{"id":25023,"date":"2026-11-04T10:42:02","date_gmt":"2026-11-04T03:42:02","guid":{"rendered":"https:\/\/vnso.vn\/?p=25023"},"modified":"2026-11-04T10:42:02","modified_gmt":"2026-11-04T03:42:02","slug":"thue-gpu-chay-ai-thi-nen-thue-gpu-nao","status":"publish","type":"post","link":"https:\/\/vnso.vn\/en\/thue-gpu-chay-ai-thi-nen-thue-gpu-nao\/","title":{"rendered":"Thu\u00ea GPU ch\u1ea1y AI th\u00ec n\u00ean thu\u00ea GPU n\u00e0o?"},"content":{"rendered":"

Thu\u00ea GPU ch\u1ea1y AI th\u00ec n\u00ean ch\u1ecdn RTX 3090, A4000, A5000 hay A100? Kh\u00f4ng ph\u1ea3i GPU c\u00f3 VRAM l\u1edbn nh\u1ea5t lu\u00f4n l\u00e0 l\u1ef1a ch\u1ecdn t\u1ed1t nh\u1ea5t \u0111\u1ec3 ch\u1ea1y AI. M\u1ed7i d\u00f2ng GPU c\u00f3 hi\u1ec7u n\u0103ng, dung l\u01b0\u1ee3ng b\u1ed9 nh\u1edb v\u00e0 m\u1ee9c chi ph\u00ed kh\u00e1c nhau. Ch\u1ecdn \u0111\u00fang c\u1ea5u h\u00ecnh gi\u00fap m\u00f4 h\u00ecnh v\u1eadn h\u00e0nh \u1ed5n \u0111\u1ecbnh v\u00e0 t\u1ed1i \u01b0u ng\u00e2n s\u00e1ch.<\/p>\n

V\u1eady c\u1ea7n bao nhi\u00eau VRAM \u0111\u1ec3 ch\u1ea1y LLM, AI Training ho\u1eb7c t\u1ea1o \u1ea3nh b\u1eb1ng AI? B\u00e0i vi\u1ebft d\u01b0\u1edbi \u0111\u00e2y s\u1ebd gi\u00fap b\u1ea1n x\u00e1c \u0111\u1ecbnh c\u1ea5u h\u00ecnh ph\u00f9 h\u1ee3p theo t\u1eebng nhu c\u1ea7u s\u1eed d\u1ee5ng.<\/p>\n

Thu\u00ea GPU ch\u1ea1y AI l\u00e0 g\u00ec?<\/h2>\n

Thu\u00ea GPU ch\u1ea1y AI l\u00e0 h\u00ecnh th\u1ee9c s\u1eed d\u1ee5ng s\u1ee9c m\u1ea1nh x\u1eed l\u00fd c\u1ee7a card \u0111\u1ed3 h\u1ecda (GPU) th\u00f4ng qua Internet, thay v\u00ec ph\u1ea3i mua v\u00e0 l\u1eafp \u0111\u1eb7t GPU tr\u1ef1c ti\u1ebfp tr\u00ean m\u00e1y t\u00ednh c\u00e1 nh\u00e2n ho\u1eb7c m\u00e1y ch\u1ee7 ri\u00eang. GPU \u0111\u01b0\u1ee3c \u0111\u1eb7t t\u1ea1i trung t\u00e2m d\u1eef li\u1ec7u c\u1ee7a nh\u00e0 cung c\u1ea5p, cho ph\u00e9p ng\u01b0\u1eddi d\u00f9ng k\u1ebft n\u1ed1i t\u1eeb xa \u0111\u1ec3 th\u1ef1c hi\u1ec7n c\u00e1c t\u00e1c v\u1ee5 nh\u01b0 hu\u1ea5n luy\u1ec7n m\u00f4 h\u00ecnh AI (Training), suy lu\u1eadn (Inference), ch\u1ea1y LLM ho\u1eb7c t\u1ea1o h\u00ecnh \u1ea3nh b\u1eb1ng AI.<\/p>\n

T\u00f9y nhu c\u1ea7u, ng\u01b0\u1eddi d\u00f9ng c\u00f3 th\u1ec3 thu\u00ea GPU theo gi\u1edd ho\u1eb7c theo th\u00e1ng, l\u1ef1a ch\u1ecdn c\u1ea5u h\u00ecnh ph\u00f9 h\u1ee3p v\u1edbi dung l\u01b0\u1ee3ng VRAM v\u00e0 hi\u1ec7u n\u0103ng c\u1ea7n thi\u1ebft. H\u00ecnh th\u1ee9c n\u00e0y gi\u00fap ti\u1ebfp c\u1eadn c\u00e1c d\u00f2ng GPU m\u1ea1nh nh\u01b0 RTX 3090, A5000 hay A100 m\u00e0 kh\u00f4ng c\u1ea7n \u0111\u1ea7u t\u01b0 chi ph\u00ed l\u1edbn \u0111\u1ec3 s\u1edf h\u1eefu ph\u1ea7n c\u1ee9ng.<\/p>\n

Nh\u1eefng y\u1ebfu t\u1ed1 c\u1ea7n bi\u1ebft tr\u01b0\u1edbc khi ch\u1ecdn GPU ch\u1ea1y AI<\/h2>\n

Tr\u01b0\u1edbc khi l\u1ef1a ch\u1ecdn GPU, ng\u01b0\u1eddi d\u00f9ng c\u1ea7n x\u00e1c \u0111\u1ecbnh m\u00f4 h\u00ecnh AI s\u1ebd tri\u1ec3n khai v\u00e0 kh\u1ed1i l\u01b0\u1ee3ng c\u00f4ng vi\u1ec7c d\u1ef1 ki\u1ebfn. M\u1ed9t c\u1ea5u h\u00ecnh ph\u00f9 h\u1ee3p c\u1ea7n \u0111\u00e1p \u1ee9ng c\u1ea3 y\u00eau c\u1ea7u v\u1ec1 b\u1ed9 nh\u1edb, hi\u1ec7u n\u0103ng t\u00ednh to\u00e1n v\u00e0 kh\u1ea3 n\u0103ng t\u01b0\u01a1ng th\u00edch ph\u1ea7n m\u1ec1m.<\/p>\n

VRAM \u2013 y\u1ebfu t\u1ed1 quan tr\u1ecdng khi ch\u1ea1y m\u00f4 h\u00ecnh AI<\/strong><\/h3>\n

VRAM l\u00e0 b\u1ed9 nh\u1edb ri\u00eang c\u1ee7a GPU, d\u00f9ng \u0111\u1ec3 l\u01b0u tr\u1eef tr\u1ecdng s\u1ed1 m\u00f4 h\u00ecnh, d\u1eef li\u1ec7u v\u00e0 c\u00e1c th\u00f4ng tin trung gian trong qu\u00e1 tr\u00ecnh t\u00ednh to\u00e1n. N\u1ebfu m\u00f4 h\u00ecnh c\u1ea7n nhi\u1ec1u b\u1ed9 nh\u1edb h\u01a1n dung l\u01b0\u1ee3ng VRAM kh\u1ea3 d\u1ee5ng, ch\u01b0\u01a1ng tr\u00ecnh c\u00f3 th\u1ec3 g\u1eb7p l\u1ed7i thi\u1ebfu b\u1ed9 nh\u1edb ho\u1eb7c ph\u1ea3i s\u1eed d\u1ee5ng ph\u01b0\u01a1ng \u00e1n ph\u00e2n b\u1ed5 t\u00e0i nguy\u00ean kh\u00e1c l\u00e0m gi\u1ea3m hi\u1ec7u n\u0103ng.<\/p>\n

Dung l\u01b0\u1ee3ng VRAM c\u1ea7n thi\u1ebft ph\u1ee5 thu\u1ed9c v\u00e0o k\u00edch th\u01b0\u1edbc m\u00f4 h\u00ecnh, \u0111\u1ed9 ch\u00ednh x\u00e1c s\u1ed1 h\u1ecdc, batch size v\u00e0 \u0111\u1ed9 d\u00e0i ng\u1eef c\u1ea3nh. V\u00ec v\u1eady, kh\u00f4ng n\u00ean ch\u1ec9 d\u1ef1a v\u00e0o t\u00ean GPU ho\u1eb7c s\u1ed1 nh\u00e2n CUDA \u0111\u1ec3 l\u1ef1a ch\u1ecdn.<\/p>\n

C\u00e1ch \u01b0\u1edbc t\u00ednh VRAM theo k\u00edch th\u01b0\u1edbc m\u00f4 h\u00ecnh<\/strong><\/h3>\n

V\u1edbi m\u00f4 h\u00ecnh c\u00f3 7 t\u1ef7 tham s\u1ed1 (7B), ri\u00eang ph\u1ea7n tr\u1ecdng s\u1ed1 c\u00f3 th\u1ec3 c\u1ea7n kho\u1ea3ng 14GB khi s\u1eed d\u1ee5ng FP16. N\u1ebfu l\u01b0\u1ee3ng t\u1eed h\u00f3a xu\u1ed1ng INT8, ph\u1ea7n tr\u1ecdng s\u1ed1 c\u00f3 th\u1ec3 c\u1ea7n kho\u1ea3ng 7GB; v\u1edbi INT4, con s\u1ed1 l\u00fd thuy\u1ebft c\u00f2n kho\u1ea3ng 3,5GB.<\/p>\n

\u0110\u00e2y ch\u1ec9 l\u00e0 m\u1ee9c \u01b0\u1edbc t\u00ednh cho tr\u1ecdng s\u1ed1, ch\u01b0a bao g\u1ed3m b\u1ed9 nh\u1edb d\u00e0nh cho KV Cache, d\u1eef li\u1ec7u \u0111\u1ea7u v\u00e0o, activation v\u00e0 c\u00e1c th\u00e0nh ph\u1ea7n kh\u00e1c. Khi Fine-tuning ho\u1eb7c Training, nhu c\u1ea7u b\u1ed9 nh\u1edb th\u01b0\u1eddng cao h\u01a1n \u0111\u00e1ng k\u1ec3 so v\u1edbi ch\u1ec9 ch\u1ea1y Inference.<\/p>\n\n\n\n\n\n\n\n
\u0110\u1ecbnh d\u1ea1ng<\/th>\nB\u1ed9 nh\u1edb l\u00fd thuy\u1ebft cho m\u1ed7i tham s\u1ed1<\/th>\nV\u00ed d\u1ee5 m\u00f4 h\u00ecnh 7B<\/th>\n<\/tr>\n
FP32<\/td>\n4 byte<\/td>\nKho\u1ea3ng 28GB<\/td>\n<\/tr>\n
FP16\/BF16<\/td>\n2 byte<\/td>\nKho\u1ea3ng 14GB<\/td>\n<\/tr>\n
INT8<\/td>\n1 byte<\/td>\nKho\u1ea3ng 7GB<\/td>\n<\/tr>\n
INT4<\/td>\n0,5 byte<\/td>\nKho\u1ea3ng 3,5GB<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

CUDA Cores v\u00e0 Tensor Cores<\/strong><\/h3>\n

CUDA Cores \u0111\u1ea3m nhi\u1ec7m nhi\u1ec1u ph\u00e9p t\u00ednh song song trong GPU. Tensor Cores \u0111\u01b0\u1ee3c t\u1ed1i \u01b0u cho c\u00e1c ph\u00e9p to\u00e1n ma tr\u1eadn th\u01b0\u1eddng xu\u1ea5t hi\u1ec7n trong AI v\u00e0 Deep Learning.<\/p>\n

Tuy nhi\u00ean, s\u1ed1 l\u01b0\u1ee3ng nh\u00e2n kh\u00f4ng quy\u1ebft \u0111\u1ecbnh to\u00e0n b\u1ed9 t\u1ed1c \u0111\u1ed9 x\u1eed l\u00fd. Ki\u1ebfn tr\u00fac GPU, b\u0103ng th\u00f4ng b\u1ed9 nh\u1edb, \u0111\u1ed9 ch\u00ednh x\u00e1c t\u00ednh to\u00e1n v\u00e0 kh\u1ea3 n\u0103ng t\u1ed1i \u01b0u c\u1ee7a ph\u1ea7n m\u1ec1m c\u0169ng \u1ea3nh h\u01b0\u1edfng \u0111\u1ebfn hi\u1ec7u n\u0103ng th\u1ef1c t\u1ebf.<\/p>\n

Ph\u00e2n bi\u1ec7t AI Training v\u00e0 AI Inference<\/strong><\/h3>\n

AI Training l\u00e0 qu\u00e1 tr\u00ecnh hu\u1ea5n luy\u1ec7n m\u00f4 h\u00ecnh t\u1eeb d\u1eef li\u1ec7u, th\u01b0\u1eddng c\u1ea7n nhi\u1ec1u t\u00e0i nguy\u00ean t\u00ednh to\u00e1n v\u00e0 b\u1ed9 nh\u1edb. Fine-tuning c\u0169ng c\u1ea7n GPU \u0111\u1ee7 m\u1ea1nh \u0111\u1ec3 c\u1eadp nh\u1eadt m\u1ed9t ph\u1ea7n ho\u1eb7c to\u00e0n b\u1ed9 tr\u1ecdng s\u1ed1 m\u00f4 h\u00ecnh.<\/p>\n

Trong khi \u0111\u00f3, AI Inference l\u00e0 qu\u00e1 tr\u00ecnh s\u1eed d\u1ee5ng m\u00f4 h\u00ecnh \u0111\u00e3 hu\u1ea5n luy\u1ec7n \u0111\u1ec3 t\u1ea1o k\u1ebft qu\u1ea3 d\u1ef1 \u0111o\u00e1n ho\u1eb7c ph\u1ea3n h\u1ed3i. Nhu c\u1ea7u VRAM th\u01b0\u1eddng th\u1ea5p h\u01a1n Training, nh\u01b0ng c\u00f3 th\u1ec3 t\u0103ng \u0111\u00e1ng k\u1ec3 khi x\u1eed l\u00fd nhi\u1ec1u y\u00eau c\u1ea7u \u0111\u1ed3ng th\u1eddi ho\u1eb7c s\u1eed d\u1ee5ng context d\u00e0i.<\/p>\n

>>> Xem th\u00eam H\u01b0\u1edbng d\u1eabn thu\u00ea GPU Train AI nhanh ch\u00f3ng, d\u1ec5 d\u00e0ng<\/a><\/strong><\/p>\n

\"N\u00ean<\/a><\/h2>\n

N\u00ean thu\u00ea GPU n\u00e0o cho t\u1eebng nhu c\u1ea7u ch\u1ea1y AI?<\/h2>\n

Kh\u00f4ng c\u00f3 m\u1ed9t GPU ph\u00f9 h\u1ee3p v\u1edbi t\u1ea5t c\u1ea3 d\u1ef1 \u00e1n. Ng\u01b0\u1eddi m\u1edbi c\u00f3 th\u1ec3 b\u1eaft \u0111\u1ea7u b\u1eb1ng c\u1ea5u h\u00ecnh t\u1ea7m trung \u0111\u1ec3 th\u1eed nghi\u1ec7m, sau \u0111\u00f3 n\u00e2ng c\u1ea5p khi m\u00f4 h\u00ecnh ho\u1eb7c kh\u1ed1i l\u01b0\u1ee3ng c\u00f4ng vi\u1ec7c t\u0103ng l\u00ean.<\/p>\n

NVIDIA RTX A4000 16GB \u2013 Ph\u00f9 h\u1ee3p th\u1eed nghi\u1ec7m v\u00e0 ph\u00e1t tri\u1ec3n AI<\/strong><\/h3>\n

RTX A4000 c\u00f3 16GB GDDR6 ECC, ki\u1ebfn tr\u00fac Ampere, 6.144 CUDA Cores v\u00e0 192 Tensor Cores th\u1ebf h\u1ec7 th\u1ee9 ba. \u0110\u00e2y l\u00e0 l\u1ef1a ch\u1ecdn ph\u00f9 h\u1ee3p cho l\u1eadp tr\u00ecnh vi\u00ean c\u1ea7n m\u00f4i tr\u01b0\u1eddng GPU \u0111\u1ec3 h\u1ecdc CUDA, ph\u00e1t tri\u1ec3n \u1ee9ng d\u1ee5ng AI, x\u1eed l\u00fd \u1ea3nh v\u00e0 th\u1eed nghi\u1ec7m m\u00f4 h\u00ecnh c\u00f3 quy m\u00f4 v\u1eeba ph\u1ea3i.<\/p>\n

V\u1edbi 16GB VRAM, GPU c\u00f3 th\u1ec3 ch\u1ea1y nhi\u1ec1u m\u00f4 h\u00ecnh nh\u1ecf, m\u1ed9t s\u1ed1 m\u00f4 h\u00ecnh LLM \u0111\u00e3 l\u01b0\u1ee3ng t\u1eed h\u00f3a v\u00e0 c\u00e1c t\u00e1c v\u1ee5 Inference ph\u00f9 h\u1ee3p. Tuy nhi\u00ean, dung l\u01b0\u1ee3ng n\u00e0y c\u00f3 th\u1ec3 h\u1ea1n ch\u1ebf khi ch\u1ea1y m\u00f4 h\u00ecnh l\u1edbn, context d\u00e0i ho\u1eb7c nhi\u1ec1u t\u00e1c v\u1ee5 c\u00f9ng l\u00fac.<\/p>\n

NVIDIA RTX 3090 24GB \u2013 Ph\u00f9 h\u1ee3p Deep Learning v\u00e0 Generative AI<\/strong><\/h3>\n

RTX 3090 s\u1edf h\u1eefu 24GB GDDR6X VRAM, cung c\u1ea5p kh\u00f4ng gian b\u1ed9 nh\u1edb l\u1edbn h\u01a1n so v\u1edbi nh\u00f3m GPU 16GB. Dung l\u01b0\u1ee3ng n\u00e0y h\u1eefu \u00edch khi th\u1eed nghi\u1ec7m Deep Learning, Computer Vision, t\u1ea1o \u1ea3nh b\u1eb1ng Stable Diffusion v\u00e0 ch\u1ea1y c\u00e1c m\u00f4 h\u00ecnh LLM \u0111\u00e3 t\u1ed1i \u01b0u b\u1ed9 nh\u1edb.<\/p>\n

RTX 3090 l\u00e0 l\u1ef1a ch\u1ecdn \u0111\u00e1ng c\u00e2n nh\u1eafc cho c\u00e1 nh\u00e2n, nh\u00e0 ph\u00e1t tri\u1ec3n v\u00e0 nh\u00f3m nghi\u00ean c\u1ee9u c\u1ea7n GPU c\u00f3 VRAM t\u01b0\u01a1ng \u0111\u1ed1i l\u1edbn. Khi tri\u1ec3n khai Training ho\u1eb7c Inference, hi\u1ec7u n\u0103ng v\u1eabn ph\u1ee5 thu\u1ed9c v\u00e0o phi\u00ean b\u1ea3n m\u00f4 h\u00ecnh, batch size v\u00e0 ph\u1ea7n m\u1ec1m s\u1eed d\u1ee5ng.<\/p>\n

NVIDIA RTX A5000 24GB \u2013 Ph\u00f9 h\u1ee3p ph\u00e1t tri\u1ec3n AI v\u00e0 workstation chuy\u00ean nghi\u1ec7p<\/strong><\/h3>\n

RTX A5000 c\u00f3 24GB GDDR6 ECC, 8.192 CUDA Cores v\u00e0 256 Tensor Cores th\u1ebf h\u1ec7 th\u1ee9 ba. GPU s\u1eed d\u1ee5ng ki\u1ebfn tr\u00fac Ampere, h\u01b0\u1edbng \u0111\u1ebfn c\u00e1c t\u00e1c v\u1ee5 \u0111\u1ed3 h\u1ecda chuy\u00ean nghi\u1ec7p, t\u00ednh to\u00e1n t\u0103ng t\u1ed1c v\u00e0 \u1ee9ng d\u1ee5ng AI.<\/p>\n

\u0110\u00e2y l\u00e0 l\u1ef1a ch\u1ecdn ph\u00f9 h\u1ee3p cho nh\u00f3m ph\u00e1t tri\u1ec3n c\u1ea7n m\u00f4i tr\u01b0\u1eddng GPU \u1ed5n \u0111\u1ecbnh \u0111\u1ec3 th\u1eed nghi\u1ec7m m\u00f4 h\u00ecnh, x\u1eed l\u00fd d\u1eef li\u1ec7u v\u00e0 ch\u1ea1y Inference. So v\u1edbi A4000, A5000 c\u00f3 th\u00eam VRAM v\u00e0 n\u0103ng l\u1ef1c t\u00ednh to\u00e1n cao h\u01a1n, ph\u00f9 h\u1ee3p v\u1edbi workload n\u1eb7ng h\u01a1n trong c\u00f9ng nh\u00f3m GPU workstation.<\/p>\n

NVIDIA A100 40GB\/80GB \u2013 Ph\u00f9 h\u1ee3p AI Training v\u00e0 m\u00f4 h\u00ecnh l\u1edbn<\/strong><\/h3>\n

A100 l\u00e0 GPU trung t\u00e2m d\u1eef li\u1ec7u thu\u1ed9c ki\u1ebfn tr\u00fac Ampere, \u0111\u01b0\u1ee3c thi\u1ebft k\u1ebf cho c\u00e1c workload t\u00ednh to\u00e1n hi\u1ec7u n\u0103ng cao. Phi\u00ean b\u1ea3n 40GB ho\u1eb7c 80GB HBM2e cung c\u1ea5p dung l\u01b0\u1ee3ng b\u1ed9 nh\u1edb l\u1edbn v\u00e0 b\u0103ng th\u00f4ng cao, ph\u00f9 h\u1ee3p v\u1edbi Training, Fine-tuning v\u00e0 Inference tr\u00ean c\u00e1c m\u00f4 h\u00ecnh c\u00f3 y\u00eau c\u1ea7u t\u00e0i nguy\u00ean l\u1edbn.<\/p>\n

A100 40GB c\u00f3 th\u1ec3 \u0111\u00e1p \u1ee9ng nhi\u1ec1u workload AI chuy\u00ean s\u00e2u m\u00e0 GPU 16GB ho\u1eb7c 24GB kh\u00f3 x\u1eed l\u00fd. Phi\u00ean b\u1ea3n 80GB ph\u00f9 h\u1ee3p h\u01a1n khi c\u1ea7n ch\u1ea1y m\u00f4 h\u00ecnh l\u1edbn, batch size cao ho\u1eb7c gi\u1ea3m nhu c\u1ea7u chia m\u00f4 h\u00ecnh qua nhi\u1ec1u GPU.<\/p>\n

NVIDIA H100 v\u00e0 H200 \u2013 D\u00e0nh cho AI quy m\u00f4 l\u1edbn<\/strong><\/h3>\n

H100 thu\u1ed9c ki\u1ebfn tr\u00fac Hopper, \u0111\u01b0\u1ee3c thi\u1ebft k\u1ebf cho AI Training v\u00e0 Inference hi\u1ec7u n\u0103ng cao. Phi\u00ean b\u1ea3n H100 SXM c\u00f3 80GB HBM3, trong khi H100 NVL c\u00f3 94GB b\u1ed9 nh\u1edb GPU theo th\u00f4ng s\u1ed1 NVIDIA.<\/p>\n

H200 ti\u1ebfp t\u1ee5c n\u00e2ng c\u1ea5p dung l\u01b0\u1ee3ng b\u1ed9 nh\u1edb l\u00ean 141GB HBM3e, c\u00f9ng b\u0103ng th\u00f4ng b\u1ed9 nh\u1edb cao. \u0110\u00e2y l\u00e0 nh\u00f3m GPU ph\u00f9 h\u1ee3p v\u1edbi doanh nghi\u1ec7p, trung t\u00e2m nghi\u00ean c\u1ee9u v\u00e0 h\u1ec7 th\u1ed1ng c\u1ea7n tri\u1ec3n khai LLM quy m\u00f4 l\u1edbn ho\u1eb7c x\u1eed l\u00fd nhi\u1ec1u workload AI chuy\u00ean s\u00e2u.<\/p>\n\n\n\n\n\n\n\n\n\n
GPU<\/th>\nVRAM tham kh\u1ea3o<\/th>\nNhu c\u1ea7u ph\u00f9 h\u1ee3p<\/th>\n<\/tr>\n
RTX A4000<\/td>\n16GB<\/td>\nH\u1ecdc AI, th\u1eed nghi\u1ec7m, Computer Vision<\/td>\n<\/tr>\n
RTX 3090<\/td>\n24GB<\/td>\nDeep Learning, Generative AI, LLM l\u01b0\u1ee3ng t\u1eed h\u00f3a<\/td>\n<\/tr>\n
RTX A5000<\/td>\n24GB<\/td>\nPh\u00e1t tri\u1ec3n AI, Inference, workstation<\/td>\n<\/tr>\n
A100<\/td>\n40GB \/ 80GB<\/td>\nTraining, Fine-tuning, LLM<\/td>\n<\/tr>\n
H100<\/td>\n80GB \/ 94GB<\/td>\nAI Training v\u00e0 Inference quy m\u00f4 l\u1edbn<\/td>\n<\/tr>\n
H200<\/td>\n141GB<\/td>\nLLM l\u1edbn, AI v\u00e0 HPC chuy\u00ean s\u00e2u<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Th\u00f4ng s\u1ed1 c\u00f3 th\u1ec3 kh\u00e1c nhau theo phi\u00ean b\u1ea3n PCIe, SXM ho\u1eb7c c\u1ea5u h\u00ecnh m\u00e1y ch\u1ee7. Kh\u1ea3 n\u0103ng ch\u1ea1y m\u00f4 h\u00ecnh th\u1ef1c t\u1ebf c\u00f2n ph\u1ee5 thu\u1ed9c v\u00e0o framework, ph\u01b0\u01a1ng ph\u00e1p l\u01b0\u1ee3ng t\u1eed h\u00f3a v\u00e0 b\u1ed9 nh\u1edb h\u1ec7 th\u1ed1ng.<\/p>\n

\"Ch\u1ecdn<\/a><\/p>\n

Ch\u1ecdn GPU theo m\u00f4 h\u00ecnh v\u00e0 \u1ee9ng d\u1ee5ng AI th\u1ef1c t\u1ebf<\/h2>\n

Ngo\u00e0i dung l\u01b0\u1ee3ng VRAM, ng\u01b0\u1eddi d\u00f9ng n\u00ean l\u1ef1a ch\u1ecdn GPU d\u1ef1a tr\u00ean lo\u1ea1i m\u00f4 h\u00ecnh v\u00e0 t\u00e1c v\u1ee5 c\u1ea7n tri\u1ec3n khai. C\u00f9ng m\u1ed9t GPU c\u00f3 th\u1ec3 ho\u1ea1t \u0111\u1ed9ng t\u1ed1t v\u1edbi Inference nh\u01b0ng kh\u00f4ng \u0111\u1ee7 t\u00e0i nguy\u00ean cho Training.<\/p>\n

Ch\u1ea1y LLM v\u00e0 chatbot AI<\/strong><\/h3>\n

C\u00e1c m\u00f4 h\u00ecnh ng\u00f4n ng\u1eef l\u1edbn nh\u01b0 Llama ho\u1eb7c nh\u1eefng m\u00f4 h\u00ecnh m\u00e3 ngu\u1ed3n m\u1edf kh\u00e1c c\u00f3 th\u1ec3 y\u00eau c\u1ea7u dung l\u01b0\u1ee3ng VRAM \u0111\u00e1ng k\u1ec3. M\u00f4 h\u00ecnh 7B \u1edf FP16 c\u1ea7n kho\u1ea3ng 14GB ch\u1ec9 cho ph\u1ea7n tr\u1ecdng s\u1ed1, ch\u01b0a t\u00ednh b\u1ed9 nh\u1edb ph\u00e1t sinh trong qu\u00e1 tr\u00ecnh v\u1eadn h\u00e0nh.<\/p>\n

V\u1edbi m\u00f4 h\u00ecnh nh\u1ecf \u0111\u00e3 l\u01b0\u1ee3ng t\u1eed h\u00f3a, GPU 16GB ho\u1eb7c 24GB c\u00f3 th\u1ec3 \u0111\u00e1p \u1ee9ng m\u1ed9t s\u1ed1 nhu c\u1ea7u th\u1eed nghi\u1ec7m v\u00e0 Inference. N\u1ebfu c\u1ea7n context d\u00e0i, nhi\u1ec1u ng\u01b0\u1eddi d\u00f9ng \u0111\u1ed3ng th\u1eddi ho\u1eb7c m\u00f4 h\u00ecnh c\u00f3 k\u00edch th\u01b0\u1edbc l\u1edbn h\u01a1n, n\u00ean c\u00e2n nh\u1eafc A100 40GB\/80GB ho\u1eb7c GPU c\u00f3 VRAM cao h\u01a1n.<\/p>\n

Ch\u1ea1y Stable Diffusion v\u00e0 Generative AI<\/strong><\/h3>\n

C\u00e1c c\u00f4ng c\u1ee5 t\u1ea1o \u1ea3nh b\u1eb1ng AI nh\u01b0 Stable Diffusion s\u1eed d\u1ee5ng GPU \u0111\u1ec3 x\u1eed l\u00fd qu\u00e1 tr\u00ecnh sinh \u1ea3nh. Dung l\u01b0\u1ee3ng VRAM c\u1ea7n thi\u1ebft ph\u1ee5 thu\u1ed9c v\u00e0o phi\u00ean b\u1ea3n m\u00f4 h\u00ecnh, \u0111\u1ed9 ph\u00e2n gi\u1ea3i, batch size v\u00e0 c\u00e1c ti\u1ec7n \u00edch m\u1edf r\u1ed9ng.<\/p>\n

RTX 3090 24GB ho\u1eb7c RTX A5000 24GB l\u00e0 nh\u1eefng l\u1ef1a ch\u1ecdn \u0111\u00e1ng c\u00e2n nh\u1eafc cho nhu c\u1ea7u t\u1ea1o \u1ea3nh v\u00e0 th\u1eed nghi\u1ec7m Generative AI. RTX A4000 16GB v\u1eabn c\u00f3 th\u1ec3 \u0111\u00e1p \u1ee9ng nhi\u1ec1u workflow ph\u00f9 h\u1ee3p, \u0111\u1eb7c bi\u1ec7t khi t\u1ed1i \u01b0u \u0111\u1ed9 ph\u00e2n gi\u1ea3i v\u00e0 thi\u1ebft l\u1eadp b\u1ed9 nh\u1edb.<\/p>\n

AI Training v\u00e0 Fine-tuning<\/strong><\/h3>\n

Training m\u00f4 h\u00ecnh t\u1eeb \u0111\u1ea7u th\u01b0\u1eddng c\u1ea7n t\u00e0i nguy\u00ean l\u1edbn h\u01a1n nhi\u1ec1u so v\u1edbi Inference. Ngo\u00e0i tr\u1ecdng s\u1ed1, GPU c\u00f2n ph\u1ea3i l\u01b0u gradient, activation v\u00e0 tr\u1ea1ng th\u00e1i optimizer trong qu\u00e1 tr\u00ecnh c\u1eadp nh\u1eadt m\u00f4 h\u00ecnh.<\/p>\n

V\u1edbi c\u00e1c b\u00e0i to\u00e1n nh\u1ecf ho\u1eb7c th\u1eed nghi\u1ec7m, RTX 3090 v\u00e0 A5000 c\u00f3 th\u1ec3 l\u00e0 l\u1ef1a ch\u1ecdn kh\u1edfi \u0111\u1ea7u. Khi c\u1ea7n Fine-tuning m\u00f4 h\u00ecnh l\u1edbn, t\u0103ng batch size ho\u1eb7c hu\u1ea5n luy\u1ec7n chuy\u00ean s\u00e2u, A100 40GB\/80GB s\u1ebd ph\u00f9 h\u1ee3p h\u01a1n.<\/p>\n

Computer Vision v\u00e0 x\u1eed l\u00fd video<\/strong><\/h3>\n

C\u00e1c \u1ee9ng d\u1ee5ng nh\u1eadn di\u1ec7n h\u00ecnh \u1ea3nh, ph\u00e2n lo\u1ea1i \u0111\u1ed1i t\u01b0\u1ee3ng, ph\u00e2n t\u00edch camera v\u00e0 x\u1eed l\u00fd video c\u00f3 nhu c\u1ea7u GPU kh\u00e1c nhau t\u00f9y theo \u0111\u1ed9 ph\u00e2n gi\u1ea3i v\u00e0 s\u1ed1 lu\u1ed3ng d\u1eef li\u1ec7u. Nh\u1eefng d\u1ef1 \u00e1n th\u1eed nghi\u1ec7m ho\u1eb7c tri\u1ec3n khai \u1edf quy m\u00f4 nh\u1ecf c\u00f3 th\u1ec3 b\u1eaft \u0111\u1ea7u v\u1edbi A4000 ho\u1eb7c RTX 3090.<\/p>\n

Khi c\u1ea7n x\u1eed l\u00fd nhi\u1ec1u lu\u1ed3ng video, m\u00f4 h\u00ecnh ph\u1ee9c t\u1ea1p ho\u1eb7c Training tr\u00ean t\u1eadp d\u1eef li\u1ec7u l\u1edbn, n\u00ean c\u00e2n nh\u1eafc GPU c\u00f3 VRAM v\u00e0 n\u0103ng l\u1ef1c t\u00ednh to\u00e1n cao h\u01a1n. Ngo\u00e0i GPU, CPU, RAM v\u00e0 t\u1ed1c \u0111\u1ed9 l\u01b0u tr\u1eef c\u0169ng \u1ea3nh h\u01b0\u1edfng \u0111\u1ebfn to\u00e0n b\u1ed9 pipeline x\u1eed l\u00fd.<\/p>\n

>>> Xem th\u00eam Thu\u00ea Cloud Gaming – C\u00e1ch ch\u01a1i Game c\u1ea5u h\u00ecnh cao, chi ph\u00ed th\u1ea5p<\/a><\/strong><\/p>\n

\"Thu\u00ea<\/a><\/h2>\n

Thu\u00ea GPU theo gi\u1edd hay theo th\u00e1ng? C\u00e1ch ch\u1ecdn c\u1ea5u h\u00ecnh t\u1ed1i \u01b0u<\/h2>\n

Sau khi x\u00e1c \u0111\u1ecbnh d\u00f2ng GPU, ng\u01b0\u1eddi d\u00f9ng c\u1ea7n l\u1ef1a ch\u1ecdn th\u1eddi gian thu\u00ea v\u00e0 t\u00e0i nguy\u00ean h\u1ec7 th\u1ed1ng \u0111i k\u00e8m. M\u1ed9t c\u1ea5u h\u00ecnh ph\u00f9 h\u1ee3p kh\u00f4ng ch\u1ec9 c\u1ea7n \u0111\u1ee7 VRAM m\u00e0 c\u00f2n ph\u1ea3i \u0111\u00e1p \u1ee9ng CPU, RAM, l\u01b0u tr\u1eef v\u00e0 th\u1eddi l\u01b0\u1ee3ng s\u1eed d\u1ee5ng.<\/p>\n

Khi n\u00e0o n\u00ean thu\u00ea GPU theo gi\u1edd?<\/strong><\/h3>\n

Thu\u00ea theo gi\u1edd ph\u00f9 h\u1ee3p v\u1edbi ng\u01b0\u1eddi m\u1edbi h\u1ecdc AI, l\u1eadp tr\u00ecnh vi\u00ean \u0111ang th\u1eed nghi\u1ec7m m\u00f4 h\u00ecnh ho\u1eb7c nh\u00f3m ph\u00e1t tri\u1ec3n c\u00f3 workload kh\u00f4ng li\u00ean t\u1ee5c. Ng\u01b0\u1eddi d\u00f9ng ch\u1ec9 c\u1ea7n tr\u1ea3 ph\u00ed theo th\u1eddi gian s\u1eed d\u1ee5ng, thay v\u00ec \u0111\u1ea7u t\u01b0 m\u00e1y ch\u1ee7 GPU ngay t\u1eeb \u0111\u1ea7u.<\/p>\n

V\u00ed d\u1ee5, n\u1ebfu ch\u1ec9 c\u1ea7n ch\u1ea1y th\u1eed m\u1ed9t m\u00f4 h\u00ecnh, ki\u1ec3m tra th\u01b0 vi\u1ec7n ho\u1eb7c th\u1ef1c hi\u1ec7n m\u1ed9t t\u00e1c v\u1ee5 Inference trong v\u00e0i gi\u1edd, thu\u00ea theo gi\u1edd gi\u00fap ki\u1ec3m so\u00e1t ng\u00e2n s\u00e1ch t\u1ed1t h\u01a1n. \u0110\u00e2y c\u0169ng l\u00e0 c\u00e1ch \u0111\u00e1nh gi\u00e1 hi\u1ec7u n\u0103ng th\u1ef1c t\u1ebf tr\u01b0\u1edbc khi ch\u1ecdn c\u1ea5u h\u00ecnh d\u00e0i h\u1ea1n.<\/p>\n

Khi n\u00e0o n\u00ean thu\u00ea GPU theo th\u00e1ng?<\/strong><\/h3>\n

Thu\u00ea theo th\u00e1ng ph\u00f9 h\u1ee3p v\u1edbi d\u1ef1 \u00e1n s\u1eed d\u1ee5ng GPU th\u01b0\u1eddng xuy\u00ean, m\u00f4i tr\u01b0\u1eddng ph\u00e1t tri\u1ec3n c\u1ea7n duy tr\u00ec li\u00ean t\u1ee5c ho\u1eb7c workload ch\u1ea1y trong th\u1eddi gian d\u00e0i. Khi th\u1eddi l\u01b0\u1ee3ng s\u1eed d\u1ee5ng cao, h\u00ecnh th\u1ee9c n\u00e0y c\u00f3 th\u1ec3 gi\u00fap t\u1ed1i \u01b0u chi ph\u00ed so v\u1edbi thu\u00ea theo gi\u1edd.<\/p>\n

Ng\u01b0\u1eddi d\u00f9ng n\u00ean so s\u00e1nh t\u1ed5ng chi ph\u00ed d\u1ef1 ki\u1ebfn theo s\u1ed1 gi\u1edd s\u1eed d\u1ee5ng th\u1ef1c t\u1ebf. C\u1ea7n t\u00ednh c\u1ea3 th\u1eddi gian m\u00e1y ch\u1ea1y th\u1eed nghi\u1ec7m, Training, Inference v\u00e0 th\u1eddi gian duy tr\u00ec m\u00f4i tr\u01b0\u1eddng.<\/p>\n

C\u1ea5u h\u00ecnh n\u00e0o ph\u00f9 h\u1ee3p v\u1edbi t\u1eebng ng\u00e2n s\u00e1ch?<\/p>\n

C\u00f3 th\u1ec3 tham kh\u1ea3o c\u00e1ch ph\u00e2n nh\u00f3m d\u01b0\u1edbi \u0111\u00e2y \u0111\u1ec3 x\u00e1c \u0111\u1ecbnh \u0111i\u1ec3m b\u1eaft \u0111\u1ea7u:<\/p>\n\n\n\n\n\n\n\n\n
Nhu c\u1ea7u<\/th>\nGPU n\u00ean c\u00e2n nh\u1eafc<\/th>\n\u0110\u1ecbnh h\u01b0\u1edbng l\u1ef1a ch\u1ecdn<\/th>\n<\/tr>\n
H\u1ecdc AI, l\u1eadp tr\u00ecnh CUDA<\/td>\nRTX A4000 16GB<\/td>\nTh\u1eed nghi\u1ec7m v\u00e0 ph\u00e1t tri\u1ec3n c\u01a1 b\u1ea3n<\/td>\n<\/tr>\n
Ch\u1ea1y m\u00f4 h\u00ecnh AI, t\u1ea1o \u1ea3nh<\/td>\nRTX 3090 24GB<\/td>\nC\u1ea7n VRAM l\u1edbn h\u01a1n cho Generative AI<\/td>\n<\/tr>\n
Ph\u00e1t tri\u1ec3n AI chuy\u00ean s\u00e2u<\/td>\nRTX A5000 24GB<\/td>\nWorkstation v\u00e0 Inference<\/td>\n<\/tr>\n
Fine-tuning, LLM l\u1edbn<\/td>\nA100 40GB\/80GB<\/td>\n\u01afu ti\u00ean VRAM v\u00e0 b\u0103ng th\u00f4ng<\/td>\n<\/tr>\n
AI Training quy m\u00f4 l\u1edbn<\/td>\nH100\/H200<\/td>\nWorkload chuy\u00ean s\u00e2u, y\u00eau c\u1ea7u hi\u1ec7u n\u0103ng cao<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Nh\u1eefng th\u00f4ng s\u1ed1 c\u1ea7n ki\u1ec3m tra tr\u01b0\u1edbc khi thu\u00ea GPU ch\u1ea1y AI<\/strong><\/h3>\n