{"id":23341,"date":"2025-10-08T17:15:04","date_gmt":"2025-10-08T10:15:04","guid":{"rendered":"https:\/\/vnso.vn\/?p=23341"},"modified":"2025-10-09T08:27:44","modified_gmt":"2025-10-09T01:27:44","slug":"sai-lam-thuong-gap-khi-thue-gpu-gia-re-can-luu-y","status":"publish","type":"post","link":"https:\/\/vnso.vn\/en\/sai-lam-thuong-gap-khi-thue-gpu-gia-re-can-luu-y\/","title":{"rendered":"Sai l\u1ea7m th\u01b0\u1eddng g\u1eb7p khi thu\u00ea GPU gi\u00e1 r\u1ebb c\u1ea7n l\u01b0u \u00fd"},"content":{"rendered":"

Khi AI, Deep Learning hay d\u1ef1ng h\u00ecnh 3D ng\u00e0y c\u00e0ng ph\u1ed5 bi\u1ebfn, nhu c\u1ea7u thu\u00ea GPU gi\u00e1 r\u1ebb b\u00f9ng n\u1ed5. Kh\u00f4ng ph\u1ea3i ai c\u0169ng c\u00f3 \u0111i\u1ec1u ki\u1ec7n \u0111\u1ea7u t\u01b0 d\u00e0n m\u00e1y h\u00e0ng tr\u0103m tri\u1ec7u, n\u00ean thu\u00ea GPU \u2014 \u0111\u1eb7c bi\u1ec7t l\u00e0 c\u00e1c g\u00f3i gi\u00e1 r\u1ebb \u2014 tr\u1edf th\u00e0nh l\u1ef1a ch\u1ecdn h\u1ea5p d\u1eabn, chi ph\u00ed th\u1ea5p, tri\u1ec3n khai nhanh, kh\u00f4ng c\u1ea7n \u0111\u1ea7u t\u01b0 ph\u1ea7n c\u1ee9ng.<\/p>\n

Nh\u01b0ng \u0111\u1eb1ng sau m\u1ee9c gi\u00e1 r\u1ebb \u0111\u00f3, r\u1ea5t nhi\u1ec1u ng\u01b0\u1eddi \u0111\u00e3 r\u01a1i v\u00e0o c\u1ea3nh ti\u1ec1n m\u1ea5t, hi\u1ec7u n\u0103ng gi\u1ea3m n\u1eeda ch\u1ec9 v\u00ec ch\u1ecdn sai c\u1ea5u h\u00ecnh ho\u1eb7c kh\u00f4ng hi\u1ec3u r\u00f5 c\u00e1ch ho\u1ea1t \u0111\u1ed9ng c\u1ee7a GPU cloud. B\u00e0i vi\u1ebft n\u00e0y chia s\u1ebb l\u1ea1i nh\u1eefng sai l\u1ea7m ph\u1ed5 bi\u1ebfn nh\u1ea5t khi thu\u00ea GPU gi\u00e1 r\u1ebb c\u00f9ng c\u00e1ch nh\u1eadn bi\u1ebft v\u00e0 tr\u00e1nh l\u1eb7p l\u1ea1i.<\/p>\n

D\u1ecbch v\u1ee5 thu\u00ea GPU l\u00e0 g\u00ec?<\/h2>\n

D\u1ecbch v\u1ee5 thu\u00ea GPU (Cloud GPU, Server GPU) l\u00e0 gi\u1ea3i ph\u00e1p cho ph\u00e9p ng\u01b0\u1eddi d\u00f9ng s\u1eed d\u1ee5ng s\u1ee9c m\u1ea1nh x\u1eed l\u00fd c\u1ee7a card \u0111\u1ed3 h\u1ecda (GPU) t\u1eeb h\u1ec7 th\u1ed1ng m\u00e1y ch\u1ee7 c\u1ee7a nh\u00e0 cung c\u1ea5p m\u00e0 kh\u00f4ng c\u1ea7n \u0111\u1ea7u t\u01b0 mua ph\u1ea7n c\u1ee9ng. Thay v\u00ec ph\u1ea3i trang b\u1ecb m\u00e1y tr\u1ea1m ho\u1eb7c server \u0111\u1eaft ti\u1ec1n, b\u1ea1n c\u00f3 th\u1ec3 thu\u00ea GPU theo gi\u1edd, theo ng\u00e0y ho\u1eb7c theo th\u00e1ng \u0111\u1ec3 hu\u1ea5n luy\u1ec7n m\u00f4 h\u00ecnh AI, render \u0111\u1ed3 h\u1ecda, d\u1ef1ng phim hay ch\u1ea1y \u1ee9ng d\u1ee5ng h\u1ecdc m\u00e1y.<\/p>\n

D\u1ecbch v\u1ee5 n\u00e0y gi\u00fap ti\u1ebft ki\u1ec7m chi ph\u00ed, d\u1ec5 m\u1edf r\u1ed9ng v\u00e0 \u0111\u1ea3m b\u1ea3o hi\u1ec7u su\u1ea5t cao nh\u1edd h\u1ea1 t\u1ea7ng \u0111\u01b0\u1ee3c t\u1ed1i \u01b0u s\u1eb5n, ph\u00f9 h\u1ee3p cho c\u1ea3 c\u00e1 nh\u00e2n, l\u1eadp tr\u00ecnh vi\u00ean v\u00e0 doanh nghi\u1ec7p mu\u1ed1n t\u1eadn d\u1ee5ng s\u1ee9c m\u1ea1nh GPU m\u1ed9t c\u00e1ch linh ho\u1ea1t v\u00e0 hi\u1ec7u qu\u1ea3.<\/p>\n

>>> C\u00f3 th\u1ec3 b\u1ea1n quan t\u00e2m D\u1ecbch v\u1ee5 thu\u00ea Server GPU, Cloud GPU ch\u1ec9 t\u1eeb 30k\/gi\u1edd<\/a><\/strong><\/p>\n

Nh\u1eefng sai l\u1ea7m th\u01b0\u1eddng g\u1eb7p khi s\u1eed d\u1ee5ng d\u1ecbch v\u1ee5 thu\u00ea GPU gi\u00e1 r\u1ebb<\/h2>\n

Ch\u1ecdn GPU kh\u00f4ng ph\u00f9 h\u1ee3p v\u1edbi m\u1ee5c \u0111\u00edch s\u1eed d\u1ee5ng<\/h3>\n

Sai l\u1ea7m \u0111\u1ea7u ti\u00ean v\u00e0 c\u0169ng ph\u1ed5 bi\u1ebfn nh\u1ea5t l\u00e0 ch\u1ecdn GPU theo qu\u1ea3ng c\u00e1o, kh\u00f4ng theo nhu c\u1ea7u th\u1ef1c t\u1ebf.<\/p>\n

Nhi\u1ec1u ng\u01b0\u1eddi th\u1ea5y card x\u1ecbn nh\u01b0 RTX 4090, A100 hay H100 \u0111\u01b0\u1ee3c nh\u1eafc \u0111\u1ebfn, li\u1ec1n ch\u1ecdn ngay v\u00ec tin r\u1eb1ng m\u1ea1nh h\u01a1n l\u00e0 t\u1ed1t h\u01a1n. Nh\u01b0ng n\u1ebfu b\u1ea1n ch\u1ec9 ch\u1ea1y inference, render nh\u1eb9 ho\u1eb7c h\u1ecdc th\u1eed m\u00f4 h\u00ecnh nh\u1ecf, s\u1ee9c m\u1ea1nh \u0111\u00f3 l\u00e0 th\u1eeba th\u00e3i, \u0111\u1ed3ng ngh\u0129a b\u1ea1n \u0111ang tr\u1ea3 ti\u1ec1n cho hi\u1ec7u n\u0103ng kh\u00f4ng bao gi\u1edd d\u00f9ng t\u1edbi.<\/p>\n

V\u00ed d\u1ee5, n\u1ebfu b\u1ea1n ch\u1ec9 ch\u1ea1y Stable Diffusion mini m\u00e0 l\u1ea1i thu\u00ea H100, chi ph\u00ed g\u1ea5p 5 l\u1ea7n nh\u01b0ng t\u1ed1c \u0111\u1ed9 g\u1ea7n nh\u01b0 kh\u00f4ng c\u1ea3i thi\u1ec7n.<\/p>\n

Ng\u01b0\u1ee3c l\u1ea1i, v\u1edbi c\u00e1c m\u00f4 h\u00ecnh l\u1edbn nh\u01b0 LLaMA hay Stable Diffusion XL, n\u1ebfu thu\u00ea GPU gi\u00e1 r\u1ebb c\u00f3 VRAM th\u1ea5p (v\u00ed d\u1ee5 8\u201312GB), b\u1ea1n s\u1ebd g\u1eb7p l\u1ed7i tr\u00e0n b\u1ed9 nh\u1edb, gi\u1ea3m batch size, t\u1ed1c \u0111\u1ed9 train ch\u1eadm h\u01a1n g\u1ea5p nhi\u1ec1u l\u1ea7n. M\u1ed9t GPU 8GB VRAM c\u00f3 th\u1ec3 \u0111\u1ee7 cho inference, nh\u01b0ng kh\u00f4ng th\u1ec3 train m\u00f4 h\u00ecnh LLaMA 13B, d\u1eabn \u0111\u1ebfn l\u1ed7i tr\u00e0n b\u1ed9 nh\u1edb.<\/p>\n

Lu\u00f4n x\u00e1c \u0111\u1ecbnh tr\u01b0\u1edbc workload \u2014 training hay inference, model l\u1edbn hay nh\u1ecf \u2014 r\u1ed3i m\u1edbi ch\u1ecdn GPU ph\u00f9 h\u1ee3p. \u0110\u1eebng \u0111\u1ec3 gi\u00e1 r\u1ebb \u0111\u00e1nh l\u1eeba b\u1ea1n khi GPU kh\u00f4ng \u0111\u1ee7 s\u1ee9c cho c\u00f4ng vi\u1ec7c.<\/p>\n

\"Llama<\/a>

M\u00f4 h\u00ecnh ng\u00f4n ng\u1eef Llama n\u1ed5i ti\u1ebfng<\/p><\/div>\n

B\u1ecf qua dung l\u01b0\u1ee3ng VRAM v\u00e0 b\u0103ng th\u00f4ng b\u1ed9 nh\u1edb<\/h3>\n

VRAM l\u00e0 b\u00ecnh ch\u1ee9a d\u1eef li\u1ec7u cho GPU. Dung l\u01b0\u1ee3ng VRAM th\u1ea5p \u0111\u1ed3ng ngh\u0129a b\u1ea1n b\u1ecb gi\u1edbi h\u1ea1n k\u00edch th\u01b0\u1edbc m\u00f4 h\u00ecnh ho\u1eb7c batch size khi train. Nhi\u1ec1u ng\u01b0\u1eddi thu\u00ea GPU gi\u00e1 r\u1ebb ch\u1ec9 nh\u00ecn s\u1ed1 nh\u00e2n CUDA m\u00e0 qu\u00ean m\u1ea5t VRAM v\u00e0 k\u1ebft qu\u1ea3 l\u00e0 m\u00f4 h\u00ecnh ch\u1ea1y ch\u1eadm, th\u1eadm ch\u00ed kh\u00f4ng th\u1ec3 kh\u1edfi \u0111\u1ed9ng.<\/p>\n

T\u1ed1c \u0111\u1ed9 b\u0103ng th\u00f4ng b\u1ed9 nh\u1edb (memory bandwidth) c\u0169ng \u1ea3nh h\u01b0\u1edfng tr\u1ef1c ti\u1ebfp \u0111\u1ebfn hi\u1ec7u n\u0103ng. Hai GPU c\u00f3 c\u00f9ng s\u1ed1 nh\u00e2n nh\u01b0ng kh\u00e1c chu\u1ea9n b\u1ed9 nh\u1edb (GDDR5 vs HBM2e) s\u1ebd cho ra t\u1ed1c \u0111\u1ed9 x\u1eed l\u00fd r\u1ea5t kh\u00e1c nhau.<\/p>\n

B\u1ea1n n\u00ean lu\u00f4n xem k\u1ef9 VRAM (t\u1ed1i thi\u1ec3u 16GB cho model t\u1ea7m trung) v\u00e0 chu\u1ea9n b\u1ed9 nh\u1edb. N\u1ebfu c\u00f3 th\u1ec3, h\u00e3y th\u1eed benchmark nh\u1ecf tr\u01b0\u1edbc khi thu\u00ea d\u00e0i h\u1ea1n.<\/p>\n

>>> Xem th\u00eam: GPU Train Model cho m\u1ecdi m\u00f4 h\u00ecnh AI \u0111\u01a1n gi\u1ea3n, ti\u1ebft ki\u1ec7m<\/a><\/strong><\/p>\n

Kh\u00f4ng ki\u1ec3m tra GPU c\u00f3 \u0111\u1ed9c quy\u1ec1n hay kh\u00f4ng<\/h3>\n

M\u1ed9t s\u1ed1 nh\u00e0 cung c\u1ea5p cho thu\u00ea GPU gi\u00e1 r\u1ebb b\u1eb1ng c\u00e1ch chia s\u1ebb GPU v\u1eadt l\u00fd<\/em> gi\u1eefa nhi\u1ec1u ng\u01b0\u1eddi d\u00f9ng (time-slicing). \u0110i\u1ec1u n\u00e0y khi\u1ebfn hi\u1ec7u n\u0103ng thay \u0111\u1ed5i li\u00ean t\u1ee5c, \u0111\u1eb7c bi\u1ec7t khi c\u00f3 h\u00e0ng x\u00f3m \u1ed3n \u00e0o \u0111ang d\u00f9ng c\u00f9ng GPU.<\/p>\n

Hi\u1ec7n t\u01b0\u1ee3ng n\u00e0y \u0111\u01b0\u1ee3c g\u1ecdi l\u00e0 noisy neighbor<\/strong> \u2014 b\u1ea1n ch\u1ea1y ch\u1eadm h\u01a1n d\u00f9 c\u00f9ng c\u1ea5u h\u00ecnh, ch\u1ec9 v\u00ec t\u00e0i nguy\u00ean b\u1ecb chia s\u1ebb ng\u1ea7m.<\/p>\n

N\u00ean h\u1ecfi r\u00f5 nh\u00e0 cung c\u1ea5p xem GPU c\u00f3 \u0111\u01b0\u1ee3c \u201cdedicated\u201d (\u0111\u1ed9c quy\u1ec1n) kh\u00f4ng. N\u1ebfu h\u1ecd kh\u00f4ng th\u1ec3 x\u00e1c nh\u1eadn, hi\u1ec7u n\u0103ng c\u00f3 th\u1ec3 kh\u00f4ng \u1ed5n \u0111\u1ecbnh nh\u01b0 qu\u1ea3ng c\u00e1o.<\/p>\n

Kh\u00f4ng \u0111\u1ecdc k\u1ef9 c\u1ea5u tr\u00fac gi\u00e1 v\u00e0 chi ph\u00ed \u1ea9n<\/h3>\n

M\u1ed9t l\u1ed7i kh\u00e1c \u0111\u1ebfn t\u1eeb billing \u2013 c\u00e1ch t\u00ednh ti\u1ec1n. M\u1ed9t s\u1ed1 \u0111\u01a1n v\u1ecb b\u00e1o gi\u00e1 r\u1ea5t r\u1ebb theo gi\u1edd, nh\u01b0ng l\u1ea1i t\u00ednh th\u00eam ph\u00ed l\u01b0u tr\u1eef, ph\u00ed m\u1ea1ng (egress), ho\u1eb7c ph\u1ee5 ph\u00ed khi d\u1eebng r\u1ed3i kh\u1edfi \u0111\u1ed9ng l\u1ea1i m\u00e1y \u1ea3o.<\/p>\n

K\u1ebft qu\u1ea3: t\u1ed5ng chi ph\u00ed cu\u1ed1i c\u00f9ng cao h\u01a1n g\u00f3i \u0111\u1eaft m\u00e0 hi\u1ec7u n\u0103ng v\u1eabn kh\u00f4ng \u1ed5n \u0111\u1ecbnh.<\/p>\n

Tr\u01b0\u1edbc khi thu\u00ea, h\u00e3y h\u1ecfi r\u00f5:<\/p>\n