{"id":24219,"date":"2026-04-05T12:01:01","date_gmt":"2026-04-05T05:01:01","guid":{"rendered":"https:\/\/vnso.vn\/?p=24219"},"modified":"2026-04-05T12:01:01","modified_gmt":"2026-04-05T05:01:01","slug":"kien-truc-blackwell-la-gi-buoc-tien-moi-cua-gpu-ai","status":"publish","type":"post","link":"https:\/\/vnso.vn\/en\/kien-truc-blackwell-la-gi-buoc-tien-moi-cua-gpu-ai\/","title":{"rendered":"Ki\u1ebfn tr\u00fac Blackwell l\u00e0 g\u00ec? B\u01b0\u1edbc ti\u1ebfn m\u1edbi c\u1ee7a GPU AI"},"content":{"rendered":"

Khi chi ph\u00ed hu\u1ea5n luy\u1ec7n v\u00e0 v\u1eadn h\u00e0nh AI ng\u00e0y c\u00e0ng t\u0103ng, \u0111\u1eb7c bi\u1ec7t v\u1edbi c\u00e1c m\u00f4 h\u00ecnh ng\u00f4n ng\u1eef l\u1edbn (LLM), b\u00e0i to\u00e1n kh\u00f4ng c\u00f2n n\u1eb1m \u1edf vi\u1ec7c \u201cc\u00f3 GPU hay kh\u00f4ng\u201d m\u00e0 l\u00e0 \u201cGPU c\u00f3 \u0111\u1ee7 hi\u1ec7u qu\u1ea3 \u0111\u1ec3 scale hay kh\u00f4ng\u201d. Th\u1ef1c t\u1ebf cho th\u1ea5y, vi\u1ec7c hu\u1ea5n luy\u1ec7n m\u1ed9t m\u00f4 h\u00ecnh quy m\u00f4 h\u00e0ng tr\u0103m t\u1ef7 tham s\u1ed1 c\u00f3 th\u1ec3 ti\u00eau t\u1ed1n h\u00e0ng tri\u1ec7u USD ti\u1ec1n h\u1ea1 t\u1ea7ng, trong \u0111\u00f3 ph\u1ea7n l\u1edbn chi ph\u00ed \u0111\u1ebfn t\u1eeb n\u0103ng l\u01b0\u1ee3ng, b\u0103ng th\u00f4ng b\u1ed9 nh\u1edb v\u00e0 kh\u1ea3 n\u0103ng m\u1edf r\u1ed9ng c\u1ee5m GPU. \u0110\u00e2y ch\u00ednh l\u00e0 b\u1ed1i c\u1ea3nh khi\u1ebfn NVIDIA<\/span><\/span> gi\u1edbi thi\u1ec7u ki\u1ebfn tr\u00fac Blackwell \u2013 m\u1ed9t b\u01b0\u1edbc chuy\u1ec3n t\u1eeb GPU \u0111\u01a1n l\u1ebb sang n\u1ec1n t\u1ea3ng AI quy m\u00f4 c\u00f4ng nghi\u1ec7p.<\/p>\n

Ki\u1ebfn tr\u00fac Blackwell l\u00e0 g\u00ec?<\/h2>\n

Blackwell l\u00e0 ki\u1ebfn tr\u00fac GPU th\u1ebf h\u1ec7 m\u1edbi c\u1ee7a NVIDIA hay c\u00f2n g\u1ecdi l\u00e0 Blackwell Architecture<\/span><\/span>, k\u1ebf nhi\u1ec7m Hopper<\/span><\/span>, \u0111\u01b0\u1ee3c thi\u1ebft k\u1ebf chuy\u00ean bi\u1ec7t cho c\u00e1c workload AI hi\u1ec7n \u0111\u1ea1i nh\u01b0 hu\u1ea5n luy\u1ec7n v\u00e0 suy lu\u1eadn m\u00f4 h\u00ecnh ng\u00f4n ng\u1eef l\u1edbn, generative AI v\u00e0 h\u1ec7 th\u1ed1ng AI doanh nghi\u1ec7p. Kh\u00f4ng gi\u1ed1ng c\u00e1c th\u1ebf h\u1ec7 tr\u01b0\u1edbc t\u1eadp trung v\u00e0o hi\u1ec7u n\u0103ng \u0111\u01a1n GPU, Blackwell \u0111\u01b0\u1ee3c x\u00e2y d\u1ef1ng v\u1edbi t\u01b0 duy \u201cAI infrastructure\u201d, ngh\u0129a l\u00e0 to\u00e0n b\u1ed9 ki\u1ebfn tr\u00fac t\u1eeb ph\u1ea7n c\u1ee9ng \u0111\u1ebfn ph\u1ea7n m\u1ec1m \u0111\u1ec1u ph\u1ee5c v\u1ee5 cho kh\u1ea3 n\u0103ng m\u1edf r\u1ed9ng \u1edf quy m\u00f4 data center.<\/p>\n

\u0110i\u1ec3m c\u1ed1t l\u00f5i n\u1eb1m \u1edf vi\u1ec7c Blackwell kh\u00f4ng c\u00f2n l\u00e0 m\u1ed9t GPU \u0111\u1ed9c l\u1eadp, m\u00e0 l\u00e0 m\u1ed9t ph\u1ea7n trong h\u1ec7 sinh th\u00e1i bao g\u1ed3m CPU Grace, k\u1ebft n\u1ed1i NVLink, h\u1ec7 th\u1ed1ng NVSwitch v\u00e0 c\u00e1c c\u1ee5m m\u00e1y d\u1ea1ng rack-scale nh\u01b0 DGX ho\u1eb7c NVL. \u0110\u00e2y l\u00e0 c\u00e1ch ti\u1ebfp c\u1eadn gi\u00fap doanh nghi\u1ec7p tri\u1ec3n khai AI theo h\u01b0\u1edbng \u201cfactory\u201d thay v\u00ec t\u1eebng node ri\u00eang l\u1ebb.<\/p>\n

\"Ki\u1ebfn<\/a><\/p>\n

Nh\u1eefng thay \u0111\u1ed5i ki\u1ebfn tr\u00fac mang t\u00ednh n\u1ec1n t\u1ea3ng<\/h2>\n

M\u1ed9t trong nh\u1eefng n\u00e2ng c\u1ea5p quan tr\u1ecdng nh\u1ea5t c\u1ee7a Blackwell n\u1eb1m \u1edf Tensor Core th\u1ebf h\u1ec7 th\u1ee9 5. N\u1ebfu nh\u01b0 tr\u01b0\u1edbc \u0111\u00e2y Hopper<\/span><\/span> t\u1eadp trung v\u00e0o FP8, th\u00ec Blackwell m\u1edf r\u1ed9ng xu\u1ed1ng FP4 v\u00e0 FP6 \u2013 c\u00e1c \u0111\u1ecbnh d\u1ea1ng s\u1ed1 h\u1ecdc c\u00f3 \u0111\u1ed9 ch\u00ednh x\u00e1c th\u1ea5p h\u01a1n nh\u01b0ng hi\u1ec7u qu\u1ea3 h\u01a1n cho AI inference. \u0110i\u1ec1u n\u00e0y gi\u00fap t\u0103ng throughput x\u1eed l\u00fd l\u00ean kho\u1ea3ng 1.5 l\u1ea7n so v\u1edbi th\u1ebf h\u1ec7 tr\u01b0\u1edbc, \u0111\u1ed3ng th\u1eddi gi\u1ea3m \u0111\u00e1ng k\u1ec3 chi ph\u00ed v\u1eadn h\u00e0nh tr\u00ean m\u1ed7i truy v\u1ea5n AI. Trong b\u1ed1i c\u1ea3nh c\u00e1c d\u1ecbch v\u1ee5 AI ph\u1ea3i x\u1eed l\u00fd h\u00e0ng tri\u1ec7u request m\u1ed7i ng\u00e0y, s\u1ef1 kh\u00e1c bi\u1ec7t n\u00e0y chuy\u1ec3n tr\u1ef1c ti\u1ebfp th\u00e0nh l\u1ee3i th\u1ebf kinh t\u1ebf.<\/p>\n

M\u1ed9t thay \u0111\u1ed5i l\u1edbn kh\u00e1c l\u00e0 thi\u1ebft k\u1ebf dual-die. Thay v\u00ec gi\u1edbi h\u1ea1n trong m\u1ed9t khu\u00f4n chip, Blackwell s\u1eed d\u1ee5ng hai die GPU k\u1ebft n\u1ed1i v\u1edbi nhau th\u00f4ng qua NVLink t\u1ed1c \u0111\u1ed9 cao, t\u1ea1o th\u00e0nh m\u1ed9t GPU logic duy nh\u1ea5t. C\u00e1ch ti\u1ebfp c\u1eadn n\u00e0y gi\u00fap v\u01b0\u1ee3t qua gi\u1edbi h\u1ea1n v\u1eadt l\u00fd c\u1ee7a transistor density, \u0111\u1ed3ng th\u1eddi m\u1edf r\u1ed9ng \u0111\u00e1ng k\u1ec3 kh\u1ea3 n\u0103ng t\u00ednh to\u00e1n m\u00e0 kh\u00f4ng c\u1ea7n thay \u0111\u1ed5i qu\u00e1 nhi\u1ec1u v\u1ec1 ki\u1ebfn tr\u00fac l\u1eadp tr\u00ecnh.<\/p>\n

B\u1ed9 nh\u1edb c\u0169ng l\u00e0 y\u1ebfu t\u1ed1 \u0111\u01b0\u1ee3c n\u00e2ng c\u1ea5p m\u1ea1nh. Blackwell s\u1eed d\u1ee5ng HBM3E v\u1edbi b\u0103ng th\u00f4ng cao h\u01a1n \u0111\u00e1ng k\u1ec3 so v\u1edbi HBM3, gi\u00fap gi\u1ea3m bottleneck khi x\u1eed l\u00fd c\u00e1c m\u00f4 h\u00ecnh l\u1edbn. Trong AI hi\u1ec7n \u0111\u1ea1i, memory bandwidth th\u01b0\u1eddng l\u00e0 \u0111i\u1ec3m ngh\u1ebdn l\u1edbn h\u01a1n c\u1ea3 compute, v\u00ec vi\u1ec7c di chuy\u1ec3n d\u1eef li\u1ec7u gi\u1eefa GPU v\u00e0 b\u1ed9 nh\u1edb chi\u1ebfm ph\u1ea7n l\u1edbn th\u1eddi gian x\u1eed l\u00fd. Vi\u1ec7c c\u1ea3i thi\u1ec7n b\u0103ng th\u00f4ng gi\u00fap t\u0103ng hi\u1ec7u su\u1ea5t th\u1ef1c t\u1ebf, kh\u00f4ng ch\u1ec9 tr\u00ean benchmark m\u00e0 c\u1ea3 trong m\u00f4i tr\u01b0\u1eddng production.<\/p>\n

>>> Xem th\u00eam Thu\u00ea Server GPU NVIDIA A100 PCIe 40GB ch\u1ec9 v\u1edbi 30K \u0111\/gi\u1edd<\/a><\/strong><\/p>\n

NVLink v\u00e0 b\u01b0\u1edbc chuy\u1ec3n sang AI quy m\u00f4 c\u00f4ng nghi\u1ec7p<\/h2>\n

N\u1ebfu ch\u1ec9 nh\u00ecn Blackwell nh\u01b0 m\u1ed9t GPU m\u1ea1nh h\u01a1n, s\u1ebd b\u1ecf l\u1ee1 \u0111i\u1ec3m quan tr\u1ecdng nh\u1ea5t: kh\u1ea3 n\u0103ng scale. V\u1edbi NVLink th\u1ebf h\u1ec7 m\u1edbi, c\u00e1c GPU Blackwell c\u00f3 th\u1ec3 k\u1ebft n\u1ed1i v\u1edbi nhau trong c\u00e1c c\u1ee5m l\u00ean t\u1edbi h\u00e0ng ch\u1ee5c ho\u1eb7c h\u00e0ng tr\u0103m GPU v\u1edbi b\u0103ng th\u00f4ng c\u1ef1c cao. \u0110i\u1ec1u n\u00e0y cho ph\u00e9p hu\u1ea5n luy\u1ec7n c\u00e1c m\u00f4 h\u00ecnh c\u00f3 quy m\u00f4 v\u01b0\u1ee3t xa gi\u1edbi h\u1ea1n c\u1ee7a m\u1ed9t node \u0111\u01a1n l\u1ebb.<\/p>\n

Trong c\u00e1c h\u1ec7 th\u1ed1ng nh\u01b0 NVL72, h\u00e0ng ch\u1ee5c GPU \u0111\u01b0\u1ee3c li\u00ean k\u1ebft th\u00e0nh m\u1ed9t c\u1ee5m th\u1ed1ng nh\u1ea5t, ho\u1ea1t \u0111\u1ed9ng g\u1ea7n nh\u01b0 m\u1ed9t si\u00eau m\u00e1y t\u00ednh AI. \u0110\u00e2y ch\u00ednh l\u00e0 n\u1ec1n t\u1ea3ng cho kh\u00e1i ni\u1ec7m \u201cAI factory\u201d \u2013 n\u01a1i d\u1eef li\u1ec7u \u0111\u01b0\u1ee3c \u0111\u01b0a v\u00e0o v\u00e0 m\u00f4 h\u00ecnh AI \u0111\u01b0\u1ee3c s\u1ea3n xu\u1ea5t li\u00ean t\u1ee5c, t\u01b0\u01a1ng t\u1ef1 nh\u01b0 d\u00e2y chuy\u1ec1n s\u1ea3n xu\u1ea5t c\u00f4ng nghi\u1ec7p.<\/p>\n

Hi\u1ec7u n\u0103ng v\u00e0 hi\u1ec7u qu\u1ea3 n\u0103ng l\u01b0\u1ee3ng: y\u1ebfu t\u1ed1 quy\u1ebft \u0111\u1ecbnh chi ph\u00ed AI<\/h2>\n

M\u1ed9t trong nh\u1eefng \u0111i\u1ec3m khi\u1ebfn Blackwell tr\u1edf n\u00ean quan tr\u1ecdng v\u1edbi doanh nghi\u1ec7p l\u00e0 hi\u1ec7u qu\u1ea3 n\u0103ng l\u01b0\u1ee3ng. Theo c\u00e1c th\u1eed nghi\u1ec7m th\u1ef1c t\u1ebf, ki\u1ebfn tr\u00fac n\u00e0y c\u00f3 th\u1ec3 c\u1ea3i thi\u1ec7n hi\u1ec7u su\u1ea5t tr\u00ean m\u1ed7i watt l\u00ean h\u01a1n 40% so v\u1edbi th\u1ebf h\u1ec7 tr\u01b0\u1edbc. \u0110i\u1ec1u n\u00e0y \u0111\u1eb7c bi\u1ec7t quan tr\u1ecdng trong data center, n\u01a1i chi ph\u00ed \u0111i\u1ec7n v\u00e0 l\u00e0m m\u00e1t chi\u1ebfm t\u1ef7 tr\u1ecdng l\u1edbn trong t\u1ed5ng chi ph\u00ed v\u1eadn h\u00e0nh.<\/p>\n

Khi k\u1ebft h\u1ee3p gi\u1eefa hi\u1ec7u n\u0103ng t\u0103ng kho\u1ea3ng 1.5 l\u1ea7n v\u00e0 hi\u1ec7u qu\u1ea3 n\u0103ng l\u01b0\u1ee3ng c\u1ea3i thi\u1ec7n \u0111\u00e1ng k\u1ec3, t\u1ed5ng chi ph\u00ed s\u1edf h\u1eefu (TCO) cho c\u00e1c workload AI c\u00f3 th\u1ec3 gi\u1ea3m m\u1ea1nh. \u0110\u00e2y l\u00e0 l\u00fd do Blackwell kh\u00f4ng ch\u1ec9 l\u00e0 n\u00e2ng c\u1ea5p k\u1ef9 thu\u1eadt, m\u00e0 l\u00e0 m\u1ed9t thay \u0111\u1ed5i v\u1ec1 m\u1eb7t kinh t\u1ebf trong vi\u1ec7c tri\u1ec3n khai AI.<\/p>\n

So s\u00e1nh Blackwell v\u00e0 Hopper: kh\u00e1c bi\u1ec7t n\u1eb1m \u1edf t\u01b0 duy thi\u1ebft k\u1ebf<\/h2>\n

N\u1ebfu Hopper<\/span><\/span> \u0111\u00e1nh d\u1ea5u b\u01b0\u1edbc chuy\u1ec3n sang AI-first GPU, th\u00ec Blackwell l\u00e0 b\u01b0\u1edbc ti\u1ebfp theo: AI-scale infrastructure. Hopper v\u1eabn ph\u00f9 h\u1ee3p cho training v\u00e0 inference, nh\u01b0ng khi m\u00f4 h\u00ecnh v\u01b0\u1ee3t qu\u00e1 quy m\u00f4 nh\u1ea5t \u0111\u1ecbnh, c\u00e1c h\u1ea1n ch\u1ebf v\u1ec1 memory, interconnect v\u00e0 efficiency b\u1eaft \u0111\u1ea7u l\u1ed9 r\u00f5.<\/p>\n

Blackwell gi\u1ea3i quy\u1ebft c\u00e1c v\u1ea5n \u0111\u1ec1 n\u00e0y b\u1eb1ng c\u00e1ch thi\u1ebft k\u1ebf l\u1ea1i to\u00e0n b\u1ed9 stack. T\u1eeb vi\u1ec7c h\u1ed7 tr\u1ee3 precision th\u1ea5p h\u01a1n, t\u0103ng b\u0103ng th\u00f4ng b\u1ed9 nh\u1edb, \u0111\u1ebfn kh\u1ea3 n\u0103ng k\u1ebft n\u1ed1i GPU \u1edf quy m\u00f4 l\u1edbn, t\u1ea5t c\u1ea3 \u0111\u1ec1u h\u01b0\u1edbng t\u1edbi m\u1ed9t m\u1ee5c ti\u00eau: gi\u1ea3m chi ph\u00ed tr\u00ean m\u1ed7i token AI \u0111\u01b0\u1ee3c t\u1ea1o ra.<\/p>\n

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

\u1ee8ng d\u1ee5ng th\u1ef1c t\u1ebf c\u1ee7a Blackwell trong doanh nghi\u1ec7p<\/h2>\n

Trong m\u00f4i tr\u01b0\u1eddng doanh nghi\u1ec7p, Blackwell kh\u00f4ng ch\u1ec9 d\u00e0nh cho c\u00e1c c\u00f4ng ty c\u00f4ng ngh\u1ec7 l\u1edbn. C\u00e1c l\u0129nh v\u1ef1c nh\u01b0 t\u00e0i ch\u00ednh, s\u1ea3n xu\u1ea5t, th\u01b0\u01a1ng m\u1ea1i \u0111i\u1ec7n t\u1eed hay logistics \u0111\u1ec1u c\u00f3 th\u1ec3 h\u01b0\u1edfng l\u1ee3i t\u1eeb kh\u1ea3 n\u0103ng x\u1eed l\u00fd AI nhanh v\u00e0 hi\u1ec7u qu\u1ea3 h\u01a1n.<\/p>\n

V\u1edbi c\u00e1c h\u1ec7 th\u1ed1ng chatbot, tr\u1ee3 l\u00fd AI ho\u1eb7c ph\u00e2n t\u00edch d\u1eef li\u1ec7u l\u1edbn, vi\u1ec7c chuy\u1ec3n sang n\u1ec1n t\u1ea3ng Blackwell gi\u00fap gi\u1ea3m latency v\u00e0 t\u0103ng kh\u1ea3 n\u0103ng x\u1eed l\u00fd \u0111\u1ed3ng th\u1eddi. Trong khi \u0111\u00f3, c\u00e1c doanh nghi\u1ec7p ph\u00e1t tri\u1ec3n m\u00f4 h\u00ecnh ri\u00eang c\u00f3 th\u1ec3 r\u00fat ng\u1eafn th\u1eddi gian hu\u1ea5n luy\u1ec7n t\u1eeb v\u00e0i tu\u1ea7n xu\u1ed1ng c\u00f2n v\u00e0i ng\u00e0y, t\u00f9y thu\u1ed9c v\u00e0o quy m\u00f4 h\u1ea1 t\u1ea7ng.<\/p>\n

\u0110\u1eb7c bi\u1ec7t, trong b\u1ed1i c\u1ea3nh AI \u0111ang chuy\u1ec3n t\u1eeb th\u1eed nghi\u1ec7m sang v\u1eadn h\u00e0nh th\u1ef1c t\u1ebf, y\u1ebfu t\u1ed1 \u1ed5n \u0111\u1ecbnh v\u00e0 kh\u1ea3 n\u0103ng scale tr\u1edf n\u00ean quan tr\u1ecdng h\u01a1n hi\u1ec7u n\u0103ng \u0111\u01a1n l\u1ebb. \u0110\u00e2y ch\u00ednh l\u00e0 \u0111i\u1ec3m Blackwell th\u1ec3 hi\u1ec7n r\u00f5 l\u1ee3i th\u1ebf.<\/p>\n

>>> Xem th\u00eam DGX Platform – N\u1ec1n t\u1ea3ng AI d\u00e0nh ri\u00eang cho doanh nghi\u1ec7p<\/a><\/strong><\/p>\n

Khi n\u00e0o doanh nghi\u1ec7p n\u00ean c\u00e2n nh\u1eafc Blackwell?<\/h2>\n

Blackwell ph\u00f9 h\u1ee3p v\u1edbi c\u00e1c doanh nghi\u1ec7p \u0111\u00e3 b\u01b0\u1edbc qua giai \u0111o\u1ea1n th\u1eed nghi\u1ec7m AI v\u00e0 \u0111ang \u0111\u1ed1i m\u1eb7t v\u1edbi c\u00e1c v\u1ea5n \u0111\u1ec1 nh\u01b0 chi ph\u00ed inference cao, h\u1ec7 th\u1ed1ng kh\u00f4ng scale \u0111\u01b0\u1ee3c ho\u1eb7c th\u1eddi gian training qu\u00e1 d\u00e0i. V\u1edbi c\u00e1c workload nh\u1ecf ho\u1eb7c th\u1eed nghi\u1ec7m ban \u0111\u1ea7u, vi\u1ec7c s\u1eed d\u1ee5ng GPU th\u1ebf h\u1ec7 tr\u01b0\u1edbc ho\u1eb7c Cloud GPU v\u1eabn l\u00e0 l\u1ef1a ch\u1ecdn h\u1ee3p l\u00fd h\u01a1n v\u1ec1 chi ph\u00ed.<\/p>\n

Tuy nhi\u00ean, khi AI tr\u1edf th\u00e0nh m\u1ed9t ph\u1ea7n c\u1ed1t l\u00f5i trong v\u1eadn h\u00e0nh, vi\u1ec7c \u0111\u1ea7u t\u01b0 v\u00e0o n\u1ec1n t\u1ea3ng nh\u01b0 Blackwell gi\u00fap doanh nghi\u1ec7p t\u1ed1i \u01b0u chi ph\u00ed d\u00e0i h\u1ea1n v\u00e0 \u0111\u1ea3m b\u1ea3o kh\u1ea3 n\u0103ng m\u1edf r\u1ed9ng trong t\u01b0\u01a1ng lai.<\/p>\n

T\u1ed5ng k\u1ebft v\u1ec1 ki\u1ebfn tr\u00fac Blackwell<\/h2>\n

Ki\u1ebfn tr\u00fac Blackwell kh\u00f4ng ch\u1ec9 l\u00e0 m\u1ed9t b\u1ea3n n\u00e2ng c\u1ea5p GPU, m\u00e0 l\u00e0 s\u1ef1 thay \u0111\u1ed5i c\u00e1ch x\u00e2y d\u1ef1ng v\u00e0 v\u1eadn h\u00e0nh h\u1ea1 t\u1ea7ng AI. V\u1edbi hi\u1ec7u n\u0103ng cao h\u01a1n, ti\u00eau th\u1ee5 \u0111i\u1ec7n n\u0103ng th\u1ea5p h\u01a1n v\u00e0 kh\u1ea3 n\u0103ng m\u1edf r\u1ed9ng v\u01b0\u1ee3t tr\u1ed9i, Blackwell \u0111\u1eb7t n\u1ec1n m\u00f3ng cho c\u00e1c h\u1ec7 th\u1ed1ng AI quy m\u00f4 c\u00f4ng nghi\u1ec7p.<\/p>\n

Trong b\u1ed1i c\u1ea3nh c\u1ea1nh tranh ng\u00e0y c\u00e0ng d\u1ef1a v\u00e0o t\u1ed1c \u0111\u1ed9 v\u00e0 chi ph\u00ed x\u1eed l\u00fd AI, vi\u1ec7c l\u1ef1a ch\u1ecdn \u0111\u00fang n\u1ec1n t\u1ea3ng h\u1ea1 t\u1ea7ng s\u1ebd quy\u1ebft \u0111\u1ecbnh tr\u1ef1c ti\u1ebfp \u0111\u1ebfn l\u1ee3i th\u1ebf c\u1ee7a doanh nghi\u1ec7p. Blackwell, v\u00ec v\u1eady, kh\u00f4ng \u0111\u01a1n thu\u1ea7n l\u00e0 c\u00f4ng ngh\u1ec7 m\u1edbi m\u00e0 l\u00e0 m\u1ed9t b\u01b0\u1edbc chuy\u1ec3n chi\u1ebfn l\u01b0\u1ee3c trong k\u1ef7 nguy\u00ean AI.<\/p>\n

Tri\u1ec3n khai AI hi\u1ec7u qu\u1ea3 v\u1edbi Cloud GPU, Server GPU NVIDIA t\u1ea1i VNSO<\/h2>\n

Trong th\u1ef1c t\u1ebf, kh\u00f4ng ph\u1ea3i doanh nghi\u1ec7p n\u00e0o c\u0169ng s\u1eb5n s\u00e0ng \u0111\u1ea7u t\u01b0 ngay h\u1ea1 t\u1ea7ng ph\u1ea7n c\u1ee9ng cao c\u1ea5p nh\u01b0 Blackwell hay x\u00e2y d\u1ef1ng AI data center ri\u00eang. B\u00e0i to\u00e1n ph\u1ed5 bi\u1ebfn h\u01a1n l\u00e0 l\u00e0m sao ti\u1ebfp c\u1eadn s\u1ee9c m\u1ea1nh GPU c\u1ee7a NVIDIA<\/span><\/span> m\u1ed9t c\u00e1ch linh ho\u1ea1t, t\u1ed1i \u01b0u chi ph\u00ed v\u00e0 c\u00f3 th\u1ec3 scale ngay khi c\u1ea7n. \u0110\u00e2y ch\u00ednh l\u00e0 l\u00fd do m\u00f4 h\u00ecnh Cloud GPU tr\u1edf th\u00e0nh l\u1ef1a ch\u1ecdn ph\u00f9 h\u1ee3p cho ph\u1ea7n l\u1edbn doanh nghi\u1ec7p hi\u1ec7n nay.<\/p>\n

V\u1edbi d\u1ecbch v\u1ee5 Cloud GPU t\u1ea1i VNSO, doanh nghi\u1ec7p c\u00f3 th\u1ec3 khai th\u00e1c s\u1ee9c m\u1ea1nh c\u1ee7a c\u00e1c d\u00f2ng GPU NVIDIA t\u1eeb A100, H100 \u0111\u1ebfn c\u00e1c c\u1ea5u h\u00ecnh t\u1ed1i \u01b0u cho AI training v\u00e0 inference m\u00e0 kh\u00f4ng c\u1ea7n \u0111\u1ea7u t\u01b0 ban \u0111\u1ea7u l\u1edbn.<\/p>\n

\u0110i\u1ec3m kh\u00e1c bi\u1ec7t kh\u00f4ng ch\u1ec9 n\u1eb1m \u1edf ph\u1ea7n c\u1ee9ng, m\u00e0 c\u00f2n \u1edf c\u00e1ch tri\u1ec3n khai. Thay v\u00ec ph\u1ea3i t\u1ef1 c\u1ea5u h\u00ecnh m\u00f4i tr\u01b0\u1eddng ph\u1ee9c t\u1ea1p, VNSO cung c\u1ea5p h\u1ec7 th\u1ed1ng s\u1eb5n s\u00e0ng cho AI v\u1edbi CUDA, driver, framework \u0111\u01b0\u1ee3c t\u1ed1i \u01b0u s\u1eb5n. Trong b\u1ed1i c\u1ea3nh chi ph\u00ed AI ng\u00e0y c\u00e0ng ph\u1ee5 thu\u1ed9c v\u00e0o hi\u1ec7u qu\u1ea3 v\u1eadn h\u00e0nh, vi\u1ec7c s\u1eed d\u1ee5ng Cloud GPU gi\u00fap doanh nghi\u1ec7p chuy\u1ec3n t\u1eeb m\u00f4 h\u00ecnh CAPEX sang OPEX, ch\u1ec9 tr\u1ea3 chi ph\u00ed theo m\u1ee9c s\u1eed d\u1ee5ng th\u1ef1c t\u1ebf.<\/p>\n

N\u1ebfu doanh nghi\u1ec7p \u0111ang g\u1eb7p c\u00e1c v\u1ea5n \u0111\u1ec1 nh\u01b0 th\u1eddi gian training qu\u00e1 l\u00e2u, h\u1ec7 th\u1ed1ng kh\u00f4ng \u0111\u1ee7 t\u00e0i nguy\u00ean \u0111\u1ec3 ch\u1ea1y m\u00f4 h\u00ecnh l\u1edbn ho\u1eb7c chi ph\u00ed inference t\u0103ng cao, Cloud GPU t\u1ea1i VNSO l\u00e0 gi\u1ea3i ph\u00e1p tr\u1ef1c ti\u1ebfp \u0111\u1ec3 gi\u1ea3i quy\u1ebft. Thay v\u00ec \u0111\u1ea7u t\u01b0 h\u1ea1 t\u1ea7ng ph\u1ee9c t\u1ea1p, doanh nghi\u1ec7p c\u00f3 th\u1ec3 b\u1eaft \u0111\u1ea7u ngay v\u1edbi m\u00f4i tr\u01b0\u1eddng GPU m\u1ea1nh m\u1ebd, linh ho\u1ea1t v\u00e0 s\u1eb5n s\u00e0ng m\u1edf r\u1ed9ng theo nhu c\u1ea7u AI trong t\u01b0\u01a1ng lai.<\/p>\n

>>> Li\u00ean h\u1ec7 ngay nh\u1eadn t\u01b0 v\u1ea5n, b\u00e1o gi\u00e1<\/strong>\u00a0Server AI\/GPU, Cloud GPU VNSO<\/strong><\/a><\/p>\n

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