{"id":24546,"date":"2026-05-08T10:12:46","date_gmt":"2026-05-08T03:12:46","guid":{"rendered":"https:\/\/vnso.vn\/?p=24546"},"modified":"2026-05-12T16:47:54","modified_gmt":"2026-05-12T09:47:54","slug":"nvidia-a100-bao-gia-thue-gpu-a100-40gb-80gb-theo-gio","status":"publish","type":"post","link":"https:\/\/vnso.vn\/en\/nvidia-a100-bao-gia-thue-gpu-a100-40gb-80gb-theo-gio\/","title":{"rendered":"Nvidia A100 \u2013 B\u00e1o Gi\u00e1 Thu\u00ea GPU A100 40GB & 80GB Theo Gi\u1edd"},"content":{"rendered":"

Nvidia A100<\/strong> l\u00e0 d\u00f2ng GPU datacenter cao c\u1ea5p \u0111\u01b0\u1ee3c t\u1ed1i \u01b0u cho AI, machine learning, deep learning v\u00e0 HPC. D\u00f9 \u0111\u00e3 c\u00f3 H100 hay H200 ra m\u1eaft, A100 v\u1eabn l\u00e0 GPU \u0111\u01b0\u1ee3c thu\u00ea nhi\u1ec1u nh\u1ea5t hi\u1ec7n nay nh\u1edd hi\u1ec7u n\u0103ng m\u1ea1nh, VRAM l\u1edbn v\u00e0 chi ph\u00ed t\u1ed1i \u01b0u h\u01a1n \u0111\u00e1ng k\u1ec3.<\/span><\/p>\n

Hi\u1ec7n nay, nhu c\u1ea7u thu\u00ea Nvidia A100 Cloud GPU theo gi\u1edd t\u1ea1i Vi\u1ec7t Nam \u0111ang t\u0103ng r\u1ea5t nhanh, \u0111\u1eb7c bi\u1ec7t trong c\u00e1c l\u0129nh v\u1ef1c nh\u01b0 chatbot AI, AI Agent, Stable Diffusion, OCR, LLM v\u00e0 h\u1ea1 t\u1ea7ng AI SaaS. Thay v\u00ec \u0111\u1ea7u t\u01b0 server GPU h\u00e0ng tr\u0103m tri\u1ec7u \u0111\u1ed3ng, nhi\u1ec1u doanh nghi\u1ec7p l\u1ef1a ch\u1ecdn thu\u00ea GPU A100 theo gi\u1edd \u0111\u1ec3 t\u1ed1i \u01b0u chi ph\u00ed v\u00e0 tri\u1ec3n khai nhanh h\u01a1n.<\/span><\/p>\n

V\u1eady Nvidia A100 m\u1ea1nh nh\u01b0 th\u1ebf n\u00e0o? N\u00ean ch\u1ecdn A100 40GB hay 80GB?<\/strong> Gi\u00e1 thu\u00ea cloud GPU A100 theo gi\u1edd, ng\u00e0y, tu\u1ea7n hi\u1ec7n nay bao nhi\u00eau? B\u00e0i vi\u1ebft d\u01b0\u1edbi \u0111\u00e2y s\u1ebd ph\u00e2n t\u00edch chi ti\u1ebft.<\/span><\/p>\n

C\u00e1c \u0111\u1eb7c \u0111i\u1ec3m k\u1ef9 thu\u1eadt v\u00e0 \u1ee9ng d\u1ee5ng n\u1ed5i b\u1eadt:<\/strong><\/span><\/h2>\n

Hi\u1ec7u su\u1ea5t AI:<\/strong> H\u1ed7 tr\u1ee3 hu\u1ea5n luy\u1ec7n m\u00f4 h\u00ecnh ng\u00f4n ng\u1eef l\u1edbn (LLM), BERT-Large v\u00e0 c\u00e1c t\u00e1c v\u1ee5 nh\u1eadn d\u1ea1ng h\u00ecnh \u1ea3nh v\u1edbi t\u1ed1c \u0111\u1ed9 v\u01b0\u1ee3t tr\u1ed9i. <\/span>C\u00f4ng ngh\u1ec7 MIG (Multi-Instance GPU): Cho ph\u00e9p chia nh\u1ecf A100 th\u00e0nh t\u1ed1i \u0111a 7 GPU \u1ea3o, t\u1ed1i \u01b0u h\u00f3a t\u00e0i nguy\u00ean cho nhi\u1ec1u ng\u01b0\u1eddi d\u00f9ng.<\/span><\/p>\n

K\u1ebft n\u1ed1i nhanh:<\/strong> T\u00edch h\u1ee3p giao di\u1ec7n SXM4 ho\u1eb7c PCIe 4.0, c\u00f9ng c\u00f4ng ngh\u1ec7 NVLink, gi\u00fap t\u0103ng t\u1ed1c \u0111\u1ed9 truy\u1ec1n t\u1ea3i d\u1eef li\u1ec7u.<\/span><\/p>\n

\u1ee8ng d\u1ee5ng:<\/strong> Tr\u1ecdng t\u00e2m cho m\u00e1y ch\u1ee7 AI (DGX A100) v\u00e0 c\u00e1c h\u1ec7 th\u1ed1ng m\u00e1y tr\u1ea1m (DGX Station A100) ph\u1ee5c v\u1ee5 nghi\u00ean c\u1ee9u khoa h\u1ecdc v\u00e0 c\u00f4ng nghi\u1ec7p.<\/span><\/p>\n

So s\u00e1nh nhanh:<\/strong><\/span><\/p>\n

A100 40GB\/80GB:<\/strong> L\u1ef1a ch\u1ecdn t\u1ed1i \u01b0u v\u1ec1 chi ph\u00ed cho nhu c\u1ea7u t\u00ednh to\u00e1n AI ti\u00eau chu\u1ea9n.<\/span><\/p>\n

A100 so v\u1edbi H100:<\/strong> H100 cung c\u1ea5p hi\u1ec7u su\u1ea5t cao h\u01a1n \u0111\u1eb7c bi\u1ec7t cho Generative AI, trong khi A100 v\u1eabn l\u00e0 gi\u1ea3i ph\u00e1p c\u00e2n b\u1eb1ng gi\u1eefa hi\u1ec7u su\u1ea5t v\u00e0 chi ph\u00ed.<\/span><\/p>\n

NVIDIA A100 hi\u1ec7n \u0111\u01b0\u1ee3c ph\u00e2n ph\u1ed1i ch\u00ednh h\u00e3ng th\u00f4ng qua c\u00e1c \u0111\u1ed1i t\u00e1c OEM, cam k\u1ebft cung c\u1ea5p gi\u1ea3i ph\u00e1p \u0111i\u1ec7n to\u00e1n AI t\u1ed1i \u01b0u.<\/span><\/p>\n

V\u00ec sao Nvidia A100 v\u1eabn \u0111\u01b0\u1ee3c thu\u00ea r\u1ea5t nhi\u1ec1u n\u0103m 2026?<\/span><\/h2>\n

\"\"<\/p>\n

Nhi\u1ec1u ng\u01b0\u1eddi ngh\u0129 r\u1eb1ng khi H100 xu\u1ea5t hi\u1ec7n th\u00ec A100 s\u1ebd nhanh ch\u00f3ng l\u1ed7i th\u1eddi. Tuy nhi\u00ean th\u1ef1c t\u1ebf ho\u00e0n to\u00e0n ng\u01b0\u1ee3c l\u1ea1i. A100<\/a> v\u1eabn \u0111ang l\u00e0 m\u1ed9t trong nh\u1eefng GPU \u0111\u01b0\u1ee3c thu\u00ea nhi\u1ec1u nh\u1ea5t tr\u00ean th\u1ecb tr\u01b0\u1eddng Cloud GPU nh\u1edd s\u1ef1 c\u00e2n b\u1eb1ng r\u1ea5t t\u1ed1t gi\u1eefa hi\u1ec7u n\u0103ng v\u00e0 chi ph\u00ed.<\/span><\/p>\n

\u0110\u1ed1i v\u1edbi ph\u1ea7n l\u1edbn workload AI hi\u1ec7n nay nh\u01b0 chatbot doanh nghi\u1ec7p, OCR, Stable Diffusion, AI automation hay fine-tune model v\u1eeba v\u00e0 nh\u1ecf, A100 v\u1eabn \u0111\u00e1p \u1ee9ng r\u1ea5t m\u1ea1nh m\u00e0 kh\u00f4ng c\u1ea7n s\u1eed d\u1ee5ng t\u1edbi H100 v\u1edbi m\u1ee9c gi\u00e1 \u0111\u1eaft h\u01a1n r\u1ea5t nhi\u1ec1u.<\/span><\/p>\n

\u0110i\u1ec1u doanh nghi\u1ec7p quan t\u00e2m kh\u00f4ng ch\u1ec9 l\u00e0 benchmark cao nh\u1ea5t m\u00e0 c\u00f2n l\u00e0 kh\u1ea3 n\u0103ng t\u1ed1i \u01b0u ROI, tri\u1ec3n khai nhanh v\u00e0 ti\u1ebft ki\u1ec7m chi ph\u00ed v\u1eadn h\u00e0nh. \u0110\u00e2y l\u00e0 l\u00fd do A100 v\u1eabn \u0111\u01b0\u1ee3c c\u00e1c AI startup, c\u00f4ng ty SaaS v\u00e0 team machine learning s\u1eed d\u1ee5ng r\u1ea5t r\u1ed9ng r\u00e3i.<\/span><\/p>\n

\u0110i\u1ec3m m\u1ea1nh c\u1ee7a Nvidia A100<\/span><\/h2>\n

M\u1ed9t trong nh\u1eefng kh\u00e1c bi\u1ec7t l\u1edbn nh\u1ea5t gi\u1eefa A100 v\u00e0 GPU gaming n\u1eb1m \u1edf Tensor Core. \u0110\u00e2y l\u00e0 th\u00e0nh ph\u1ea7n \u0111\u01b0\u1ee3c t\u1ed1i \u01b0u cho c\u00e1c ph\u00e9p to\u00e1n AI v\u00e0 Deep Learning<\/a>, gi\u00fap t\u0103ng t\u1ed1c qu\u00e1 tr\u00ecnh train model c\u0169ng nh\u01b0 inference.<\/span><\/p>\n

Khi x\u1eed l\u00fd c\u00e1c workload AI l\u1edbn, Tensor Core c\u00f3 th\u1ec3 t\u1ea1o ra kh\u00e1c bi\u1ec7t r\u1ea5t r\u00f5 v\u1ec1 hi\u1ec7u su\u1ea5t. Nh\u1eefng t\u00e1c v\u1ee5 m\u1ea5t nhi\u1ec1u ng\u00e0y tr\u00ean GPU ph\u1ed5 th\u00f4ng c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c r\u00fat ng\u1eafn xu\u1ed1ng ch\u1ec9 c\u00f2n v\u00e0i gi\u1edd khi ch\u1ea1y tr\u00ean c\u1ee5m A100.<\/span><\/p>\n

Ngo\u00e0i Tensor Core, VRAM c\u0169ng l\u00e0 y\u1ebfu t\u1ed1 c\u1ef1c k\u1ef3 quan tr\u1ecdng. Nvidia A100 hi\u1ec7n c\u00f3 hai phi\u00ean b\u1ea3n ph\u1ed5 bi\u1ebfn l\u00e0 40GB v\u00e0 80GB. V\u1edbi AI model ng\u00e0y c\u00e0ng l\u1edbn, VRAM g\u1ea7n nh\u01b0 quy\u1ebft \u0111\u1ecbnh kh\u1ea3 n\u0103ng v\u1eadn h\u00e0nh c\u1ee7a h\u1ec7 th\u1ed1ng.<\/span><\/p>\n

VRAM l\u1edbn gi\u00fap:<\/span><\/p>\n

Load model AI l\u1edbn h\u01a1n<\/span><\/p>\n

T\u0103ng batch size<\/span><\/p>\n

Gi\u1ea3m l\u1ed7i CUDA Out Of Memory<\/span><\/p>\n

Inference \u1ed5n \u0111\u1ecbnh h\u01a1n<\/span><\/p>\n

X\u1eed l\u00fd context d\u00e0i hi\u1ec7u qu\u1ea3 h\u01a1n<\/span><\/p>\n

\u0110i\u1ec1u n\u00e0y \u0111\u1eb7c bi\u1ec7t quan tr\u1ecdng khi l\u00e0m vi\u1ec7c v\u1edbi c\u00e1c m\u00f4 h\u00ecnh nh\u01b0 Llama, Qwen, DeepSeek hay Stable Diffusion XL. M\u1ed9t l\u1ee3i th\u1ebf kh\u00e1c c\u1ee7a A100 l\u00e0 bandwidth b\u1ed9 nh\u1edb r\u1ea5t cao. Trong AI workload, bandwidth \u1ea3nh h\u01b0\u1edfng tr\u1ef1c ti\u1ebfp t\u1edbi t\u1ed1c \u0111\u1ed9 truy xu\u1ea5t d\u1eef li\u1ec7u v\u00e0 hi\u1ec7u su\u1ea5t training. \u0110\u00e2y l\u00e0 l\u00fd do A100 x\u1eed l\u00fd Deep Learning t\u1ed1t h\u01a1n \u0111\u00e1ng k\u1ec3 so v\u1edbi GPU gaming.<\/span><\/p>\n

Ngo\u00e0i ra, Nvidia A100 c\u00f2n h\u1ed7 tr\u1ee3 MIG (Multi-Instance GPU), cho ph\u00e9p chia m\u1ed9t GPU v\u1eadt l\u00fd th\u00e0nh nhi\u1ec1u GPU logic \u0111\u1ed9c l\u1eadp. T\u00ednh n\u0103ng n\u00e0y gi\u00fap cloud provider t\u1ed1i \u01b0u t\u00e0i nguy\u00ean v\u00e0 ch\u1ea1y nhi\u1ec1u workload c\u00f9ng l\u00fac hi\u1ec7u qu\u1ea3 h\u01a1n.<\/span><\/p>\n

Nvidia A100 40GB v\u00e0 80GB kh\u00e1c nhau nh\u01b0 th\u1ebf n\u00e0o?<\/span><\/h2>\n

A100 hi\u1ec7n c\u00f3 hai phi\u00ean b\u1ea3n ph\u1ed5 bi\u1ebfn nh\u1ea5t l\u00e0 40GB v\u00e0 80GB. S\u1ef1 kh\u00e1c bi\u1ec7t l\u1edbn nh\u1ea5t n\u1eb1m \u1edf dung l\u01b0\u1ee3ng VRAM v\u00e0 kh\u1ea3 n\u0103ng x\u1eed l\u00fd AI workload quy m\u00f4 l\u1edbn.<\/span><\/p>\n

Nvidia A100 40GB<\/span><\/h3>\n

A100 40GB<\/a> l\u00e0 l\u1ef1a ch\u1ecdn ph\u1ed5 bi\u1ebfn nh\u1ea5t hi\u1ec7n nay tr\u00ean th\u1ecb tr\u01b0\u1eddng cloud GPU. \u0110\u00e2y l\u00e0 d\u00f2ng GPU ph\u00f9 h\u1ee3p v\u1edbi: <\/span>AI startup, <\/span>Team R&D, <\/span>Fine-tune model v\u1eeba, <\/span>OCR, <\/span>NLP, <\/span>Computer Vision, <\/span>Stable Diffusion, <\/span>AI Chatbot.<\/span><\/p>\n

\u01afu \u0111i\u1ec3m l\u1edbn nh\u1ea5t c\u1ee7a A100 40GB l\u00e0 chi ph\u00ed d\u1ec5 ti\u1ebfp c\u1eadn trong khi hi\u1ec7u n\u0103ng v\u1eabn r\u1ea5t m\u1ea1nh. V\u1edbi \u0111a s\u1ed1 doanh nghi\u1ec7p v\u1eeba v\u00e0 nh\u1ecf, phi\u00ean b\u1ea3n n\u00e0y \u0111\u00e3 \u0111\u1ee7 \u0111\u00e1p \u1ee9ng t\u1ed1t nhu c\u1ea7u AI production.<\/span><\/p>\n

Nvidia A100 80GB<\/span><\/h3>\n

N\u1ebfu A100 40GB t\u1ed1i \u01b0u v\u1ec1 chi ph\u00ed th\u00ec A100 80GB<\/a> l\u1ea1i h\u01b0\u1edbng t\u1edbi c\u00e1c workload AI n\u1eb7ng h\u01a1n. Phi\u00ean b\u1ea3n n\u00e0y ph\u00f9 h\u1ee3p v\u1edbi vi\u1ec7c train LLM l\u1edbn, multi-GPU training v\u00e0 x\u1eed l\u00fd AI concurrent cao.<\/strong><\/span><\/p>\n

So v\u1edbi b\u1ea3n 40GB, A100 80GB kh\u00f4ng ch\u1ec9 c\u00f3 VRAM g\u1ea5p \u0111\u00f4i m\u00e0 c\u00f2n s\u1edf h\u1eefu bandwidth b\u1ed9 nh\u1edb m\u1ea1nh h\u01a1n. \u0110i\u1ec1u n\u00e0y gi\u00fap GPU x\u1eed l\u00fd model l\u1edbn \u1ed5n \u0111\u1ecbnh h\u01a1n, \u0111\u1eb7c bi\u1ec7t khi deploy chatbot AI nhi\u1ec1u ng\u01b0\u1eddi d\u00f9ng \u0111\u1ed3ng th\u1eddi ho\u1eb7c inference context d\u00e0i.<\/span><\/p>\n

\u0110\u1ed1i v\u1edbi c\u00e1c h\u1ec7 th\u1ed1ng AI SaaS quy m\u00f4 l\u1edbn ho\u1eb7c doanh nghi\u1ec7p ph\u00e1t tri\u1ec3n LLM ri\u00eang, A100 80GB th\u01b0\u1eddng l\u00e0 l\u1ef1a ch\u1ecdn ph\u00f9 h\u1ee3p h\u01a1n.<\/span><\/p>\n

>> Xem th\u00eam: Nvidia A100 \u2013 H\u01b0\u1edbng D\u1eabn Thu\u00ea GPU A100 40GB V\u00e0 Gi\u1ea3i \u0110\u00e1p C\u00e1c C\u00e2u H\u1ecfi Th\u01b0\u1eddng G\u1eb7p<\/a><\/strong><\/p>\n

B\u00e1o gi\u00e1 thu\u00ea Nvidia A100 theo gi\u1edd m\u1edbi nh\u1ea5t<\/span><\/h2>\n

\"\"<\/p>\n

Gi\u00e1 thu\u00ea Nvidia A100 40GB<\/span><\/h3>\n

M\u1ed9t trong nh\u1eefng \u0111i\u1ec3m nhi\u1ec1u kh\u00e1ch h\u00e0ng quan t\u00e2m khi thu\u00ea Cloud GPU l\u00e0 c\u00e1ch t\u1ed1i \u01b0u chi ph\u00ed v\u1eadn h\u00e0nh. Th\u1ef1c t\u1ebf, kh\u00f4ng ph\u1ea3i c\u1ee9 thu\u00ea theo gi\u1edd l\u00e0 r\u1ebb nh\u1ea5t, \u0111\u1eb7c bi\u1ec7t khi workload AI k\u00e9o d\u00e0i li\u00ean t\u1ee5c nhi\u1ec1u ng\u00e0y.<\/span><\/p>\n

Hi\u1ec7n nay v\u1edbi g\u00f3i Nvidia A100 40GB t\u1ea1i VNSO, c\u1ea5u h\u00ecnh c\u01a1 b\u1ea3n g\u1ed3m:<\/span><\/p>\n

– 1x Nvidia A100 40GB<\/span><\/p>\n

– 16 vCPU<\/span><\/p>\n

– RAM 48GB<\/span><\/p>\n

– SSD NVMe 1TB<\/span><\/p>\n

VNSO cung c\u1ea5p c\u00e1c h\u00ecnh th\u1ee9c cho thu\u00ea thu\u00ea A100 40GB \u0111a d\u1ea1ng, kh\u00e1c nhau:<\/span><\/p>\n

– Thu\u00ea theo gi\u1edd: Gi\u00e1 thu\u00ea 59.000\u0111\/gi\u1edd<\/strong><\/span><\/p>\n

– Thu\u00ea theo ng\u00e0y: Gi\u00e1 thu\u00ea 1.200.000\u0111\/ng\u00e0y<\/strong><\/span><\/p>\n

– Thu\u00ea theo tu\u1ea7n: Gi\u00e1 thu\u00ea 6.700.000\u0111\/tu\u1ea7n<\/strong><\/span><\/p>\n

– Thu\u00ea theo th\u00e1ng: Gi\u00e1 thu\u00ea t\u1eeb 36.000.000\u0111 – 38.000.000\u0111\/th\u00e1ng<\/strong><\/span><\/p>\n

V\u1edbi d\u1ecbch v\u1ee5 thu\u00ea Cloud GPU (A100) t\u1ea1i VNSO h\u1ec7 th\u1ed1ng y\u00eau c\u1ea7u n\u1ea1p t\u1ed1i thi\u1ec3u 1 tri\u1ec7u \u0111\u1ed3ng \u0111\u1ec3 k\u00edch ho\u1ea1t t\u00e0i kho\u1ea3n Cloud GPU, t\u01b0\u01a1ng \u0111\u01b0\u01a1ng kho\u1ea3ng 16 – 17 gi\u1edd s\u1eed d\u1ee5ng A100 40GB.<\/span><\/p>\n

N\u1ebfu ch\u1ec9 d\u00f9ng \u0111\u1ec3 test model, ch\u1ea1y demo, benchmark ho\u1eb7c fine-tune ng\u1eafn, thu\u00ea theo gi\u1edd l\u00e0 ti\u1ebft ki\u1ec7m nh\u1ea5t v\u00ec ch\u1ec9 tr\u1ea3 \u0111\u00fang th\u1eddi gian s\u1eed d\u1ee5ng. Tuy nhi\u00ean, n\u1ebfu ch\u1ea1y AI li\u00ean t\u1ee5c nhi\u1ec1u ti\u1ebfng ho\u1eb7c \u0111\u1ec3 training qua \u0111\u00eam th\u00ec thu\u00ea theo ng\u00e0y s\u1ebd r\u1ebb h\u01a1n. V\u00ed d\u1ee5 ch\u1ea1y 24 gi\u1edd theo gi\u00e1 gi\u1edd s\u1ebd kho\u1ea3ng 1,4 tri\u1ec7u \u0111\u1ed3ng, trong khi g\u00f3i ng\u00e0y ch\u1ec9 kho\u1ea3ng 1,2 tri\u1ec7u.<\/span><\/p>\n

V\u1edbi workload k\u00e9o d\u00e0i nhi\u1ec1u ng\u00e0y nh\u01b0 train LLM, build dataset ho\u1eb7c inference production, g\u00f3i tu\u1ea7n l\u00e0 t\u1ed1i \u01b0u chi ph\u00ed nh\u1ea5t. Thu\u00ea l\u1ebb 7 ng\u00e0y s\u1ebd kho\u1ea3ng 8,4 tri\u1ec7u \u0111\u1ed3ng nh\u01b0ng g\u00f3i tu\u1ea7n ch\u1ec9 kho\u1ea3ng 6,7 tri\u1ec7u, ti\u1ebft ki\u1ec7m g\u1ea7n 2 tri\u1ec7u \u0111\u1ed3ng.<\/span><\/p>\n

Gi\u00e1 thu\u00ea Nvidia A100 80GB<\/span><\/h3>\n

– 1x Nvidia A100 80GB<\/span><\/p>\n

– 80 vCPU<\/span><\/p>\n

– RAM 96GB<\/span><\/p>\n

– SSD NVMe 2TB<\/span><\/p>\n

VNSO cung c\u1ea5p c\u00e1c h\u00ecnh th\u1ee9c cho thu\u00ea A100 80GB \u0111a d\u1ea1ng, kh\u00e1c nhau:<\/span><\/p>\n

– Thu\u00ea theo gi\u1edd: Gi\u00e1 thu\u00ea 80.000\u0111\/gi\u1edd<\/strong><\/span><\/p>\n

– Thu\u00ea theo ng\u00e0y: Gi\u00e1 thu\u00ea 1.800.000\u0111\/ng\u00e0y<\/strong><\/span><\/p>\n

– Thu\u00ea theo tu\u1ea7n: Gi\u00e1 thu\u00ea 10.800.000\u0111\/tu\u1ea7n<\/strong><\/span><\/p>\n

– Thu\u00ea theo th\u00e1ng: Gi\u00e1 thu\u00ea t\u1eeb 54.000.000\u0111 – 56.000.000\u0111\/th\u00e1ng<\/strong><\/span><\/p>\n

V\u1edbi g\u00f3i Nvidia A100 80GB t\u1ea1i VNSO h\u1ec7 th\u1ed1ng c\u0169ng y\u00eau c\u1ea7u n\u1ea1p t\u1ed1i thi\u1ec3u 1 tri\u1ec7u \u0111\u1ed3ng \u0111\u1ec3 k\u00edch ho\u1ea1t t\u00e0i kho\u1ea3n cloud GPU, t\u01b0\u01a1ng \u0111\u01b0\u01a1ng kho\u1ea3ng 12 \u2013 13 gi\u1edd s\u1eed d\u1ee5ng A100 80GB.<\/span><\/p>\n

N\u1ebfu ch\u1ec9 c\u1ea7n test LLM, benchmark ho\u1eb7c ch\u1ea1y inference ng\u1eafn h\u1ea1n th\u00ec thu\u00ea theo gi\u1edd l\u00e0 h\u1ee3p l\u00fd nh\u1ea5t \u0111\u1ec3 t\u1ed1i \u01b0u chi ph\u00ed. Tuy nhi\u00ean, A100 80GB th\u01b0\u1eddng \u0111\u01b0\u1ee3c d\u00f9ng cho workload n\u1eb7ng nh\u01b0 train model l\u1edbn, context d\u00e0i ho\u1eb7c multi-user inference n\u00ean \u0111a s\u1ed1 kh\u00e1ch h\u00e0ng s\u1ebd ti\u1ebft ki\u1ec7m h\u01a1n khi thu\u00ea theo ng\u00e0y ho\u1eb7c theo tu\u1ea7n.<\/span><\/p>\n

Ngo\u00e0i c\u00e1c d\u00f2ng GPU cao c\u1ea5p nh\u01b0 Nvidia A100 40GB v\u00e0 A100 80GB, VNSO c\u00f2n cung c\u1ea5p \u0111a d\u1ea1ng Cloud GPU<\/a> ph\u00f9 h\u1ee3p cho nhi\u1ec1u nhu c\u1ea7u AI kh\u00e1c nhau, t\u1eeb h\u1ecdc machine learning, test model cho t\u1edbi AI production quy m\u00f4 l\u1edbn. B\u1ea1n c\u00f3 th\u1ec3 l\u1ef1a ch\u1ecdn c\u00e1c d\u00f2ng ph\u1ed5 bi\u1ebfn nh\u01b0 RTX 3060, Tesla P100, Nvidia L4, L40, Blackwell 6000 hay H100,…<\/strong> t\u00f9y theo workload v\u00e0 ng\u00e2n s\u00e1ch th\u1ef1c t\u1ebf. Xem th\u00eam c\u1ea5u h\u00ecnh v\u00e0 b\u1ea3ng gi\u00e1 GPU t\u1ea1i: thuegpu.vnso.vn<\/a><\/p>\n

>> Xem th\u00eam: Cloud GPU theo gi\u1edd \u2013 Gi\u1ea3i ph\u00e1p t\u1ed1i \u01b0u cho Startup AI v\u1ed1n \u00edt<\/a><\/strong><\/p>\n

Th\u01b0 vi\u1ec7n AI c\u00e0i s\u1eb5n \u2013 Deploy nhanh kh\u00f4ng c\u1ea7n setup ph\u1ee9c t\u1ea1p<\/h2>\n

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M\u1ed9t l\u1ee3i th\u1ebf l\u1edbn khi thu\u00ea Cloud GPU Nvidia A100 t\u1ea1i VNSO l\u00e0 h\u1ec7 th\u1ed1ng \u0111\u00e3 \u0111\u01b0\u1ee3c c\u00e0i s\u1eb5n nhi\u1ec1u th\u01b0 vi\u1ec7n AI ph\u1ed5 bi\u1ebfn. Ng\u01b0\u1eddi d\u00f9ng kh\u00f4ng c\u1ea7n m\u1ea5t th\u1eddi gian setup m\u00f4i tr\u01b0\u1eddng hay c\u1ea5u h\u00ecnh CUDA ph\u1ee9c t\u1ea1p nh\u01b0 khi t\u1ef1 build server GPU.<\/p>\n

C\u00e1c framework ph\u1ed5 bi\u1ebfn th\u01b0\u1eddng \u0111\u00e3 \u0111\u01b0\u1ee3c t\u1ed1i \u01b0u s\u1eb5n nh\u01b0: Python 3 & Pip, PyTorch, TensorFlow, OpenCV, Ollama, tmux & screen, CUDA,…<\/strong> \u0110i\u1ec1u n\u00e0y gi\u00fap AI engineer ho\u1eb7c developer c\u00f3 th\u1ec3 t\u1ea1o instance, upload dataset, train model ngay ch\u1ec9 sau v\u00e0i ph\u00fat kh\u1edfi t\u1ea1o server. Ngo\u00e0i ra, m\u00f4i tr\u01b0\u1eddng \u0111\u00e3 \u0111\u01b0\u1ee3c t\u1ed1i \u01b0u cho Nvidia A100 n\u00ean kh\u1ea3 n\u0103ng t\u01b0\u01a1ng th\u00edch v\u00e0 hi\u1ec7u su\u1ea5t th\u01b0\u1eddng \u1ed5n \u0111\u1ecbnh h\u01a1n so v\u1edbi t\u1ef1 c\u00e0i \u0111\u1eb7t th\u1ee7 c\u00f4ng.<\/p>\n

Kho Models AI s\u1eb5n c\u00f3 \u2013 Tri\u1ec3n khai nhanh h\u01a1n<\/h2>\n

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B\u00ean c\u1ea1nh th\u01b0 vi\u1ec7n AI, nhi\u1ec1u h\u1ec7 th\u1ed1ng Cloud GPU hi\u1ec7n nay c\u00f2n t\u00edch h\u1ee3p s\u1eb5n kho AI Models<\/a> \u0111\u1ec3 ng\u01b0\u1eddi d\u00f9ng tri\u1ec3n khai nhanh m\u00e0 kh\u00f4ng c\u1ea7n t\u1ea3i ho\u1eb7c c\u1ea5u h\u00ecnh t\u1eeb \u0111\u1ea7u.<\/p>\n

Ng\u01b0\u1eddi d\u00f9ng c\u00f3 th\u1ec3 nhanh ch\u00f3ng th\u1eed nghi\u1ec7m: Gemma 4<\/a>, Llama, DeepSeek, Qwen, Mistral, Stable Diffusion, Whisper, Embedding Models, GPT-OSS, PersonaPlex-7B, Ministral 3<\/strong><\/p>\n

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C\u00f3 n\u00ean thu\u00ea Nvidia A100 n\u0103m 2026?<\/span><\/h2>\n

C\u00e2u tr\u1ea3 l\u1eddi l\u00e0 c\u00f3. D\u00f9 th\u1ecb tr\u01b0\u1eddng \u0111\u00e3 xu\u1ea5t hi\u1ec7n H100, H200 hay B200, Nvidia A100 v\u1eabn l\u00e0 GPU c\u1ef1c k\u1ef3 \u0111\u00e1ng gi\u00e1 nh\u1edd:<\/span><\/p>\n

– Hi\u1ec7u n\u0103ng m\u1ea1nh<\/span><\/p>\n

– VRAM l\u1edbn<\/span><\/p>\n

– H\u1ec7 sinh th\u00e1i \u1ed5n \u0111\u1ecbnh<\/span><\/p>\n

– D\u1ec5 tri\u1ec3n khai<\/span><\/p>\n

– Chi ph\u00ed h\u1ee3p l\u00fd<\/span><\/p>\n

Trong th\u1ef1c t\u1ebf, r\u1ea5t nhi\u1ec1u h\u1ec7 th\u1ed1ng AI production hi\u1ec7n nay v\u1eabn \u0111ang v\u1eadn h\u00e0nh tr\u00ean A100 m\u1ed7i ng\u00e0y, t\u1eeb chatbot AI cho t\u1edbi AI Agent v\u00e0 h\u1ea1 t\u1ea7ng inference LLM.<\/span><\/p>\n

\u0110\u1ed1i v\u1edbi \u0111a s\u1ed1 doanh nghi\u1ec7p, A100 v\u1eabn mang l\u1ea1i hi\u1ec7u n\u0103ng tr\u00ean chi ph\u00ed t\u1ed1t h\u01a1n r\u1ea5t nhi\u1ec1u so v\u1edbi vi\u1ec7c \u0111\u1ea7u t\u01b0 GPU th\u1ebf h\u1ec7 m\u1edbi v\u1edbi chi ph\u00ed qu\u00e1 cao.<\/span><\/p>\n

>> Xem th\u00eam: Cloud GPU \u2013 V\u00ec sao l\u00e0 n\u1ec1n t\u1ea3ng quan tr\u1ecdng trong hu\u1ea5n luy\u1ec7n AI<\/a><\/strong><\/p>\n

K\u1ebft lu\u1eadn<\/span><\/h2>\n

Nvidia A100<\/strong> v\u1eabn l\u00e0 m\u1ed9t trong nh\u1eefng GPU AI m\u1ea1nh v\u00e0 ph\u1ed5 bi\u1ebfn nh\u1ea5t hi\u1ec7n nay cho AI Training, AI Inference, Machine Learning v\u00e0 Cloud GPU. V\u1edbi hai phi\u00ean b\u1ea3n A100 40GB v\u00e0 A100 80GB, doanh nghi\u1ec7p c\u00f3 th\u1ec3 linh ho\u1ea1t l\u1ef1a ch\u1ecdn theo workload v\u00e0 ng\u00e2n s\u00e1ch.<\/span><\/p>\n

N\u1ebfu b\u1ea1n \u0111ang c\u1ea7n h\u1ea1 t\u1ea7ng \u0111\u1ec3 fine-tune model, deploy chatbot AI, ch\u1ea1y Stable Diffusion ho\u1eb7c x\u00e2y d\u1ef1ng h\u1ec7 th\u1ed1ng LLM production, Nvidia A100 v\u1eabn l\u00e0 l\u1ef1a ch\u1ecdn r\u1ea5t \u0111\u00e1ng c\u00e2n nh\u1eafc trong n\u0103m 2026 nh\u1edd s\u1ef1 c\u00e2n b\u1eb1ng gi\u1eefa hi\u1ec7u n\u0103ng, kh\u1ea3 n\u0103ng scale v\u00e0 chi ph\u00ed v\u1eadn h\u00e0nh. Li\u00ean h\u1ec7 ngay VNSO \u0111\u1ec3 \u0111\u01b0\u1ee3c h\u1ed7 tr\u1ee3 t\u01b0 v\u1ea5n s\u1ea3n ph\u1ea9m. Ho\u1eb7c truy c\u00e2p trang web gpu.vnso.vn<\/a> \u0111\u1ec3 \u0111\u0103ng k\u00fd g\u00f3i thu\u00ea A100 ho\u00e0n to\u00e0n t\u1ef1 \u0111\u1ed9ng, c\u00f3 GPU trong 1 ph\u00fat.<\/span><\/p>\n

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