{"id":3280,"date":"2019-12-03T16:00:02","date_gmt":"2019-12-03T16:00:02","guid":{"rendered":"https:\/\/www.thegioimaychu.vn\/blog\/?p=3280"},"modified":"2019-12-03T16:02:51","modified_gmt":"2019-12-03T16:02:51","slug":"tang-toc-i-o-cho-cac-cum-may-chu-da-gpu","status":"publish","type":"post","link":"https:\/\/thegioimaychu.vn\/blog\/ai-hpc\/tang-toc-i-o-cho-cac-cum-may-chu-da-gpu-p3280\/","title":{"rendered":"T\u1eafng t\u1ed1c I\/O cho c\u00e1c c\u1ee5m m\u00e1y ch\u1ee7 \u0111a GPU"},"content":{"rendered":"<style>.iblink{color:#975!important;}.iblink:hover{color:orange!important;}<\/style><p>Khi b\u00e0n \u0111\u1ebfn c\u00e1c m\u00e1y ch\u1ee7 t\u0103ng t\u1ed1c GPU, c\u00f3 th\u1ec3 n\u00f3i h\u1ec7 th\u1ed1ng DGX-2 c\u1ee7a Nvidia r\u1ea5t kh\u00f3 b\u1ecb \u0111\u00e1nh b\u1ea1i.\u00a016 GPU V100 c\u1ee7a n\u00f3 \u0111\u01b0\u1ee3c h\u00e0n l\u1ea1i c\u00f9ng v\u1edbi k\u1ebft n\u1ed1i NVLink cung c\u1ea5p 2 petaflops hi\u1ec7u n\u0103ng tensor floating point ho\u1eb7c 125 teraflops hi\u1ec7u n\u0103ng double precision.\u00a0Cung c\u1ea5p cho c\u00e1c GPU \u0111\u00f3 l\u00e0 m\u1ed9t n\u1eeda terabyte b\u1ed9 nh\u1edb HBM2 x\u1ebfp ch\u1ed3ng l\u00ean nhau c\u00f3 th\u1ec3 cung c\u1ea5p t\u1edbi 16 TB m\u1ed7i gi\u00e2y.\u00a0Nh\u01b0ng n\u1ebfu b\u1ea1n mu\u1ed1n k\u1ebft n\u1ed1i nhi\u1ec1u m\u00e1y ch\u1ee7 DGX-2 v\u1edbi nhau v\u00e0 ch\u1ea1y kh\u1ed1i l\u01b0\u1ee3ng c\u00f4ng vi\u1ec7c n\u1eb7ng v\u1ec1 I\/O th\u00f4ng qua c\u1ee5m \u0111\u00f3, b\u1ea1n s\u1ebd c\u1ea7n m\u1ed9t s\u1ed1 ph\u1ea7n c\u1ee9ng v\u00e0 ph\u1ea7n m\u1ec1m b\u1ed5 sung \u0111\u1ec3 \u0111\u1ea3m b\u1ea3o c\u00e1c GPU \u0111\u00f3 kh\u00f4ng b\u1ecb thi\u1ebfu d\u1eef li\u1ec7u gi\u1eefa ch\u1eebng.<\/p>\n<p>V\u1ec1 c\u01a1 b\u1ea3n, \u0111\u00f3 l\u00e0 v\u00ec DGX-2 \u0111\u01b0\u1ee3c thi\u1ebft k\u1ebf theo ki\u1ebfn \u200b\u200btr\u00fac scale-up (n\u00e2ng c\u1ea5p) ch\u1ee9 kh\u00f4ng ph\u1ea3i scale-out (m\u1edf r\u1ed9ng).\u00a0\u0110i\u1ec1u \u0111\u00f3 c\u00f3 ngh\u0129a l\u00e0 c\u00f3 m\u1ed9t n\u00fat th\u1eaft c\u1ed5 chai d\u1eef li\u1ec7u c\u1ed1 h\u1eefu v\u00e0o v\u00e0 ra kh\u1ecfi m\u00e1y ch\u1ee7 \u0111\u00f3.\u00a0May m\u1eafn thay, m\u1ed9t s\u1ed1 th\u1ee9 c\u1ea7n thi\u1ebft \u0111\u1ec3 c\u1ee5m DGX-2 gi\u1ea3i quy\u1ebft v\u1edbi m\u1ee9c I\/O cao hi\u1ec7n \u0111\u00e3 c\u00f3: Card giao ti\u1ebfp Mellanox NIC, cung c\u1ea5p ph\u1ea7n c\u1ee9ng giao ti\u1ebfp m\u1ea1ng hi\u1ec7u su\u1ea5t cao v\u00e0 h\u1ed7 tr\u1ee3 RDMA;\u00a0GPUDirect, c\u00f4ng ngh\u1ec7 RDMA h\u1ed7 tr\u1ee3 \u0111\u01b0\u1eddng k\u1ebft n\u1ed1i d\u1eef li\u1ec7u tr\u1ef1c ti\u1ebfp gi\u1eefa GPU v\u00e0 c\u00e1c thi\u1ebft b\u1ecb ngang h\u00e0ng qua PCI-Express;\u00a0Th\u01b0 vi\u1ec7n Nvidia Collective Communications Library (NCCL), m\u1ed9t package cung c\u1ea5p kh\u1ea3 n\u0103ng giao ti\u1ebfp \u0111\u01b0\u1ee3c t\u1ed1i \u01b0u h\u00f3a cho c\u00e1c thi\u1ebft l\u1eadp \u0111a GPU v\u00e0 \u0111a node;\u00a0v\u00e0 GPUDirect Storage, thi\u1ebft l\u1eadp \u0111\u01b0\u1eddng k\u1ebft n\u1ed1i tr\u1ef1c ti\u1ebfp gi\u1eefa b\u1ed9 nh\u1edb GPU v\u00e0 storage. (Tr\u00ean th\u1ef1c t\u1ebf, GPUDirect Storage hi\u1ec7n ch\u1ec9 kh\u1ea3 d\u1ee5ng cho nh\u1eefng kh\u00e1ch h\u00e0ng \u0111\u0103ng k\u00fd s\u1edbm v\u00e0 d\u1ef1 ki\u1ebfn s\u1ebd c\u00f3 m\u1eb7t r\u1ed9ng r\u00e3i h\u01a1n trong n\u1eeda \u0111\u1ea7u n\u0103m 2020).<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/thegioimaychu.vn\/blog\/wp-content\/uploads\/2019\/12\/tgmc-blog-5de68601735e8.png\" alt=\"\" \/><\/p>\n<p>Ph\u1ea7n c\u00f2n thi\u1ebfu cho \u0111\u1ebfn b\u00e2y gi\u1edd l\u00e0 m\u1ed9t l\u1edbp ph\u1ea7n m\u1ec1m k\u1ebft h\u1ee3p t\u1ea5t c\u1ea3 c\u00e1c y\u1ebfu t\u1ed1 n\u00e0y l\u1ea1i v\u1edbi nhau v\u00e0 cung c\u1ea5p kh\u1ea3 n\u0103ng t\u0103ng t\u1ed1c I\/O c\u1ee7a <a class=\"iblink\" href=\"\/storage\/\">h\u1ec7 th\u1ed1ng l\u01b0u tr\u1eef<\/a> cho c\u00e1c \u1ee9ng d\u1ee5ng GPU \u0111\u01b0\u1ee3c scale-out \u1edf quy m\u00f4 l\u1edbn.\u00a0T\u1ea1i SC19 tu\u1ea7n n\u00e0y, Gi\u00e1m \u0111\u1ed1c \u0111i\u1ec1u h\u00e0nh c\u1ee7a Nvidia, Jensen Huang, \u0111\u00e3 c\u00f4ng b\u1ed1 m\u1ed9t con m\u00e3nh th\u00fa nh\u01b0 v\u1eady, m\u00e0 c\u00f4ng ty g\u1ecdi l\u00e0 Magnum IO.\u00a0V\u1ec1 c\u01a1 b\u1ea3n, \u0111\u00e2y l\u00e0 b\u1ed9 ph\u1ea7n m\u1ec1m cung c\u1ea5p API \u0111\u01b0\u1ee3c thi\u1ebft k\u1ebf \u0111\u1ec3 gi\u1ea3i t\u1ecfa n\u00fat c\u1ed5 chai I \/ O cho c\u00e1c m\u00e1y ch\u1ee7 \u0111a GPU n\u00e0y.\u00a0Theo Huang, Magnum IO l\u00e0 m\u1ed9t trong nh\u1eefng \u0111i\u1ec1u l\u1edbn nh\u1ea5t ch\u00fang t\u00f4i \u0111\u00e3 l\u00e0m trong n\u0103m qua. L\u00ed thuy\u1ebft h\u1ee3p l\u00fd cho tuy\u00ean b\u1ed1 \u0111\u00f3 l\u00e0 r\u1ea5t \u00edt c\u00f4ng ngh\u1ec7 kh\u00e1c m\u00e0 h\u1ecd \u0111\u00e3 ph\u00e1t tri\u1ec3n g\u1ea7n \u0111\u00e2y c\u00f3 th\u1ec3 t\u0103ng t\u1ed1c kh\u1ed1i l\u01b0\u1ee3ng c\u00f4ng vi\u1ec7c t\u0103ng t\u1ed1c GPU l\u00ean m\u1ee9c \u0111\u1ed9 n\u00e0y &#8211; l\u00ean \u0111\u1ebfn 20X trong m\u1ed9t s\u1ed1 tr\u01b0\u1eddng h\u1ee3p.<\/p>\n<p>B\u1ed9 ph\u1ea7n m\u1ec1m m\u1edbi n\u00e0y nh\u1eb1m v\u00e0o c\u00e1c nh\u00e0 khoa h\u1ecdc v\u00e0 nh\u00e0 nghi\u00ean c\u1ee9u d\u1eef li\u1ec7u \u0111ang x\u1eed l\u00fd c\u00e1c b\u1ed9 d\u1eef li\u1ec7u l\u1edbn s\u1eed d\u1ee5ng trong <a class=\"iblink\" href=\"\/blog\/tag\/ai\/\">tr\u00ed tu\u1ec7 nh\u00e2n t\u1ea1o<\/a> v\u00e0 c\u00e1c \u1ee9ng d\u1ee5ng \u0111i\u1ec7n to\u00e1n hi\u1ec7u n\u0103ng cao kh\u00e1c.\u00a0\u0110\u1ec3 gi\u1ea3i quy\u1ebft v\u1ea5n \u0111\u1ec1 \u0111\u00f3, Magnum IO giao ti\u1ebfp v\u1edbi ng\u0103n x\u1ebfp ph\u1ea7n m\u1ec1m CUDA-X c\u1ee7a Nvidia, cung c\u1ea5p kh\u1ea3 n\u0103ng t\u0103ng t\u1ed1c GPU tr\u00ean m\u1ed9t lo\u1ea1t c\u00e1c g\u00f3i AI v\u00e0 HPC th\u01b0\u1eddng \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng.<\/p>\r\n\t\t\t\t<div class=\"tgmcad hor\" style=\"text-align:center;margin:30px 0; padding: 10px 0; border-radius:5px; border-top:solid 1px #ccc; border-bottom:solid 1px #ccc; \">\r\n\t\t\t\t<a href=\"https:\/\/thegioimaychu.vn\/giai-phap\/deep-learning\/\"><img decoding=\"async\" alt=\"Gi\u1ea3i ph\u00e1p h\u1ea1 t\u1ea7ng Deep Learning, Tr\u00ed tu\u1ec7 Nh\u00e2n t\u1ea1o - AI\" src=\"https:\/\/thegioimaychu.vn\/blog\/wp-content\/uploads\/2019\/11\/tgmc-deep-learning.jpg\" \/><\/a>\r\n\t\t\t\t<\/div>\n<p>L\u00e0 m\u1ed9t v\u1ea5n \u0111\u1ec1 th\u1ef1c t\u1ebf, <a class=\"iblink\" href=\"\/storage\/\">h\u1ec7 th\u1ed1ng l\u01b0u tr\u1eef<\/a> v\u00e0 t\u1eadp tin b\u00ean ngo\u00e0i c\u0169ng ph\u1ea3i gi\u1eef k\u1ebft th\u00fac c\u1ee7a m\u00f3n h\u1eddi hi\u1ec7u su\u1ea5t.\u00a0C\u00e1c \u0111\u1ed1i t\u00e1c l\u01b0u tr\u1eef ban \u0111\u1ea7u bao g\u1ed3m DataDirect Networks, Excelero, IBM Spectrum, Pure Storage v\u00e0 WekaIO, mang \u0111\u1ebfn cho b\u1ea1n c\u1ea3m gi\u00e1c v\u1ec1 m\u1ee9c \u0111\u1ed9 hi\u1ec7u n\u0103ng l\u01b0u tr\u1eef m\u00e0 Nvidia c\u00f3 trong t\u00e2m tr\u00ed.\u00a0\u00dd t\u01b0\u1edfng chung l\u00e0 t\u1eadn d\u1ee5ng l\u1ee3i th\u1ebf c\u1ee7a vi\u1ec7c l\u01b0u tr\u1eef kh\u00f4ng bay h\u01a1i (\u0111\u1eb7c bi\u1ec7t l\u00e0 NVM-Express), \u0111\u1ed9c l\u1eadp ho\u1eb7c trong c\u00e1c lo\u1ea1i v\u1ea3i m\u1ea1ng.<\/p>\n<p>\u1ede c\u1ea5p \u0111\u1ed9 ph\u1ea7n c\u1ee9ng, b\u0103ng th\u00f4ng d\u1eef li\u1ec7u c\u1ea7n thi\u1ebft \u0111\u01b0\u1ee3c ph\u00e2n ph\u1ed1i b\u1eb1ng c\u00e1ch trang b\u1ecb <a class=\"iblink\" href=\"\/server\/gpu-system\/\">m\u00e1y ch\u1ee7 GPU<\/a> v\u1edbi r\u1ea5t nhi\u1ec1u NIC nhanh, trong tr\u01b0\u1eddng h\u1ee3p Huang m\u00f4 t\u1ea3, s\u1eed d\u1ee5ng t\u00e1m b\u1ed9 \u0111i\u1ec1u h\u1ee3p Mellanox 100Gbps cho m\u1ed7i m\u00e1y ch\u1ee7 DGX-2.\u00a0C\u00f3 l\u1ebd, Magnum IO s\u1ebd ho\u1ea1t \u0111\u1ed9ng v\u1edbi c\u00e1c lo\u1ea1i m\u00e1y ch\u1ee7 \u0111a GPU kh\u00e1c, do Nvidia \u0111\u00e3 li\u1ec7t k\u00ea Cray \/ HPE, Dell EMC, IBM, Lenovo, Inspur, Penguin Computing, Atos v\u00e0 <a class=\"iblink\" href=\"\/supermicro\/\">Supermicro<\/a>, v\u1edbi t\u01b0 c\u00e1ch l\u00e0 \u0111\u1ed1i t\u00e1c c\u1ee7a Magnum IO OEM.\u00a0Huang c\u0169ng \u0111\u1ec1 c\u1eadp r\u1eb1ng h\u1ecd \u0111ang xem x\u00e9t c\u00e1c nh\u00e0 cung c\u1ea5p NIC kh\u00e1c ngo\u00e0i Mellanox, nh\u01b0ng kh\u00f4ng r\u00f5 anh ta c\u00f3 \u00fd \u0111\u1ecbnh g\u00ec.<\/p>\n<p>Thi\u1ebft l\u1eadp 8-NIC DGX-2 m\u00e0 anh ta s\u1eed d\u1ee5ng l\u00e0m v\u00ed d\u1ee5 cung c\u1ea5p m\u1ed9t b\u0103ng th\u00f4ng kh\u1ed5ng l\u1ed3 100 GB\/gi\u00e2y \u0111\u1ebfn v\u00e0 \u0111i t\u1eeb m\u00e1y ch\u1ee7.\u00a0B\u0103ng th\u00f4ng \u0111\u00f3 l\u00e0 th\u1ee9 t\u1ef1 c\u1ee7a b\u0103ng th\u00f4ng b\u1ed9 nh\u1edb tr\u00ean CPU, \u00f4ng Huang Huang ch\u1ec9 ra.<\/p>\n<p>Magnum IO c\u0169ng c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c x\u1ebfp l\u1edbp b\u00ean d\u01b0\u1edbi RAPIDS, ng\u0103n x\u1ebfp khoa h\u1ecdc d\u1eef li\u1ec7u \u0111\u01b0\u1ee3c t\u0103ng t\u1ed1c c\u1ee7a Nvidia m\u00e0\u00a0ch\u00fang t\u00f4i \u0111\u00e3 b\u00e1o c\u00e1o v\u00e0o n\u0103m ngo\u00e1i\u00a0.\u00a0Thu\u1eadn ti\u1ec7n, b\u1ed9 ph\u1ea7n m\u1ec1m n\u00e0y c\u0169ng h\u1ed7 tr\u1ee3 c\u1ea5u h\u00ecnh \u0111a GPU, \u0111a n\u00fat.\u00a0N\u00f3 \u0111\u00e3 \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng t\u1ea1i m\u1ed9t s\u1ed1 \u0111\u1ecba \u0111i\u1ec3m n\u1ed5i b\u1eadt c\u1ee7a HPC, bao g\u1ed3m Ph\u00f2ng th\u00ed nghi\u1ec7m qu\u1ed1c gia Oak Ridge, NASA, Trung t\u00e2m si\u00eau m\u00e1y t\u00ednh San Diego v\u00e0 Ph\u00f2ng th\u00ed nghi\u1ec7m Berkeley.<\/p>\n<p>\u00c1p d\u1ee5ng Magnum IO cho s\u1ed1 li\u1ec7u TPC-H, \u0111i\u1ec3m chu\u1ea9n h\u1ed7 tr\u1ee3 quy\u1ebft \u0111\u1ecbnh \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 \u0111o hi\u1ec7u su\u1ea5t truy v\u1ea5n tr\u00ean c\u01a1 s\u1edf d\u1eef li\u1ec7u 10TB, t\u0103ng t\u1ed1c \u0111\u1ed9 th\u1ef1c thi theo h\u1ec7 s\u1ed1 20. Tr\u00ean \u1ee9ng d\u1ee5ng sinh h\u1ecdc c\u1ea5u tr\u00fac trong th\u1ebf gi\u1edbi th\u1ef1c b\u1eb1ng g\u00f3i Visual Ph\u00e2n t\u00edch \u0111\u1ed9ng h\u1ecdc (VMD), Magnum IO t\u0103ng hi\u1ec7u n\u0103ng th\u1eddi gian ch\u1ea1y l\u00ean g\u1ea5p 3 l\u1ea7n.<\/p>\n<p>Magnum IO c\u0169ng \u0111\u00e3 \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 t\u0103ng t\u1ed1c tr\u1ef1c quan h\u00f3a Mars Lander, m\u00e0 Huang tuy\u00ean b\u1ed1 l\u00e0 tr\u1ef1c quan h\u00f3a kh\u1ed1i l\u01b0\u1ee3ng t\u01b0\u01a1ng t\u00e1c l\u1edbn nh\u1ea5t th\u1ebf gi\u1edbi. Trong thi\u1ebft l\u1eadp n\u00e0y, b\u1ed1n m\u00e1y ch\u1ee7 DGX-2, c\u00f3 kh\u1ea3 n\u0103ng truy\u1ec1n d\u1eef li\u1ec7u v\u1edbi t\u1ed1c \u0111\u1ed9 400 GB \/ gi\u00e2y, \u0111\u00e3 \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng \u0111\u1ec3 \u0103n 150 TB d\u1eef li\u1ec7u m\u00f4 ph\u1ecfng.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/thegioimaychu.vn\/blog\/wp-content\/uploads\/2019\/12\/tgmc-blog-5de686047e86d.png\" alt=\"\" \/><\/p>\n<p>V\u1edbi c\u00e1c lo\u1ea1i \u1ee9ng d\u1ee5ng \u0111\u01b0\u1ee3c ph\u1ed1i h\u1ee3p b\u1edfi Magnum IO, Nvidia \u0111\u00e3 ph\u00e1t tri\u1ec3n m\u1ed9t ki\u1ebfn \u200b\u200btr\u00fac tham chi\u1ebfu c\u00e1c lo\u1ea1i, m\u00e0 Huang g\u1ecdi l\u00e0 &#8220;supercomputing analytics Instrument&#8221;. \u1ede d\u1ea1ng ban \u0111\u1ea7u, n\u00f3 bao g\u1ed3m m\u1ed9t m\u00e1y ch\u1ee7 DGX-2 \u0111\u01b0\u1ee3c trang b\u1ecb t\u00e1m Mellanox 100Gbps NIC v\u00e0 30 TB \u1ed5 l\u01b0u tr\u1eef NVM-Express.\u00a0Nvidia d\u01b0\u1eddng nh\u01b0 kh\u00f4ng b\u00e1n m\u1ed9t h\u1ec7 th\u1ed1ng nh\u01b0 v\u1eady v\u00e0o th\u1eddi \u0111i\u1ec3m n\u00e0y (\u00edt nh\u1ea5t, kh\u00f4ng c\u00f3 gi\u00e1 n\u00e0o \u0111\u01b0\u1ee3c \u0111\u01b0a ra), m\u1eb7c d\u00f9 c\u00f3 l\u1ebd ng\u01b0\u1eddi ta c\u00f3 th\u1ec3 l\u1eafp r\u00e1p m\u1ed9t h\u1ec7 th\u1ed1ng nh\u01b0 v\u1eady m\u00e0 kh\u00f4ng g\u1eb7p qu\u00e1 nhi\u1ec1u kh\u00f3 kh\u0103n.\u00a0M\u1ed9t l\u1ea7n n\u1eefa, ch\u00fang t\u00f4i ngh\u0129 r\u1eb1ng gi\u1ea3 \u0111\u1ecbnh \u1edf \u0111\u00e2y l\u00e0 c\u00e1c \u0111\u1ed1i t\u00e1c OEM c\u1ee7a Nvidia s\u1ebd ti\u1ebfp nh\u1eadn \u0111i\u1ec1u n\u00e0y v\u00e0 cung c\u1ea5p c\u00e1c phi\u00ean b\u1ea3n c\u1ee7a ri\u00eang h\u1ecd.<\/p>\n<p>C\u00e1c h\u1ec7 th\u1ed1ng n\u00e0y s\u1ebd kh\u00f4ng h\u1ec1 r\u1ebb &#8211; m\u1ed9t m\u00e1y ch\u1ee7 DGX-2 ch\u01b0a \u0111\u01b0\u1ee3c b\u00e1n l\u1ebb c\u00f3 gi\u00e1 399.000 USD &#8211; nh\u01b0ng n\u1ebfu b\u1ea1n \u0111ang t\u00ecm ki\u1ebfm kh\u1ea3 n\u0103ng t\u0103ng t\u1ed1c GPU \u0111\u01b0\u1ee3c scale-out v\u00e0 b\u1ea1n c\u00f3 b\u1ed9 d\u1eef li\u1ec7u l\u1edbn \u0111\u1ec3 s\u1eed d\u1ee5ng, \u0111i\u1ec1u n\u00e0y c\u00f3 th\u1ec3 ph\u00f9 h\u1ee3p v\u1edbi c\u00e1i bill ph\u1ea3i tr\u1ea3.\u00a0Ch\u00fang t\u00f4i hy v\u1ecdng Nvidia s\u1ebd n\u00f3i nhi\u1ec1u h\u01a1n v\u1ec1 c\u00e1c c\u00f4ng ngh\u1ec7 n\u00e0y trong t\u01b0\u01a1ng lai.<\/p>\n<div>____<br \/><b>B\u00e0i vi\u1ebft li\u00ean quan<\/b><\/div>\n<ul>\n<li><a href=\"https:\/\/thegioimaychu.vn\/blog\/?post_type=post&#038;p=23415\">B\u01b0\u1edbc v\u00e0o k\u1ef7 nguy\u00ean tr\u1ee3 l\u00fd AI c\u00e1 nh\u00e2n &#8211; so s\u00e1nh chi ti\u1ebft NVIDIA RTX Spark v\u00e0 DGX Spark<\/a><\/li>\n<li><a href=\"https:\/\/thegioimaychu.vn\/blog\/?post_type=post&#038;p=23322\">NVIDIA RTX PRO 4500 Blackwell Server Edition: B\u01b0\u1edbc nh\u1ea3y v\u1ecdt t\u1eeb th\u1ebf h\u1ec7 L4 cho h\u1ea1 t\u1ea7ng AI suy lu\u1eadn hi\u1ec7u n\u0103ng cao<\/a><\/li>\n<li><a href=\"https:\/\/thegioimaychu.vn\/blog\/?post_type=post&#038;p=23119\">NVIDIA NGC\u2011ready Low\u2011latency Edge AI: Gi\u1ea3i ph\u00e1p cho Retail, Manufacturing v\u00e0 Smart Cities<\/a><\/li>\n<li><a href=\"https:\/\/thegioimaychu.vn\/blog\/?post_type=post&#038;p=23110\">So s\u00e1nh s\u1ee9c m\u1ea1nh c\u1ee7a m\u00e1y t\u00ednh AI DGX Spark v\u1edbi c\u00e1c card GPU m\u00e1y tr\u1ea1m chuy\u00ean nghi\u1ec7p c\u1ee7a NVIDIA<\/a><\/li>\n<li><a href=\"https:\/\/thegioimaychu.vn\/blog\/?post_type=post&#038;p=22969\">T\u1eeb Orin \u0111\u1ebfn Thor: B\u01b0\u1edbc nh\u1ea3y v\u1ecdt ki\u1ebfn tr\u00fac c\u1ee7a NVIDIA JetPack 7.0 v\u00e0 t\u01b0\u01a1ng lai c\u1ee7a robot h\u00ecnh ng\u01b0\u1eddi<\/a><\/li>\n<li><a href=\"https:\/\/thegioimaychu.vn\/blog\/?post_type=post&#038;p=22887\">T\u1ea1i sao g\u1ecdi NVIDIA DGX Spark l\u00e0 Si\u00eau m\u00e1y t\u00ednh AI c\u00e1 nh\u00e2n?<\/a><\/li>\n<\/ul>\n<\/p>","protected":false},"excerpt":{"rendered":"<p>Khi b&agrave;n 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&#273;&oacute;&#8230;<\/p>\n","protected":false},"author":2,"featured_media":3284,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[3,1],"tags":[12,1364,671,74,1314,120,262],"class_list":["post-3280","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-hpc","category-tong-hop","tag-deep-learning","tag-dgx","tag-gpu","tag-hpc","tag-nvidia","tag-nvlink","tag-rdma"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>T\u1eafng t\u1ed1c I\/O cho c\u00e1c c\u1ee5m m\u00e1y ch\u1ee7 \u0111a GPU - Blog | TheGioiMayChu<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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