{"id":24522,"date":"2026-04-28T11:06:37","date_gmt":"2026-04-28T04:06:37","guid":{"rendered":"https:\/\/vnso.vn\/?p=24522"},"modified":"2026-04-28T11:06:37","modified_gmt":"2026-04-28T04:06:37","slug":"nvidia-a100-40gb-gpu-la-gi-tat-ca-nhung-gi-ban-can-biet","status":"publish","type":"post","link":"https:\/\/vnso.vn\/en\/nvidia-a100-40gb-gpu-la-gi-tat-ca-nhung-gi-ban-can-biet\/","title":{"rendered":"NVIDIA A100 40GB GPU l\u00e0 g\u00ec? T\u1ea5t c\u1ea3 nh\u1eefng g\u00ec b\u1ea1n c\u1ea7n bi\u1ebft"},"content":{"rendered":"
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T\u1eeb c\u00e1c m\u00f4 h\u00ecnh AI \u0111\u1ebfn nh\u1eefng b\u00e0i to\u00e1n HPC quy m\u00f4 l\u1edbn, NVIDIA A100 40GB Tensor Core GPU l\u00e0 n\u1ec1n t\u1ea3ng trung t\u00e2m gi\u00fap r\u00fat ng\u1eafn th\u1eddi gian hu\u1ea5n luy\u1ec7n t\u1eeb h\u00e0ng tu\u1ea7n xu\u1ed1ng c\u00f2n v\u00e0i ng\u00e0y ho\u1eb7c gi\u1edd. \u0110\u00e2y c\u0169ng l\u00e0 l\u00fd do v\u00ec sao GPU n\u00e0y nhanh ch\u00f3ng \u0111\u01b0\u1ee3c c\u00e1c doanh nghi\u1ec7p v\u00e0 trung t\u00e2m d\u1eef li\u1ec7u l\u1ef1a ch\u1ecdn ngay sau khi ra m\u1eaft b\u1edfi NVIDIA<\/span><\/span>.<\/p>\n

V\u1eady NVIDIA A100 40GB th\u1ef1c ch\u1ea5t l\u00e0 g\u00ec, s\u1ee9c m\u1ea1nh n\u1eb1m \u1edf \u0111\u00e2u v\u00e0 v\u00ec sao n\u00f3 v\u1eabn gi\u1eef vai tr\u00f2 quan tr\u1ecdng trong h\u1ea1 t\u1ea7ng AI hi\u1ec7n \u0111\u1ea1i? N\u1ed9i dung d\u01b0\u1edbi \u0111\u00e2y s\u1ebd ph\u00e2n t\u00edch to\u00e0n di\u1ec7n t\u1eeb ki\u1ebfn tr\u00fac, hi\u1ec7u n\u0103ng cho \u0111\u1ebfn c\u00e1ch GPU n\u00e0y \u0111\u01b0\u1ee3c tri\u1ec3n khai trong th\u1ef1c t\u1ebf.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n

NVIDIA A100 40GB l\u00e0 g\u00ec?<\/h2>\n

GPU A100 \u0111\u01b0\u1ee3c NVIDIA<\/span><\/span> ch\u00ednh th\u1ee9c c\u00f4ng b\u1ed1 v\u00e0o th\u00e1ng 5\/2020 t\u1ea1i s\u1ef1 ki\u1ec7n NVIDIA GTC 2020<\/span><\/span>, \u0111\u00e1nh d\u1ea5u b\u01b0\u1edbc chuy\u1ec3n quan tr\u1ecdng c\u1ee7a GPU t\u1eeb \u0111\u1ed3 h\u1ecda sang n\u1ec1n t\u1ea3ng h\u1ea1 t\u1ea7ng AI. Ngay t\u1eeb th\u1eddi \u0111i\u1ec3m ra m\u1eaft, A100 \u0111\u00e3 tr\u1edf th\u00e0nh ti\u00eau chu\u1ea9n cho c\u00e1c h\u1ec7 th\u1ed1ng AI training v\u00e0 HPC tr\u00ean to\u00e0n c\u1ea7u.<\/p>\n

NVIDIA A100 40GB l\u00e0 GPU chuy\u00ean d\u1ee5ng cho data center, thu\u1ed9c ki\u1ebfn tr\u00fac Ampere, \u0111\u01b0\u1ee3c thi\u1ebft k\u1ebf \u0111\u1ec3 x\u1eed l\u00fd c\u00e1c workload n\u1eb7ng nh\u01b0 AI, machine learning, ph\u00e2n t\u00edch d\u1eef li\u1ec7u v\u00e0 t\u00ednh to\u00e1n hi\u1ec7u n\u0103ng cao. GPU n\u00e0y kh\u00f4ng ph\u1ee5c v\u1ee5 hi\u1ec3n th\u1ecb h\u00ecnh \u1ea3nh m\u00e0 t\u1eadp trung ho\u00e0n to\u00e0n v\u00e0o n\u0103ng l\u1ef1c t\u00ednh to\u00e1n song song.<\/p>\n

>>> Xem th\u00eam Thu\u00ea NVIDIA A100 Cloud GPU gi\u00e1 ch\u1ec9 t\u1eeb 59k\/gi\u1edd<\/a><\/strong><\/p>\n

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

H\u00ecnh \u1ea3nh c\u1ee7a 1x GPU NVIDIA A100 PCIe 40GB<\/p><\/div>\n

C\u00e1c phi\u00ean b\u1ea3n c\u1ee7a NVIDIA A100<\/h3>\n
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B\u00ean c\u1ea1nh phi\u00ean b\u1ea3n 40GB, NVIDIA c\u00f2n ph\u00e1t tri\u1ec3n nhi\u1ec1u bi\u1ebfn th\u1ec3 kh\u00e1c \u0111\u1ec3 \u0111\u00e1p \u1ee9ng c\u00e1c nhu c\u1ea7u tri\u1ec3n khai kh\u00e1c nhau. Phi\u00ean b\u1ea3n A100 80GB ra m\u1eaft sau \u0111\u00f3 t\u1eadp trung v\u00e0o c\u00e1c m\u00f4 h\u00ecnh l\u1edbn h\u01a1n v\u1edbi dung l\u01b0\u1ee3ng b\u1ed9 nh\u1edb g\u1ea5p \u0111\u00f4i v\u00e0 b\u0103ng th\u00f4ng cao h\u01a1n, ph\u00f9 h\u1ee3p cho LLM v\u00e0 workload quy m\u00f4 l\u1edbn.<\/p>\n

V\u1ec1 h\u00ecnh th\u1ee9c tri\u1ec3n khai, A100 c\u00f3 hai chu\u1ea9n ch\u00ednh l\u00e0 PCIe v\u00e0 SXM. B\u1ea3n PCIe ph\u00f9 h\u1ee3p v\u1edbi c\u00e1c server ph\u1ed5 th\u00f4ng, d\u1ec5 tri\u1ec3n khai v\u00e0 linh ho\u1ea1t trong n\u00e2ng c\u1ea5p. Trong khi \u0111\u00f3, b\u1ea3n SXM \u0111\u01b0\u1ee3c thi\u1ebft k\u1ebf cho h\u1ec7 th\u1ed1ng hi\u1ec7u n\u0103ng cao, s\u1eed d\u1ee5ng NVLink v\u00e0 NVSwitch \u0111\u1ec3 k\u1ebft n\u1ed1i nhi\u1ec1u GPU v\u1edbi b\u0103ng th\u00f4ng l\u1edbn, t\u1ed1i \u01b0u cho AI training quy m\u00f4 l\u1edbn v\u00e0 HPC.<\/p>\n

S\u1ef1 \u0111a d\u1ea1ng n\u00e0y gi\u00fap A100 tr\u1edf th\u00e0nh n\u1ec1n t\u1ea3ng linh ho\u1ea1t, c\u00f3 th\u1ec3 m\u1edf r\u1ed9ng t\u1eeb h\u1ec7 th\u1ed1ng nh\u1ecf \u0111\u1ebfn c\u00e1c c\u1ee5m AI h\u00e0ng ngh\u00ecn GPU.<\/p>\n

B\u1ea3ng th\u00f4ng s\u1ed1 c\u1ea5u h\u00ecnh NVIDIA A100 40GB (SXM v\u00e0 PCIe)<\/h2>\n

A100 40GB l\u00e0 GPU l\u00e0 GPU c\u00e2n b\u1eb1ng t\u1ed1t gi\u1eefa compute, memory v\u00e0 kh\u1ea3 n\u0103ng scale. Trong h\u1ec7 th\u1ed1ng AI th\u1ef1c t\u1ebf, hi\u1ec7u n\u0103ng kh\u00f4ng ch\u1ec9 ph\u1ee5 thu\u1ed9c TFLOPS m\u00e0 c\u00f2n ph\u1ee5 thu\u1ed9c v\u00e0o bandwidth, interconnect v\u00e0 kh\u1ea3 n\u0103ng chia t\u00e0i nguy\u00ean. \u0110\u00e2y ch\u00ednh l\u00e0 l\u00fd do A100 v\u1eabn \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng r\u1ed9ng r\u00e3i d\u00f9 \u0111\u00e3 c\u00f3 th\u1ebf h\u1ec7 m\u1edbi h\u01a1n.<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
H\u1ea1ng m\u1ee5c<\/th>\nNVIDIA A100 40GB PCIe<\/th>\nNVIDIA A100 40GB SXM<\/th>\n<\/tr>\n<\/thead>\n
Ki\u1ebfn tr\u00fac<\/td>\nAmpere (GA100)<\/td>\nAmpere (GA100)<\/td>\n<\/tr>\n
Ti\u1ebfn tr\u00ecnh<\/td>\n7nm (TSMC)<\/td>\n7nm (TSMC)<\/td>\n<\/tr>\n
S\u1ed1 transistor<\/td>\n~54.2 t\u1ef7<\/td>\n~54.2 t\u1ef7<\/td>\n<\/tr>\n
S\u1ed1 SM<\/td>\n108<\/td>\n108<\/td>\n<\/tr>\n
CUDA Cores<\/td>\n6912<\/td>\n6912<\/td>\n<\/tr>\n
Tensor Cores<\/td>\n432 (Gen 3)<\/td>\n432 (Gen 3)<\/td>\n<\/tr>\n
B\u1ed9 nh\u1edb<\/td>\n40GB HBM2e<\/td>\n40GB HBM2e<\/td>\n<\/tr>\n
Bus b\u1ed9 nh\u1edb<\/td>\n5120-bit<\/td>\n5120-bit<\/td>\n<\/tr>\n
Bandwidth<\/td>\n~1.56 TB\/s<\/td>\n~1.56 TB\/s<\/td>\n<\/tr>\n
L2 Cache<\/td>\n40 MB<\/td>\n40 MB<\/td>\n<\/tr>\n
FP32<\/td>\n~19.5 TFLOPS<\/td>\n~19.5 TFLOPS<\/td>\n<\/tr>\n
FP64<\/td>\n~9.7 TFLOPS<\/td>\n~9.7 TFLOPS<\/td>\n<\/tr>\n
Tensor (FP16\/BF16)<\/td>\n~312 TFLOPS<\/td>\n~312 TFLOPS<\/td>\n<\/tr>\n
TF32<\/td>\n~156 TFLOPS<\/td>\n~156 TFLOPS<\/td>\n<\/tr>\n
INT8<\/td>\n~624 TOPS<\/td>\n~624 TOPS<\/td>\n<\/tr>\n
Base Clock<\/td>\n~765 MHz<\/td>\n~1095 MHz<\/td>\n<\/tr>\n
Boost Clock<\/td>\n~1410 MHz<\/td>\n~1410 MHz<\/td>\n<\/tr>\n
TDP<\/td>\n250W<\/td>\n400W<\/td>\n<\/tr>\n
Giao ti\u1ebfp<\/td>\nPCIe 4.0 x16<\/td>\nSXM4<\/td>\n<\/tr>\n
NVLink<\/td>\nC\u00f3 (2 GPU)<\/td>\nC\u00f3 (multi GPU, NVSwitch)<\/td>\n<\/tr>\n
MIG<\/td>\nT\u1ed1i \u0111a 7 instance (~5GB\/instance)<\/td>\nT\u1ed1i \u0111a 7 instance (~5GB\/instance)<\/td>\n<\/tr>\n
Display Output<\/td>\nKh\u00f4ng<\/td>\nKh\u00f4ng<\/td>\n<\/tr>\n
Ng\u00e0y ra m\u1eaft<\/td>\n06\/2020<\/td>\n05\/2020<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n

Hi\u1ec7u su\u1ea5t AI \u0111\u1ea1t ti\u00eau chu\u1ea9n nhi\u1ec1u h\u1ea1 t\u1ea7ng AI ng\u00e0y nay<\/h2>\n

Ki\u1ebfn tr\u00fac Ampere<\/h3>\n

A100 40GB \u0111\u01b0\u1ee3c x\u00e2y d\u1ef1ng tr\u00ean chip GA100 v\u1edbi kho\u1ea3ng 54 t\u1ef7 transistor, s\u1ea3n xu\u1ea5t tr\u00ean ti\u1ebfn tr\u00ecnh 7nm. \u0110\u00e2y l\u00e0 m\u1ed9t trong nh\u1eefng GPU ph\u1ee9c t\u1ea1p nh\u1ea5t t\u1eebng \u0111\u01b0\u1ee3c ph\u00e1t tri\u1ec3n, v\u1edbi 108 SM (Streaming Multiprocessors) v\u00e0 h\u00e0ng ngh\u00ecn nh\u00e2n x\u1eed l\u00fd song song.<\/p>\n

\u0110i\u1ec3m kh\u00e1c bi\u1ec7t c\u1ee7a Ampere n\u1eb1m \u1edf c\u00e1ch t\u1ed1i \u01b0u cho ph\u00e9p t\u00ednh ma tr\u1eadn, y\u1ebfu t\u1ed1 c\u1ed1t l\u00f5i trong deep learning. \u0110i\u1ec1u n\u00e0y gi\u00fap A100 x\u1eed l\u00fd c\u00e1c b\u00e0i to\u00e1n AI hi\u1ec7u qu\u1ea3 h\u01a1n nhi\u1ec1u so v\u1edbi GPU truy\u1ec1n th\u1ed1ng.<\/p>\n

Tensor Core Gen 3<\/h3>\n

A100 s\u1eed d\u1ee5ng Tensor Core th\u1ebf h\u1ec7 th\u1ee9 3, h\u1ed7 tr\u1ee3 nhi\u1ec1u \u0111\u1ecbnh d\u1ea1ng t\u00ednh to\u00e1n nh\u01b0 FP32, TF32, FP16, BF16 v\u00e0 INT8. Nh\u1edd TF32, GPU c\u00f3 th\u1ec3 t\u0103ng t\u1ed1c training m\u00e0 kh\u00f4ng c\u1ea7n thay \u0111\u1ed5i code, gi\u00fap gi\u1ea3m \u0111\u00e1ng k\u1ec3 chi ph\u00ed tri\u1ec3n khai AI.<\/p>\n

Trong benchmark th\u1ef1c t\u1ebf, A100 c\u00f3 th\u1ec3 mang l\u1ea1i hi\u1ec7u n\u0103ng cao h\u01a1n t\u1edbi 20 l\u1ea7n so v\u1edbi th\u1ebf h\u1ec7 tr\u01b0\u1edbc trong c\u00e1c t\u00e1c v\u1ee5 AI training.<\/p>\n

V\u1edbi inference, GPU n\u00e0y c\u00f3 th\u1ec3 \u0111\u1ea1t hi\u1ec7u n\u0103ng cao h\u01a1n CPU t\u1edbi 249 l\u1ea7n trong c\u00e1c m\u00f4 h\u00ecnh nh\u01b0 BERT, cho th\u1ea5y s\u1ef1 ch\u00eanh l\u1ec7ch r\u1ea5t l\u1edbn gi\u1eefa ki\u1ebfn tr\u00fac GPU v\u00e0 CPU trong AI.<\/p>\n

>>> Xem th\u00eam Cloud GPU cho sinh vi\u00ean – Gi\u1ea3i ph\u00e1p l\u00e0m \u0111\u1ed3 \u00e1n t\u1ed1t nghi\u1ec7p ti\u1ebft ki\u1ec7m<\/a><\/strong><\/p>\n

\"C\u00e1c-th\u1ebf-h\u1ec7-NVIDIA-Tensor-Core-GPU\"<\/a>

C\u00e1c GPU NVIDIA s\u1eed d\u1ee5ng Tensor Core<\/p><\/div>\n

B\u1ed9 nh\u1edb 40GB HBM2e VRAM<\/h3>\n

M\u1ed9t trong nh\u1eefng \u0111i\u1ec3m quan tr\u1ecdng nh\u1ea5t c\u1ee7a A100 40GB l\u00e0 b\u1ed9 nh\u1edb HBM2e dung l\u01b0\u1ee3ng 40GB, v\u1edbi b\u0103ng th\u00f4ng kho\u1ea3ng 1.56 TB\/s. Con s\u1ed1 n\u00e0y cao h\u01a1n r\u1ea5t nhi\u1ec1u so v\u1edbi GDDR6 tr\u00ean GPU ph\u1ed5 th\u00f4ng, gi\u00fap gi\u1ea3m ngh\u1ebdn d\u1eef li\u1ec7u khi training model l\u1edbn.<\/p>\n

Trong c\u00e1c workload AI, GPU th\u01b0\u1eddng b\u1ecb gi\u1edbi h\u1ea1n b\u1edfi bandwidth h\u01a1n l\u00e0 compute, v\u00ec v\u1eady HBM2e mang l\u1ea1i l\u1ee3i th\u1ebf r\u00f5 r\u1ec7t. \u0110i\u1ec1u n\u00e0y \u0111\u1eb7c bi\u1ec7t quan tr\u1ecdng v\u1edbi c\u00e1c m\u00f4 h\u00ecnh l\u1edbn nh\u01b0 LLM, n\u01a1i d\u1eef li\u1ec7u ph\u1ea3i \u0111\u01b0\u1ee3c truy\u1ec1n li\u00ean t\u1ee5c gi\u1eefa b\u1ed9 nh\u1edb v\u00e0 Tensor Core.<\/p>\n

Hi\u1ec7u n\u0103ng t\u00ednh to\u00e1n th\u1ef1c t\u1ebf<\/h3>\n

A100 40GB cung c\u1ea5p c\u00e1c m\u1ee9c hi\u1ec7u n\u0103ng \u0111\u00e1ng ch\u00fa \u00fd:<\/p>\n

– FP32: kho\u1ea3ng 19.5 TFLOPS<\/p>\n

– FP64: kho\u1ea3ng 9.7 TFLOPS<\/p>\n

– Tensor (BF16\/FP16): h\u01a1n 300 TFLOPS<\/p>\n

\u0110\u00e2y l\u00e0 l\u00fd do GPU n\u00e0y kh\u00f4ng ch\u1ec9 ph\u1ee5c v\u1ee5 AI m\u00e0 c\u00f2n \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng trong HPC, v\u00ed d\u1ee5 nh\u01b0 m\u00f4 ph\u1ecfng v\u1eadt l\u00fd ho\u1eb7c nghi\u00ean c\u1ee9u khoa h\u1ecdc.<\/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

Multi-Instance GPU (MIG): T\u1ed1i \u01b0u t\u00e0i nguy\u00ean \u1edf c\u1ea5p h\u1ea1 t\u1ea7ng<\/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), cho ph\u00e9p chia m\u1ed9t GPU th\u00e0nh t\u1ed1i \u0111a 7 instance \u0111\u1ed9c l\u1eadp. V\u1edbi phi\u00ean b\u1ea3n 40GB, m\u1ed7i instance c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c ph\u00e2n b\u1ed5 kho\u1ea3ng 5GB VRAM.<\/p>\n

\u0110i\u1ec1u n\u00e0y gi\u00fap m\u1ed9t GPU c\u00f3 th\u1ec3 ph\u1ee5c v\u1ee5 nhi\u1ec1u workload c\u00f9ng l\u00fac, t\u1eeb \u0111\u00f3 t\u0103ng hi\u1ec7u su\u1ea5t s\u1eed d\u1ee5ng t\u00e0i nguy\u00ean trong m\u00f4i tr\u01b0\u1eddng cloud ho\u1eb7c data center. Trong nghi\u00ean c\u1ee9u th\u1ef1c t\u1ebf, vi\u1ec7c s\u1eed d\u1ee5ng MIG c\u00f3 th\u1ec3 ti\u1ebft ki\u1ec7m t\u1edbi 40% t\u00e0i nguy\u00ean GPU m\u00e0 v\u1eabn gi\u1eef nguy\u00ean throughput cho c\u00e1c m\u00f4 h\u00ecnh deep learning.<\/p>\n

Kh\u1ea3 n\u0103ng m\u1edf r\u1ed9ng trong h\u1ea1 t\u1ea7ng AI (Scale)<\/h3>\n

A100 kh\u00f4ng ho\u1ea1t \u0111\u1ed9ng \u0111\u1ed9c l\u1eadp m\u00e0 th\u01b0\u1eddng \u0111\u01b0\u1ee3c tri\u1ec3n khai theo c\u1ee5m v\u1edbi NVLink v\u00e0 NVSwitch, cho ph\u00e9p k\u1ebft n\u1ed1i nhi\u1ec1u GPU v\u1edbi b\u0103ng th\u00f4ng cao.<\/p>\n

Trong th\u1ef1c t\u1ebf, c\u00e1c h\u1ec7 th\u1ed1ng c\u00f3 th\u1ec3 scale l\u00ean h\u00e0ng ngh\u00ecn GPU A100 \u0111\u1ec3 hu\u1ea5n luy\u1ec7n m\u00f4 h\u00ecnh l\u1edbn. M\u1ed9t v\u00ed d\u1ee5 \u0111i\u1ec3n h\u00ecnh l\u00e0 b\u00e0i to\u00e1n BERT c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c gi\u1ea3i trong ch\u01b0a t\u1edbi m\u1ed9t ph\u00fat khi s\u1eed d\u1ee5ng 2048 GPU A100.<\/p>\n

\u1ee8ng d\u1ee5ng th\u1ef1c t\u1ebf c\u1ee7a NVIDIA A100 40GB<\/h2>\n

A100 40GB \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng trong nhi\u1ec1u l\u0129nh v\u1ef1c kh\u00e1c nhau, t\u1eeb AI \u0111\u1ebfn khoa h\u1ecdc: Trong AI v\u00e0 machine learning, GPU n\u00e0y ph\u1ee5c v\u1ee5 training v\u00e0 inference cho c\u00e1c m\u00f4 h\u00ecnh l\u1edbn, \u0111\u1eb7c bi\u1ec7t l\u00e0 NLP v\u00e0 computer vision. V\u1ec1 HPC, A100 gi\u00fap r\u00fat ng\u1eafn th\u1eddi gian m\u00f4 ph\u1ecfng t\u1eeb h\u00e0ng gi\u1edd xu\u1ed1ng ch\u1ec9 c\u00f2n v\u00e0i gi\u1edd, nh\u1edd kh\u1ea3 n\u0103ng x\u1eed l\u00fd m\u1ea1nh m\u1ebd. Trong data analytics, A100 \u0111\u01b0\u1ee3c t\u00edch h\u1ee3p v\u00e0o c\u00e1c h\u1ec7 th\u1ed1ng x\u1eed l\u00fd d\u1eef li\u1ec7u l\u1edbn, gi\u00fap t\u0103ng t\u1ed1c pipeline ph\u00e2n t\u00edch d\u1eef li\u1ec7u.<\/p>\n

>>> Xem th\u00eam C\u00e1ch kh\u1eafc ph\u1ee5c L\u1ed7i Out of Memory AI hi\u1ec7u qu\u1ea3 b\u1eb1ng GPU theo gi\u1edd<\/a><\/strong><\/p>\n

So s\u00e1nh A100 40GB v\u1edbi 80GB: C\u00f3 g\u00ec kh\u00e1c bi\u1ec7t?<\/h2>\n

Phi\u00ean b\u1ea3n 40GB v\u00e0 80GB c\u00f3 c\u00f9ng ki\u1ebfn tr\u00fac, nh\u01b0ng kh\u00e1c bi\u1ec7t ch\u00ednh n\u1eb1m \u1edf b\u1ed9 nh\u1edb v\u00e0 b\u0103ng th\u00f4ng.<\/p>\n

A100 80GB c\u00f3 b\u0103ng th\u00f4ng h\u01a1n 2 TB\/s v\u00e0 dung l\u01b0\u1ee3ng g\u1ea5p \u0111\u00f4i, cho ph\u00e9p x\u1eed l\u00fd dataset l\u1edbn h\u01a1n v\u00e0 t\u0103ng throughput trong m\u1ed9t s\u1ed1 workload t\u1edbi 1.25 l\u1ea7n so v\u1edbi b\u1ea3n 40GB<\/p>\n

Tuy nhi\u00ean, phi\u00ean b\u1ea3n 40GB v\u1eabn l\u00e0 l\u1ef1a ch\u1ecdn ph\u1ed5 bi\u1ebfn nh\u1edd c\u00e2n b\u1eb1ng t\u1ed1t gi\u1eefa chi ph\u00ed v\u00e0 hi\u1ec7u n\u0103ng.<\/p>\n

Vai tr\u00f2 c\u1ee7a A100 40GB trong h\u1ec7 sinh th\u00e1i AI<\/h2>\n

A100 40GB GPU l\u00e0 \u201c\u0111\u01a1n v\u1ecb compute ti\u00eau chu\u1ea9n\u201d trong h\u1ea1 t\u1ea7ng AI hi\u1ec7n \u0111\u1ea1i. N\u00f3 gi\u00fap doanh nghi\u1ec7p:<\/p>\n

– R\u00fat ng\u1eafn th\u1eddi gian training model<\/p>\n

– T\u0103ng t\u1ed1c inference trong production<\/p>\n

– T\u1ed1i \u01b0u chi ph\u00ed nh\u1edd chia t\u00e0i nguy\u00ean v\u1edbi MIG<\/p>\n

Trong nhi\u1ec1u n\u0103m, A100 ch\u00ednh l\u00e0 n\u1ec1n t\u1ea3ng cho c\u00e1c h\u1ec7 th\u1ed1ng AI l\u1edbn tr\u01b0\u1edbc khi c\u00e1c th\u1ebf h\u1ec7 m\u1edbi nh\u01b0 H100 xu\u1ea5t hi\u1ec7n.<\/p>\n

T\u1ed5ng k\u1ebft v\u1ec1 NVIDIA A100 40GB<\/h2>\n

NVIDIA A100 40GB l\u00e0 GPU data center \u0111\u01b0\u1ee3c thi\u1ebft k\u1ebf chuy\u00ean bi\u1ec7t cho AI v\u00e0 HPC, n\u1ed5i b\u1eadt v\u1edbi Tensor Core th\u1ebf h\u1ec7 m\u1edbi, b\u1ed9 nh\u1edb HBM2e b\u0103ng th\u00f4ng cao v\u00e0 kh\u1ea3 n\u0103ng chia t\u00e0i nguy\u00ean linh ho\u1ea1t v\u1edbi MIG.<\/p>\n

\u0110i\u1ec3m quan tr\u1ecdng c\u1ea7n hi\u1ec3u l\u00e0 A100 kh\u00f4ng ph\u1ea3i GPU d\u00e0nh cho c\u00e1 nh\u00e2n, m\u00e0 l\u00e0 th\u00e0nh ph\u1ea7n c\u1ee7a h\u1ea1 t\u1ea7ng AI quy m\u00f4 l\u1edbn. Gi\u00e1 tr\u1ecb th\u1ef1c c\u1ee7a n\u00f3 kh\u00f4ng n\u1eb1m \u1edf th\u00f4ng s\u1ed1 \u0111\u01a1n l\u1ebb m\u00e0 \u1edf kh\u1ea3 n\u0103ng scale, t\u1ed1i \u01b0u t\u00e0i nguy\u00ean v\u00e0 t\u0103ng t\u1ed1c to\u00e0n b\u1ed9 pipeline AI.<\/p>\n

>>> Xem th\u00eam\u00a0Cloud GPU theo gi\u1edd – Gi\u1ea3i ph\u00e1p t\u1ed1i \u01b0u cho Startup AI v\u1ed1n \u00edt<\/a><\/strong><\/p>\n

S\u1eed d\u1ee5ng GPU NVIDIA A100 40GB trong 5 ph\u00fat ch\u1ec9 v\u1edbi 59.000\u0111<\/h3>\n

Tri\u1ec3n khai AI v\u1edbi GPU NVIDIA A100 40GB kh\u00f4ng c\u00f2n l\u00e0 b\u00e0i to\u00e1n \u0111\u1ea7u t\u01b0 l\u1edbn. T\u1ea1i VNSO<\/span><\/span>, d\u1ecbch v\u1ee5 Cloud GPU A100 ch\u1ec9 t\u1eeb 59.000\u0111\/gi\u1edd gi\u00fap b\u1ea1n b\u1eaft \u0111\u1ea7u ngay l\u1eadp t\u1ee9c, kh\u00f4ng c\u1ea7n h\u1ea1 t\u1ea7ng, kh\u00f4ng chi ph\u00ed \u0111\u1ea7u t\u01b0 ban \u0111\u1ea7u.<\/p>\n

N\u1ebfu c\u1ea7n hi\u1ec7u n\u0103ng cao h\u01a1n cho h\u1ec7 th\u1ed1ng ri\u00eang, VNSO cung c\u1ea5p server GPU \u0111a d\u1ea1ng t\u1eeb A100 \u0111\u1ebfn c\u00e1c d\u00f2ng GPU AI m\u1edbi nh\u1ea5t, s\u1eb5n s\u00e0ng scale theo nhu c\u1ea7u doanh nghi\u1ec7p.<\/p>\n

>>> \u0110\u0103ng k\u00fd t\u1ef1 \u0111\u1ed9ng ho\u1eb7c Li\u00ean h\u1ec7 ngay \u0111\u1ec3 \u0111\u01b0\u1ee3c t\u01b0 v\u1ea5n c\u1ea5u h\u00ecnh ph\u00f9 h\u1ee3p.<\/strong><\/p>\n

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