc m\u00f4 h\u00ecnh ph\u1ee5c v\u1ee5 ng\u01b0\u1eddi d\u00f9ng cu\u1ed1i, v\u00ed d\u1ee5 nh\u01b0 chatbot, h\u1ec7 th\u1ed1ng g\u1ee3i \u00fd ho\u1eb7c AI vision. Kh\u1ea3 n\u0103ng x\u1eed l\u00fd \u0111\u1ed3ng th\u1eddi nhi\u1ec1u request v\u1edbi \u0111\u1ed9 tr\u1ec5 th\u1ea5p gi\u00fap doanh nghi\u1ec7p tri\u1ec3n khai AI \u1edf quy m\u00f4 l\u1edbn.<\/p>\n

Trong l\u0129nh v\u1ef1c ph\u00e2n t\u00edch d\u1eef li\u1ec7u, A100 k\u1ebft h\u1ee3p v\u1edbi c\u00e1c framework nh\u01b0 RAPIDS c\u00f3 th\u1ec3 x\u1eed l\u00fd h\u00e0ng t\u1ef7 d\u00f2ng d\u1eef li\u1ec7u nhanh h\u01a1n CPU nhi\u1ec1u l\u1ea7n. \u0110i\u1ec1u n\u00e0y \u0111\u1eb7c bi\u1ec7t quan tr\u1ecdng trong t\u00e0i ch\u00ednh, n\u01a1i c\u00e1c m\u00f4 h\u00ecnh d\u1ef1 b\u00e1o v\u00e0 ph\u00e2n t\u00edch r\u1ee7i ro c\u1ea7n t\u1ed1c \u0111\u1ed9 g\u1ea7n nh\u01b0 real-time.<\/p>\n

\"H\u1ec7<\/a>

C\u00e1c Server AI NVIDIA DGX A100 trong t\u1ee7 rack<\/p><\/div>\n

M\u1ed9t s\u1ed1 \u1ee9ng d\u1ee5ng th\u1ef1c t\u1ebf ti\u00eau bi\u1ec3u c\u1ee7a NVIDIA A100 (Case Study)<\/h3>\n

Baseten<\/strong> s\u1eed d\u1ee5ng A100 tr\u00ean h\u1ea1 t\u1ea7ng cloud \u0111\u1ec3 ph\u1ee5c v\u1ee5 c\u00e1c m\u00f4 h\u00ecnh AI \u1edf m\u00f4i tr\u01b0\u1eddng th\u1ef1c t\u1ebf. Sau khi t\u1ed1i \u01b0u, th\u1eddi gian kh\u1edfi \u0111\u1ed9ng model (cold start) gi\u1ea3m kho\u1ea3ng 30\u201360 l\u1ea7n, \u0111\u1ed3ng th\u1eddi throughput inference t\u0103ng g\u1ea7n 2 l\u1ea7n. \u0110i\u1ec1u n\u00e0y gi\u00fap h\u1ec7 th\u1ed1ng x\u1eed l\u00fd h\u00e0ng ngh\u00ecn request m\u1ed7i gi\u00e2y v\u1edbi \u0111\u1ed9 tr\u1ec5 \u1ed5n \u0111\u1ecbnh.<\/p>\n

BMW<\/span><\/span><\/strong> tri\u1ec3n khai h\u1ec7 th\u1ed1ng AI s\u1eed d\u1ee5ng GPU A100 \u0111\u1ec3 ph\u00e2n t\u00edch d\u1eef li\u1ec7u s\u1ea3n xu\u1ea5t v\u00e0 t\u1ed1i \u01b0u quy tr\u00ecnh trong nh\u00e0 m\u00e1y. Nh\u1edd kh\u1ea3 n\u0103ng x\u1eed l\u00fd song song l\u1edbn, c\u00e1c m\u00f4 h\u00ecnh computer vision v\u00e0 ph\u00e2n t\u00edch d\u1eef li\u1ec7u \u0111\u01b0\u1ee3c t\u0103ng t\u1ed1c \u0111\u00e1ng k\u1ec3, gi\u00fap c\u1ea3i thi\u1ec7n hi\u1ec7u qu\u1ea3 v\u1eadn h\u00e0nh v\u00e0 gi\u1ea3m th\u1eddi gian x\u1eed l\u00fd d\u1eef li\u1ec7u.<\/p>\n

Microsoft<\/span><\/span><\/strong> s\u1eed d\u1ee5ng A100 trong c\u00e1c c\u1ee5m GPU Azure \u0111\u1ec3 cung c\u1ea5p d\u1ecbch v\u1ee5 AI cho doanh nghi\u1ec7p. M\u1ed9t node s\u1eed d\u1ee5ng 8 GPU A100 c\u00f3 th\u1ec3 x\u1eed l\u00fd kh\u1ed1i l\u01b0\u1ee3ng l\u1edbn workload AI, t\u1eeb training \u0111\u1ebfn inference, cho ph\u00e9p scale l\u00ean h\u00e0ng ngh\u00ecn GPU trong c\u00e1c h\u1ec7 th\u1ed1ng cloud hyperscale.<\/p>\n

H\u1ec7 th\u1ed1ng NVIDIA DGX A100<\/strong> v\u1edbi nhi\u1ec1u GPU A100 c\u00f3 th\u1ec3 \u0111\u1ea1t hi\u1ec7u n\u0103ng t\u1edbi kho\u1ea3ng 2.5 petaflops AI. Trong c\u00e1c b\u00e0i to\u00e1n nh\u01b0 BERT, th\u1eddi gian training gi\u1ea3m g\u1ea7n 3 l\u1ea7n, trong khi inference nhanh h\u01a1n 4 l\u1ea7n so v\u1edbi th\u1ebf h\u1ec7 tr\u01b0\u1edbc, gi\u00fap r\u00fat ng\u1eafn \u0111\u00e1ng k\u1ec3 chu k\u1ef3 ph\u00e1t tri\u1ec3n m\u00f4 h\u00ecnh.<\/p>\n

\"NVIDIA<\/a>

H\u1ec7 th\u1ed1ng NVIDIA H100<\/p><\/div>\n

So s\u00e1nh NVIDIA A100 v\u1edbi c\u00e1c GPU AI ph\u1ed5 bi\u1ebfn kh\u00e1c<\/h2>\n

NVIDIA A100 l\u00e0 m\u1ed9t trong nh\u1eefng GPU \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng ph\u1ed5 bi\u1ebfn t\u1ea1i Vi\u1ec7t Nam v\u00e0 th\u1ebf gi\u1edbi cho Server GPU v\u00e0 Cloud GPU. Hi\u1ec7u n\u0103ng \u0111\u1ee7 m\u1ea1nh cho c\u1ea3 training v\u00e0 inference, trong khi chi ph\u00ed v\u1eabn h\u1ee3p l\u00fd h\u01a1n so v\u1edbi th\u1ebf h\u1ec7 m\u1edbi. C\u00f9ng so s\u00e1nh nhanh NVIDIA A100 so v\u1edbi c\u00e1c GPU th\u00f4ng d\u1ee5ng kh\u00e1c.<\/p>\n

– A100: l\u1ef1a ch\u1ecdn c\u00e2n b\u1eb1ng nh\u1ea5t cho doanh nghi\u1ec7p<\/p>\n

– NVIDIA H100<\/a>: d\u00e0nh cho AI quy m\u00f4 r\u1ea5t l\u1edbn, c\u1ea7n hi\u1ec7u n\u0103ng t\u1ed1i \u0111a<\/p>\n

– NVIDIA L40S<\/a>: t\u1ed1i \u01b0u chi ph\u00ed, ph\u00f9 h\u1ee3p inference v\u00e0 workload v\u1eeba<\/p>\n

B\u1ea3ng so s\u00e1nh chi ti\u1ebft NVIDIA A100 (Th\u00f4ng s\u1ed1 k\u1ef9 thu\u1eadt v\u00e0 gi\u00e1 tr\u00ean th\u1ecb tr\u01b0\u1eddng)<\/strong><\/h3>\n

Trong th\u1ef1c t\u1ebf, A100 v\u1eabn \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng r\u1ed9ng r\u00e3i nh\u1edd h\u1ec7 sinh th\u00e1i \u1ed5n \u0111\u1ecbnh v\u00e0 kh\u1ea3 n\u0103ng tri\u1ec3n khai linh ho\u1ea1t. NVIDIA H100 ph\u00f9 h\u1ee3p v\u1edbi c\u00e1c h\u1ec7 th\u1ed1ng AI quy m\u00f4 r\u1ea5t l\u1edbn, trong khi L40S th\u01b0\u1eddng \u0111\u01b0\u1ee3c ch\u1ecdn cho inference \u0111\u1ec3 t\u1ed1i \u01b0u chi ph\u00ed.<\/p>\n

\n
\n\n\n\n\n\n\n\n\n\n\n\n
Ti\u00eau ch\u00ed<\/th>\nNVIDIA A100<\/span><\/span><\/th>\nNVIDIA H100<\/span><\/span><\/th>\nNVIDIA L40S<\/span><\/span><\/th>\n<\/tr>\n<\/thead>\n
Ki\u1ebfn tr\u00fac<\/td>\nAmpere (2020)<\/td>\nHopper (2022)<\/td>\nAda Lovelace<\/td>\n<\/tr>\n
VRAM<\/td>\n40GB \/ 80GB HBM2e<\/td>\n80GB HBM3<\/td>\n48GB GDDR6X<\/td>\n<\/tr>\n
B\u0103ng th\u00f4ng<\/td>\n~1.6\u20132 TB\/s<\/td>\n~3.35 TB\/s<\/td>\n~864 GB\/s<\/td>\n<\/tr>\n
AI FP16\/BF16<\/td>\n~312 TFLOPS<\/td>\n~990 TFLOPS<\/td>\n~362 TFLOPS<\/td>\n<\/tr>\n
NVLink<\/td>\nC\u00f3 (600 GB\/s)<\/td>\nC\u00f3 (900 GB\/s)<\/td>\nKh\u00f4ng<\/td>\n<\/tr>\n
TDP<\/td>\n~400W<\/td>\n~700W<\/td>\n~350W<\/td>\n<\/tr>\n
Gi\u00e1 cloud (2026)<\/td>\n~1.5 – 3.9 USD\/gi\u1edd<\/td>\n~3 – 5 USD\/gi\u1edd<\/td>\n~0.8 – 1.5 USD\/gi\u1edd<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n

Tuy nhi\u00ean, hi\u1ec7u n\u0103ng cao lu\u00f4n \u0111i k\u00e8m v\u1edbi b\u00e0i to\u00e1n chi ph\u00ed, \u0111\u1eb7c bi\u1ec7t trong c\u00e1c h\u1ec7 th\u1ed1ng AI v\u1eadn h\u00e0nh li\u00ean t\u1ee5c.<\/p>\n

>>> Xem th\u00eam NVIDIA A100 vs. V100: So s\u00e1nh chi ti\u1ebft GPU AI<\/a><\/strong><\/p>\n

Gi\u00e1 NVIDIA A100 v\u00e0 chi ph\u00ed v\u1eadn h\u00e0nh<\/h2>\n

Chi ph\u00ed c\u1ee7a NVIDIA A100 ph\u1ee5 thu\u1ed9c v\u00e0o c\u00e1ch tri\u1ec3n khai, bao g\u1ed3m mua ph\u1ea7n c\u1ee9ng ho\u1eb7c thu\u00ea cloud GPU. Tr\u00ean th\u1ecb tr\u01b0\u1eddng, gi\u00e1 m\u1ed9t GPU A100 40GB<\/strong> th\u01b0\u1eddng dao \u0111\u1ed9ng kho\u1ea3ng 8.000\u201312.000 USD, trong khi phi\u00ean b\u1ea3n 80GB<\/strong> c\u00f3 th\u1ec3 t\u1eeb 15.000\u201320.000 USD. V\u1edbi c\u00e1c h\u1ec7 th\u1ed1ng nhi\u1ec1u GPU nh\u01b0 HGX ho\u1eb7c DGX, t\u1ed5ng chi ph\u00ed c\u00f3 th\u1ec3 l\u00ean \u0111\u1ebfn h\u00e0ng tr\u0103m ngh\u00ecn USD khi t\u00ednh c\u1ea3 server, \u0111i\u1ec7n n\u0103ng v\u00e0 h\u1ea1 t\u1ea7ng l\u00e0m m\u00e1t.<\/p>\n

Trong th\u1ef1c t\u1ebf, ph\u1ea7n l\u1edbn doanh nghi\u1ec7p l\u1ef1a ch\u1ecdn thu\u00ea cloud GPU \u0111\u1ec3 t\u1ed1i \u01b0u chi ph\u00ed v\u00e0 linh ho\u1ea1t m\u1edf r\u1ed9ng. Gi\u00e1 thu\u00ea A100 hi\u1ec7n dao \u0111\u1ed9ng kho\u1ea3ng 0.7\u20133 USD\/gi\u1edd t\u00f9y nh\u00e0 cung c\u1ea5p v\u00e0 c\u1ea5u h\u00ecnh. N\u1ebfu v\u1eadn h\u00e0nh li\u00ean t\u1ee5c, chi ph\u00ed c\u00f3 th\u1ec3 t\u1eeb kho\u1ea3ng 1.000 \u0111\u1ebfn h\u01a1n 2.000 USD m\u1ed7i th\u00e1ng cho m\u1ed9t GPU.<\/p>\n

Ngo\u00e0i chi ph\u00ed GPU, doanh nghi\u1ec7p c\u00f2n c\u1ea7n t\u00ednh \u0111\u1ebfn c\u00e1c y\u1ebfu t\u1ed1 nh\u01b0 l\u01b0u tr\u1eef, b\u0103ng th\u00f4ng m\u1ea1ng, truy\u1ec1n d\u1eef li\u1ec7u v\u00e0 hi\u1ec7u su\u1ea5t s\u1eed d\u1ee5ng t\u00e0i nguy\u00ean. Trong nhi\u1ec1u tr\u01b0\u1eddng h\u1ee3p, c\u00e1c chi ph\u00ed n\u00e0y c\u00f3 th\u1ec3 l\u00e0m t\u1ed5ng chi ph\u00ed t\u0103ng th\u00eam 20\u201340%. V\u00ec v\u1eady, hi\u1ec7u qu\u1ea3 th\u1ef1c t\u1ebf ph\u1ee5 thu\u1ed9c nhi\u1ec1u v\u00e0o c\u00e1ch t\u1ed1i \u01b0u h\u1ec7 th\u1ed1ng v\u00e0 m\u1ee9c \u0111\u1ed9 s\u1eed d\u1ee5ng GPU, h\u01a1n l\u00e0 ch\u1ec9 gi\u00e1 thu\u00ea ban \u0111\u1ea7u.<\/p>\n

2 c\u00e1ch tri\u1ec3n khai NVIDIA A100 ph\u1ed5 bi\u1ebfn – Cloud GPU v\u00e0 Server GPU<\/h3>\n

Trong th\u1ef1c t\u1ebf, doanh nghi\u1ec7p c\u00f3 hai c\u00e1ch ti\u1ebfp c\u1eadn ph\u1ed5 bi\u1ebfn \u0111\u1ec3 khai th\u00e1c s\u1ee9c m\u1ea1nh c\u1ee7a NVIDIA A100: tri\u1ec3n khai Server GPU<\/strong> ri\u00eang ho\u1eb7c s\u1eed d\u1ee5ng Cloud GPU<\/strong>. Server GPU ph\u00f9 h\u1ee3p v\u1edbi h\u1ec7 th\u1ed1ng c\u1ea7n ki\u1ec3m so\u00e1t to\u00e0n b\u1ed9 h\u1ea1 t\u1ea7ng v\u00e0 v\u1eadn h\u00e0nh d\u00e0i h\u1ea1n, trong khi cloud GPU cho ph\u00e9p kh\u1edfi t\u1ea1o nhanh, m\u1edf r\u1ed9ng linh ho\u1ea1t theo nhu c\u1ea7u v\u00e0 t\u1ed1i \u01b0u chi ph\u00ed ban \u0111\u1ea7u. T\u00f9y v\u00e0o quy m\u00f4 v\u00e0 ng\u00e2n s\u00e1ch, vi\u1ec7c l\u1ef1a ch\u1ecdn m\u00f4 h\u00ecnh tri\u1ec3n khai ph\u00f9 h\u1ee3p s\u1ebd \u1ea3nh h\u01b0\u1edfng tr\u1ef1c ti\u1ebfp \u0111\u1ebfn hi\u1ec7u qu\u1ea3 s\u1eed d\u1ee5ng GPU v\u00e0 t\u1ed5ng chi ph\u00ed v\u1eadn h\u00e0nh.<\/p>\n

\"Tri\u1ec3n<\/a><\/p>\n

V\u00ec sao NVIDIA A100 tr\u1edf th\u00e0nh ti\u00eau chu\u1ea9n c\u1ee7a h\u1ea1 t\u1ea7ng AI?<\/h2>\n

M\u1ed9t trong nh\u1eefng l\u00fd do l\u1edbn nh\u1ea5t l\u00e0 hi\u1ec7u su\u1ea5t tr\u00ean m\u1ed7i chi ph\u00ed (performance per dollar). D\u00f9 chi ph\u00ed \u0111\u1ea7u t\u01b0 ban \u0111\u1ea7u cao, A100 gi\u00fap gi\u1ea3m \u0111\u00e1ng k\u1ec3 th\u1eddi gian x\u1eed l\u00fd v\u00e0 chi ph\u00ed v\u1eadn h\u00e0nh d\u00e0i h\u1ea1n.<\/p>\n

Trong nhi\u1ec1u b\u00e0i benchmark th\u1ef1c t\u1ebf, A100 cho th\u1ea5y hi\u1ec7u n\u0103ng cao h\u01a1n t\u1eeb 2 \u0111\u1ebfn 6 l\u1ea7n so v\u1edbi th\u1ebf h\u1ec7 tr\u01b0\u1edbc trong c\u00e1c t\u00e1c v\u1ee5 AI ph\u1ed5 bi\u1ebfn. \u0110i\u1ec1u n\u00e0y \u0111\u1ed3ng ngh\u0129a v\u1edbi vi\u1ec7c doanh nghi\u1ec7p c\u00f3 th\u1ec3 gi\u1ea3m s\u1ed1 l\u01b0\u1ee3ng m\u00e1y c\u1ea7n thi\u1ebft, ti\u1ebft ki\u1ec7m \u0111i\u1ec7n n\u0103ng v\u00e0 chi ph\u00ed h\u1ea1 t\u1ea7ng.<\/p>\n

Ngo\u00e0i ra, kh\u1ea3 n\u0103ng m\u1edf r\u1ed9ng l\u00e0 y\u1ebfu t\u1ed1 then ch\u1ed1t. V\u1edbi NVLink v\u00e0 NVSwitch, c\u00e1c c\u1ee5m GPU A100 c\u00f3 th\u1ec3 scale l\u00ean h\u00e0ng tr\u0103m GPU m\u00e0 v\u1eabn duy tr\u00ec hi\u1ec7u su\u1ea5t cao. \u0110\u00e2y l\u00e0 n\u1ec1n t\u1ea3ng cho c\u00e1c h\u1ec7 th\u1ed1ng AI quy m\u00f4 l\u1edbn nh\u01b0 cloud hyperscale ho\u1eb7c trung t\u00e2m d\u1eef li\u1ec7u AI.<\/p>\n

Vai tr\u00f2 c\u1ee7a A100 trong k\u1ef7 nguy\u00ean LLM v\u00e0 AI hi\u1ec7n \u0111\u1ea1i<\/h3>\n

Khi c\u00e1c m\u00f4 h\u00ecnh nh\u01b0 LLM ph\u00e1t tri\u1ec3n, nhu c\u1ea7u kh\u00f4ng ch\u1ec9 d\u1eebng l\u1ea1i \u1edf training m\u00e0 chuy\u1ec3n sang inference \u1edf quy m\u00f4 l\u1edbn. \u0110\u00e2y l\u00e0 n\u01a1i A100 ph\u00e1t huy t\u1ed1i \u0111a gi\u00e1 tr\u1ecb. M\u1ed9t h\u1ec7 th\u1ed1ng s\u1eed d\u1ee5ng A100 c\u00f3 th\u1ec3 ph\u1ee5c v\u1ee5 h\u00e0ng ngh\u00ecn \u0111\u1ebfn h\u00e0ng ch\u1ee5c ngh\u00ecn request m\u1ed7i gi\u00e2y, t\u00f9y c\u1ea5u h\u00ecnh v\u00e0 m\u00f4 h\u00ecnh. \u0110i\u1ec1u n\u00e0y gi\u00fap c\u00e1c doanh nghi\u1ec7p tri\u1ec3n khai chatbot, AI assistant ho\u1eb7c h\u1ec7 th\u1ed1ng t\u1ef1 \u0111\u1ed9ng h\u00f3a v\u1edbi \u0111\u1ed9 \u1ed5n \u0111\u1ecbnh cao.<\/p>\n

Trong b\u1ed1i c\u1ea3nh \u0111\u00f3, A100 l\u00e0 m\u1ed9t ph\u1ea7n c\u1ee7a h\u1ea1 t\u1ea7ng AI ho\u00e0n ch\u1ec9nh, n\u01a1i hi\u1ec7u n\u0103ng, \u0111\u1ed9 \u1ed5n \u0111\u1ecbnh v\u00e0 kh\u1ea3 n\u0103ng m\u1edf r\u1ed9ng \u0111\u1ec1u \u0111\u00f3ng vai tr\u00f2 quy\u1ebft \u0111\u1ecbnh.<\/p>\n

K\u1ebft lu\u1eadn v\u1ec1 NVIDIA A100<\/h2>\n

NVIDIA A100 l\u00e0 n\u1ec1n t\u1ea3ng GPU datacenter quan tr\u1ecdng trong c\u00e1c h\u1ec7 th\u1ed1ng AI hi\u1ec7n \u0111\u1ea1i, \u0111\u1eb7c bi\u1ec7t \u1edf giai \u0111o\u1ea1n v\u1eadn h\u00e0nh v\u00e0 m\u1edf r\u1ed9ng quy m\u00f4. V\u1edbi ki\u1ebfn tr\u00fac t\u1ed1i \u01b0u cho t\u00ednh to\u00e1n song song, b\u0103ng th\u00f4ng b\u1ed9 nh\u1edb cao v\u00e0 kh\u1ea3 n\u0103ng chia nh\u1ecf t\u00e0i nguy\u00ean, A100 gi\u00fap doanh nghi\u1ec7p tri\u1ec3n khai AI hi\u1ec7u qu\u1ea3 h\u01a1n v\u1ec1 hi\u1ec7u su\u1ea5t l\u1eabn chi ph\u00ed.<\/p>\n

D\u00f9 \u0111\u00e3 c\u00f3 th\u1ebf h\u1ec7 m\u1edbi, A100 v\u1eabn gi\u1eef vai tr\u00f2 c\u00e2n b\u1eb1ng gi\u1eefa hi\u1ec7u n\u0103ng, \u0111\u1ed9 \u1ed5n \u0111\u1ecbnh v\u00e0 kh\u1ea3 n\u0103ng tri\u1ec3n khai th\u1ef1c t\u1ebf. \u0110\u00e2y l\u00e0 l\u1ef1a ch\u1ecdn ph\u00f9 h\u1ee3p cho c\u1ea3 training v\u00e0 inference trong nhi\u1ec1u b\u00e0i to\u00e1n AI \u1edf quy m\u00f4 doanh nghi\u1ec7p.<\/p>\n

D\u1ecbch v\u1ee5 cho thu\u00ea Cloud GPU v\u00e0 Server GPU<\/strong><\/h2>\n

N\u1ebfu b\u1ea1n c\u1ea7n thu\u00ea ho\u1eb7c mua GPU NVIDIA A100, h\u00e3y b\u1ee9t ph\u00e1 hi\u1ec7u su\u1ea5t AI c\u00f9ng gi\u1ea3i ph\u00e1p NVIDIA A100 Cloud GPU<\/strong><\/a> ti\u1ebft ki\u1ec7m<\/em>\u00a0v\u00e0 Server GPU<\/strong><\/a> hi\u1ec7u n\u0103ng cao t\u1ea1i VNSO<\/strong><\/em>.<\/p>\n

\u0110i\u1ec1n th\u00f4ng tin, nh\u1eadn ngay b\u00e1o gi\u00e1, t\u01b0 v\u1ea5n tri\u1ec3n khai ho\u00e0n to\u00e0n mi\u1ec5n ph\u00ed!<\/em><\/strong><\/p>\n

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