{"id":24389,"date":"2026-04-17T17:29:47","date_gmt":"2026-04-17T10:29:47","guid":{"rendered":"https:\/\/vnso.vn\/?p=24389"},"modified":"2026-04-17T17:29:47","modified_gmt":"2026-04-17T10:29:47","slug":"nvidia-a100-pcie-la-gi-phan-tich-cau-hinh-ung-dung-thuc-te","status":"publish","type":"post","link":"https:\/\/vnso.vn\/en\/nvidia-a100-pcie-la-gi-phan-tich-cau-hinh-ung-dung-thuc-te\/","title":{"rendered":"NVIDIA A100 PCIe l\u00e0 g\u00ec? Ph\u00e2n t\u00edch c\u1ea5u h\u00ecnh, \u1ee9ng d\u1ee5ng th\u1ef1c t\u1ebf"},"content":{"rendered":"

Gi\u1eefa nhi\u1ec1u l\u1ef1a ch\u1ecdn tr\u00ean th\u1ecb tr\u01b0\u1eddng, NVIDIA A100 PCIe l\u00e0t ti\u00eau chu\u1ea9n tri\u1ec3n khai ph\u1ed5 bi\u1ebfn nh\u1edd c\u00e2n b\u1eb1ng t\u1ed1t gi\u1eefa hi\u1ec7u n\u0103ng, chi ph\u00ed v\u00e0 t\u00ednh linh ho\u1ea1t. C\u00f9ng VNSO<\/a> t\u00ecm hi\u1ec3u A100 PCIe th\u1ef1c ch\u1ea5t l\u00e0 g\u00ec, m\u1ea1nh \u1edf \u0111\u00e2u, v\u00e0 v\u00ec sao n\u00f3 v\u1eabn \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng r\u1ed9ng r\u00e3i trong c\u00e1c h\u1ec7 th\u1ed1ng AI.<\/p>\n

NVIDIA A100 PCIe l\u00e0 g\u00ec?<\/h2>\n

NVIDIA A100 PCIe<\/span><\/span> l\u00e0 GPU trung t\u00e2m d\u1eef li\u1ec7u (datacenter GPU) thu\u1ed9c ki\u1ebfn tr\u00fac Ampere, \u0111\u01b0\u1ee3c thi\u1ebft k\u1ebf d\u01b0\u1edbi d\u1ea1ng card PCIe \u0111\u1ec3 d\u1ec5 d\u00e0ng tri\u1ec3n khai trong server ti\u00eau chu\u1ea9n.<\/p>\n

Kh\u00e1c v\u1edbi GPU gaming, A100 PCIe kh\u00f4ng ph\u1ee5c v\u1ee5 hi\u1ec3n th\u1ecb h\u00ecnh \u1ea3nh m\u00e0 t\u1eadp trung ho\u00e0n to\u00e0n v\u00e0o t\u00ednh to\u00e1n. GPU n\u00e0y \u0111\u01b0\u1ee3c x\u00e2y d\u1ef1ng \u0111\u1ec3 x\u1eed l\u00fd c\u00e1c workload n\u1eb7ng nh\u01b0 machine learning, deep learning, ph\u00e2n t\u00edch d\u1eef li\u1ec7u l\u1edbn v\u00e0 m\u00f4 ph\u1ecfng khoa h\u1ecdc.<\/p>\n

\u0110i\u1ec3m quan tr\u1ecdng c\u1ea7n hi\u1ec3u NVIDIA A100 PCIe l\u00e0 phi\u00ean b\u1ea3n \u201clinh ho\u1ea1t v\u00e0 t\u1ed1i \u01b0u chi ph\u00ed\u201d c\u1ee7a A100, trong khi phi\u00ean b\u1ea3n SXM h\u01b0\u1edbng \u0111\u1ebfn hi\u1ec7u n\u0103ng t\u1ed1i \u0111a trong c\u00e1c h\u1ec7 th\u1ed1ng DGX\/HGX.<\/p>\n

>>> Xem chi ti\u1ebft t\u1ed5ng th\u1ec3 to\u00e0n b\u1ed9 th\u00f4ng tin v\u1ec1 GPU NVIDIA A100 t\u1ea1i: NVIDIA A100 l\u00e0 g\u00ec? Ph\u00e2n t\u00edch chi ti\u1ebft t\u1eeb A-Z<\/a><\/strong><\/p>\n

\"nvidia-a100-pcie-40gb\"<\/a>

H\u00ecnh \u1ea3nh 1x GPU NVIDIA A100 PCIe 40GB<\/p><\/div>\n

Th\u00f4ng s\u1ed1 k\u1ef9 thu\u1eadt, c\u1ea5u h\u00ecnh NVIDIA A100 PCIe (40GB & 80GB)<\/h2>\n

A100 PCIe c\u00f3 hai phi\u00ean b\u1ea3n ch\u00ednh l\u00e0 40GB v\u00e0 80GB, ph\u1ee5c v\u1ee5 c\u00e1c nhu c\u1ea7u kh\u00e1c nhau t\u1eeb inference \u0111\u1ebfn training model l\u1edbn. \u0110i\u1ec3m \u0111\u00e1ng ch\u00fa \u00fd l\u00e0 phi\u00ean b\u1ea3n 80GB \u0111\u1ea1t b\u0103ng th\u00f4ng g\u1ea7n 2 TB\/s, thu\u1ed9c nh\u00f3m cao nh\u1ea5t trong GPU datacenter.<\/p>\n

\n
\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Th\u00f4ng s\u1ed1<\/th>\nA100 PCIe 40GB<\/th>\nA100 PCIe 80GB<\/th>\n<\/tr>\n<\/thead>\n
VRAM<\/td>\n40GB HBM2e<\/td>\n80GB HBM2e<\/td>\n<\/tr>\n
Memory bandwidth<\/td>\n~1.56 TB\/s<\/td>\n~1.94 TB\/s<\/td>\n<\/tr>\n
CUDA cores<\/td>\n6912<\/td>\n6912<\/td>\n<\/tr>\n
Tensor cores<\/td>\n432<\/td>\n432<\/td>\n<\/tr>\n
FP32<\/td>\n~19.5 TFLOPS<\/td>\n~19.5 TFLOPS<\/td>\n<\/tr>\n
Tensor (FP16\/BF16)<\/td>\n~312 TFLOPS<\/td>\n~312 TFLOPS<\/td>\n<\/tr>\n
INT8<\/td>\n~624 TOPS<\/td>\n~624 TOPS<\/td>\n<\/tr>\n
TDP<\/td>\n~250W<\/td>\n~300W<\/td>\n<\/tr>\n
PCIe<\/td>\nGen4 x16<\/td>\nGen4 x16<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n

V\u00ec sao A100 PCIe m\u1ea1nh cho AI v\u00e0 LLM?<\/h2>\n

Ki\u1ebfn tr\u00fac Ampere, n\u1ec1n t\u1ea3ng s\u1ee9c m\u1ea1nh c\u1ee7a A100<\/h3>\n

A100 \u0111\u01b0\u1ee3c x\u00e2y d\u1ef1ng tr\u00ean ki\u1ebfn tr\u00fac Ampere v\u1edbi GPU GA100, s\u1ea3n xu\u1ea5t tr\u00ean ti\u1ebfn tr\u00ecnh 7nm v\u1edbi kho\u1ea3ng 54,2 t\u1ef7 transistor. \u0110\u00e2y l\u00e0 m\u1ed9t trong nh\u1eefng chip GPU l\u1edbn nh\u1ea5t t\u1eebng \u0111\u01b0\u1ee3c s\u1ea3n xu\u1ea5t. C\u1ea5u tr\u00fac n\u00e0y cho ph\u00e9p A100 x\u1eed l\u00fd song song h\u00e0ng ngh\u00ecn ph\u00e9p to\u00e1n AI, \u0111\u1eb7c bi\u1ec7t ph\u00f9 h\u1ee3p v\u1edbi training v\u00e0 inference.<\/p>\n

GPU n\u00e0y s\u1edf h\u1eefu:<\/p>\n

– 6912 CUDA cores<\/p>\n

– 432 Tensor cores th\u1ebf h\u1ec7 3<\/p>\n

– B\u1ed9 nh\u1edb HBM2e b\u0103ng th\u00f4ng c\u1ef1c cao<\/p>\n

\"Ki\u1ebfn<\/a>

Ki\u1ebfn tr\u00fac Ampere c\u1ee7a NVIDIA<\/p><\/div>\n

Tensor Core th\u1ebf h\u1ec7 3 t\u0103ng t\u1ed1c AI<\/h3>\n

A100 h\u1ed7 tr\u1ee3 nhi\u1ec1u \u0111\u1ecbnh d\u1ea1ng t\u00ednh to\u00e1n nh\u01b0 TF32, FP16, BF16 v\u00e0 INT8. Trong \u0111\u00f3, TF32 cho ph\u00e9p t\u0103ng t\u1ed1c training m\u00e0 kh\u00f4ng c\u1ea7n ch\u1ec9nh s\u1eeda code, gi\u00fap doanh nghi\u1ec7p tri\u1ec3n khai AI nhanh h\u01a1n. Trong benchmark th\u1ef1c t\u1ebf, A100 c\u00f3 th\u1ec3 t\u0103ng t\u1ed1c inference l\u00ean \u0111\u1ebfn 249 l\u1ea7n so v\u1edbi CPU trong c\u00e1c b\u00e0i to\u00e1n NLP nh\u01b0 BERT .<\/p>\n

B\u1ed9 nh\u1edb HBM2e v\u00e0 bandwidth c\u1ef1c l\u1edbn<\/h3>\n

M\u1ed9t trong nh\u1eefng y\u1ebfu t\u1ed1 quy\u1ebft \u0111\u1ecbnh hi\u1ec7u n\u0103ng AI kh\u00f4ng n\u1eb1m \u1edf TFLOPS m\u00e0 l\u00e0 bandwidth hay c\u00f2n \u0111\u01b0\u1ee3c g\u1ecdi l\u00e0 b\u0103ng th\u00f4ng.<\/p>\n

A100 PCIe cung c\u1ea5p:<\/strong><\/p>\n

– 1.5\u20132 TB\/s bandwidth<\/p>\n

– Bus memory 5120-bit<\/p>\n

\u0110i\u1ec1u n\u00e0y gi\u00fap t\u1ea3i model l\u1edbn nhanh h\u01a1n. \u0110\u1ed3ng th\u1eddi gi\u1ea3m bottleneck khi inference LLM v\u00e0 t\u0103ng throughput h\u1ec7 th\u1ed1ng.<\/p>\n

Chia nh\u1ecf GPU cho cloud AI v\u1edbi c\u00f4ng ngh\u1ec7 MIG<\/h3>\n

M\u1ed9t trong nh\u1eefng c\u00f4ng ngh\u1ec7 quan tr\u1ecdng nh\u1ea5t c\u1ee7a A100 l\u00e0 MIG (Multi-Instance GPU). A100 c\u00f3 th\u1ec3 chia th\u00e0nh t\u1ed1i \u0111a 7 GPU \u0111\u1ed9c l\u1eadp, m\u1ed7i instance c\u00f3 t\u00e0i nguy\u00ean ri\u00eang bi\u1ec7t.<\/p>\n

\u0110i\u1ec1u n\u00e0y t\u1ea1o ra l\u1ee3i th\u1ebf l\u1edbn cho Cloud GPU, AI SaaS v\u00e0 h\u1ec7 th\u1ed1ng nhi\u1ec1u ng\u01b0\u1eddi d\u00f9ng (multi-user system). M\u1ed9t GPU c\u00f3 th\u1ec3 ph\u1ee5c v\u1ee5 nhi\u1ec1u kh\u00e1ch h\u00e0ng c\u00f9ng l\u00fac m\u00e0 v\u1eabn \u0111\u1ea3m b\u1ea3o hi\u1ec7u n\u0103ng.<\/p>\n

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

Kh\u1ea3 n\u0103ng m\u1edf r\u1ed9ng v\u1edbi NVLink<\/h3>\n

A100 PCIe h\u1ed7 tr\u1ee3 PCIe Gen4: ~64 GB\/s<\/strong> v\u00e0 NVLink: ~600 GB\/s<\/strong> gi\u1eefa 2 GPU. Trong h\u1ec7 th\u1ed1ng nhi\u1ec1u GPU, NVLink gi\u00fap t\u0103ng t\u1ed1c \u0111\u1ed9 trao \u0111\u1ed5i d\u1eef li\u1ec7u, gi\u1ea3m \u0111\u1ed9 tr\u1ec5 khi training model l\u1edbn.<\/p>\n

So s\u00e1nh A100 PCIe v\u00e0 SXM: n\u00ean ch\u1ecdn lo\u1ea1i n\u00e0o?<\/h2>\n

Phi\u00ean b\u1ea3n PCIe v\u00e0 SXM s\u1eed d\u1ee5ng c\u00f9ng GPU GA100 nh\u01b0ng kh\u00e1c nhau v\u1ec1 c\u00e1ch tri\u1ec3n khai. Trong th\u1ef1c t\u1ebf, PCIe th\u01b0\u1eddng \u0111\u1ea1t kho\u1ea3ng 85 \u0111\u1ebfn 90% hi\u1ec7u n\u0103ng SXM \u1edf workload \u0111\u01a1n GPU nh\u01b0ng t\u1ed1i \u01b0u h\u01a1n v\u1ec1 chi ph\u00ed v\u00e0 v\u1eadn h\u00e0nh. D\u01b0\u1edbi \u0111\u00e2y l\u00e0 b\u1ea3ng so s\u00e1nh t\u1ed5ng qu\u00e1t 2 phi\u00ean b\u1ea3n PCIe v\u00e0 SXM.<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n
Ti\u00eau ch\u00ed<\/th>\nNVIDIA A100 PCIe 40GB<\/th>\nNVIDIA A100 SXM4 80GB<\/th>\n<\/tr>\n<\/thead>\n
Ki\u1ec3u k\u1ebft n\u1ed1i<\/td>\nPCIe Gen4 x16<\/td>\nSXM (g\u1eafn tr\u1ef1c ti\u1ebfp main HGX)<\/td>\n<\/tr>\n
Tri\u1ec3n khai<\/td>\nServer ti\u00eau chu\u1ea9n<\/td>\nServer chuy\u00ean d\u1ee5ng (HGX\/DGX)<\/td>\n<\/tr>\n
TDP<\/td>\n~250\u2013300W<\/td>\n~400W+<\/td>\n<\/tr>\n
Hi\u1ec7u n\u0103ng<\/td>\n~85\u201390% SXM<\/td>\nCao nh\u1ea5t (full power)<\/td>\n<\/tr>\n
Bandwidth GPU-GPU<\/td>\n~64 GB\/s (PCIe)<\/td>\n~600 GB\/s (NVLink)<\/td>\n<\/tr>\n
Kh\u1ea3 n\u0103ng scale<\/td>\nH\u1ea1n ch\u1ebf<\/td>\nR\u1ea5t m\u1ea1nh (NVSwitch)<\/td>\n<\/tr>\n
Chi ph\u00ed tri\u1ec3n khai<\/td>\nTh\u1ea5p h\u01a1n<\/td>\nCao h\u01a1n \u0111\u00e1ng k\u1ec3<\/td>\n<\/tr>\n
Use case ph\u00f9 h\u1ee3p<\/td>\nCloud GPU, doanh nghi\u1ec7p<\/td>\nHyperscale, AI lab l\u1edbn<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Khi n\u00e0o doanh nghi\u1ec7p n\u00ean d\u00f9ng A100 PCIe?<\/h2>\n

A100 PCIe ph\u00f9 h\u1ee3p khi doanh nghi\u1ec7p c\u1ea7n tri\u1ec3n khai AI th\u1ef1c t\u1ebf nh\u01b0ng ch\u01b0a c\u1ea7n \u0111\u1ebfn h\u1ea1 t\u1ea7ng hyperscale.<\/p>\n

C\u00e1c tr\u01b0\u1eddng h\u1ee3p ph\u1ed5 bi\u1ebfn g\u1ed3m:<\/p>\n

– ch\u1ea1y inference LLM production<\/p>\n

– training model v\u1eeba v\u00e0 l\u1edbn<\/p>\n

– x\u00e2y d\u1ef1ng h\u1ec7 th\u1ed1ng AI n\u1ed9i b\u1ed9<\/p>\n

– cung c\u1ea5p d\u1ecbch v\u1ee5 cloud GPU<\/p>\n

Trong nhi\u1ec1u t\u00ecnh hu\u1ed1ng, A100 mang l\u1ea1i t\u1ef7 l\u1ec7 hi\u1ec7u n\u0103ng tr\u00ean chi ph\u00ed t\u1ed1t h\u01a1n so v\u1edbi c\u00e1c GPU th\u1ebf h\u1ec7 m\u1edbi nh\u01b0 H100<\/strong>, \u0111\u1eb7c bi\u1ec7t \u1edf b\u00e0i to\u00e1n inference.<\/p>\n

>>> Xem th\u00eam So s\u00e1nh A100 NVIDIA GPU v\u00e0 NVIDIA H100: N\u00ean ch\u1ecdn GPU n\u00e0o?<\/a><\/strong><\/p>\n

A100 PCIe d\u00f9ng \u0111\u1ec3 l\u00e0m g\u00ec trong th\u1ef1c t\u1ebf?<\/h3>\n

Trong m\u00f4i tr\u01b0\u1eddng production, A100 PCIe th\u01b0\u1eddng \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng cho 4 nh\u00f3m workload ch\u00ednh.<\/p>\n

\u0110\u1ea7u ti\u00ean l\u00e0 AI v\u00e0 Machine Learning. GPU n\u00e0y \u0111\u01b0\u1ee3c d\u00f9ng \u0111\u1ec3 training c\u00e1c m\u00f4 h\u00ecnh NLP, computer vision v\u00e0 \u0111\u1eb7c bi\u1ec7t l\u00e0 inference cho LLM.<\/p>\n

Ti\u1ebfp theo l\u00e0 cloud GPU. Nh\u1edd MIG, A100 tr\u1edf th\u00e0nh l\u1ef1a ch\u1ecdn ph\u1ed5 bi\u1ebfn cho c\u00e1c d\u1ecbch v\u1ee5 cho thu\u00ea GPU, n\u01a1i nhi\u1ec1u ng\u01b0\u1eddi d\u00f9ng c\u00f3 th\u1ec3 chia s\u1ebb t\u00e0i nguy\u00ean.<\/p>\n

Trong l\u0129nh v\u1ef1c d\u1eef li\u1ec7u l\u1edbn, A100 k\u1ebft h\u1ee3p v\u1edbi RAPIDS ho\u1eb7c Spark gi\u00fap t\u0103ng t\u1ed1c x\u1eed l\u00fd d\u1eef li\u1ec7u l\u00ean nhi\u1ec1u l\u1ea7n so v\u1edbi CPU truy\u1ec1n th\u1ed1ng .<\/p>\n

Cu\u1ed1i c\u00f9ng l\u00e0 HPC, n\u01a1i A100 \u0111\u01b0\u1ee3c d\u00f9ng trong m\u00f4 ph\u1ecfng v\u1eadt l\u00fd, t\u00e0i ch\u00ednh \u0111\u1ecbnh l\u01b0\u1ee3ng v\u00e0 nghi\u00ean c\u1ee9u khoa h\u1ecdc.<\/p>\n

\"Tri\u1ec3n<\/a>

Server GPU NVIDIA A100 t\u1ea1i VNSO<\/p><\/div>\n

Ti\u00eau chu\u1ea9n v\u00e0ng c\u1ee7a h\u1ea1 t\u1ea7ng AI<\/h2>\n

NVIDIA A100 PCIe kh\u00f4ng ph\u1ea3i GPU m\u1edbi nh\u1ea5t, nh\u01b0ng v\u1eabn l\u00e0 ti\u00eau chu\u1ea9n v\u00e0ng trong h\u1ea1 t\u1ea7ng AI production.<\/p>\n

V\u1edbi ki\u1ebfn tr\u00fac Ampere, Tensor Core m\u1ea1nh m\u1ebd, b\u1ed9 nh\u1edb HBM2e b\u0103ng th\u00f4ng cao v\u00e0 kh\u1ea3 n\u0103ng chia nh\u1ecf GPU b\u1eb1ng MIG, A100 PCIe mang l\u1ea1i s\u1ef1 c\u00e2n b\u1eb1ng gi\u1eefa hi\u1ec7u n\u0103ng, chi ph\u00ed v\u00e0 kh\u1ea3 n\u0103ng tri\u1ec3n khai.<\/p>\n

\u0110\u00e2y l\u00e0 l\u00fd do t\u1ea1i sao ph\u1ea7n l\u1edbn h\u1ec7 th\u1ed1ng AI doanh nghi\u1ec7p hi\u1ec7n nay v\u1eabn v\u1eadn h\u00e0nh tr\u00ean A100, \u0111\u1eb7c bi\u1ec7t trong c\u00e1c workload inference v\u00e0 cloud GPU.<\/p>\n

Tri\u1ec3n khai NVIDIA A100 PCIe nhanh h\u01a1n, hi\u1ec7u qu\u1ea3 h\u01a1n<\/h2>\n

N\u1ebfu doanh nghi\u1ec7p c\u1ea7n khai th\u00e1c s\u1ee9c m\u1ea1nh c\u1ee7a A100 PCIe m\u00e0 kh\u00f4ng mu\u1ed1n \u0111\u1ea7u t\u01b0 h\u1ea1 t\u1ea7ng ph\u1ee9c t\u1ea1p, Cloud GPU<\/a> v\u00e0 Server GPU<\/a> t\u1ea1i VNSO gi\u00fap tri\u1ec3n khai nhanh ch\u1ec9 trong v\u00e0i ph\u00fat. H\u1ec7 th\u1ed1ng \u0111\u01b0\u1ee3c t\u1ed1i \u01b0u s\u1eb5n m\u00f4i tr\u01b0\u1eddng AI, h\u1ed7 tr\u1ee3 chia t\u00e0i nguy\u00ean b\u1eb1ng MIG, ph\u00f9 h\u1ee3p cho c\u1ea3 inference, training v\u00e0 multi-user.<\/p>\n

V\u1edbi c\u00e1c nhu c\u1ea7u l\u1edbn h\u01a1n, Server GPU ri\u00eang cung c\u1ea5p hi\u1ec7u n\u0103ng \u1ed5n \u0111\u1ecbnh, d\u1ec5 d\u00e0ng m\u1edf r\u1ed9ng v\u00e0 ki\u1ec3m so\u00e1t to\u00e0n b\u1ed9 h\u1ea1 t\u1ea7ng AI n\u1ed9i b\u1ed9. G\u1eedi y\u00eau c\u1ea7u ngay \u0111\u1ec3 nh\u1eadn t\u01b0 v\u1ea5n v\u00e0 tri\u1ec3n khai h\u1ea1 t\u1ea7ng AI ch\u1ec9 trong v\u00e0i ph\u00fat.<\/p>\n

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