{"id":24755,"date":"2026-09-25T16:52:32","date_gmt":"2026-09-25T09:52:32","guid":{"rendered":"https:\/\/vnso.vn\/?p=24755"},"modified":"2026-09-25T16:52:32","modified_gmt":"2026-09-25T09:52:32","slug":"ai-inference-la-gi-cach-trien-khai-suy-luan-ai","status":"publish","type":"post","link":"https:\/\/vnso.vn\/en\/ai-inference-la-gi-cach-trien-khai-suy-luan-ai\/","title":{"rendered":"AI Inference l\u00e0 g\u00ec? C\u00e1ch tri\u1ec3n khai Suy lu\u1eadn AI"},"content":{"rendered":"

AI Inference (suy lu\u1eadn AI) l\u00e0 qu\u00e1 tr\u00ecnh s\u1eed d\u1ee5ng m\u1ed9t m\u00f4 h\u00ecnh tr\u00ed tu\u1ec7 nh\u00e2n t\u1ea1o (AI) \u0111\u00e3 \u0111\u01b0\u1ee3c hu\u1ea5n luy\u1ec7n \u0111\u1ec3 x\u1eed l\u00fd d\u1eef li\u1ec7u m\u1edbi v\u00e0 \u0111\u01b0a ra k\u1ebft qu\u1ea3.<\/p>\n

V\u00ed d\u1ee5, khi ng\u01b0\u1eddi d\u00f9ng g\u1eedi c\u00e2u h\u1ecfi cho chatbot AI, h\u1ec7 th\u1ed1ng s\u1ebd \u0111\u01b0a c\u00e2u h\u1ecfi v\u00e0o m\u00f4 h\u00ecnh. M\u00f4 h\u00ecnh ph\u00e2n t\u00edch d\u1eef li\u1ec7u \u0111\u1ea7u v\u00e0o r\u1ed3i t\u1ea1o ra c\u00e2u tr\u1ea3 l\u1eddi. \u0110\u00e2y ch\u00ednh l\u00e0 AI Inference. N\u1ebfu AI Training (hu\u1ea5n luy\u1ec7n AI) l\u00e0 qu\u00e1 tr\u00ecnh gi\u00fap m\u00f4 h\u00ecnh \u201ch\u1ecdc\u201d, AI Inference l\u00e0 qu\u00e1 tr\u00ecnh \u0111\u01b0a m\u00f4 h\u00ecnh \u0111\u00f3 v\u00e0o s\u1eed d\u1ee5ng th\u1ef1c t\u1ebf.<\/p>\n

Khi doanh nghi\u1ec7p tri\u1ec3n khai chatbot, RAG, AI Agent, nh\u1eadn di\u1ec7n h\u00ecnh \u1ea3nh ho\u1eb7c c\u00e1c \u1ee9ng d\u1ee5ng Generative AI, Inference tr\u1edf th\u00e0nh m\u1ed9t workload c\u1ea7n \u0111\u01b0\u1ee3c x\u1eed l\u00fd li\u00ean t\u1ee5c. Khi s\u1ed1 l\u01b0\u1ee3ng ng\u01b0\u1eddi d\u00f9ng v\u00e0 request t\u0103ng, y\u00eau c\u1ea7u v\u1ec1 GPU, VRAM, t\u1ed1c \u0111\u1ed9 x\u1eed l\u00fd v\u00e0 kh\u1ea3 n\u0103ng m\u1edf r\u1ed9ng c\u0169ng t\u0103ng theo.<\/p>\n

AI Inference l\u00e0 g\u00ec? Kh\u00e1c g\u00ec v\u1edbi AI Training?<\/h2>\n

AI Training v\u00e0 AI Inference \u0111\u1ec1u s\u1eed d\u1ee5ng t\u00e0i nguy\u00ean t\u00ednh to\u00e1n<\/a> \u0111\u1ec3 x\u1eed l\u00fd m\u00f4 h\u00ecnh. Tuy nhi\u00ean, m\u1ee5c \u0111\u00edch c\u1ee7a hai qu\u00e1 tr\u00ecnh ho\u00e0n to\u00e0n kh\u00e1c nhau. AI Training d\u00f9ng d\u1eef li\u1ec7u \u0111\u1ec3 hu\u1ea5n luy\u1ec7n m\u00f4 h\u00ecnh. Trong giai \u0111o\u1ea1n n\u00e0y, h\u1ec7 th\u1ed1ng li\u00ean t\u1ee5c c\u1eadp nh\u1eadt c\u00e1c tham s\u1ed1 c\u1ee7a model \u0111\u1ec3 m\u00f4 h\u00ecnh h\u1ecdc \u0111\u01b0\u1ee3c c\u00e1ch x\u1eed l\u00fd nhi\u1ec7m v\u1ee5.<\/p>\n

Sau khi model ho\u00e0n t\u1ea5t qu\u00e1 tr\u00ecnh hu\u1ea5n luy\u1ec7n, n\u00f3 c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c \u0111\u01b0a v\u00e0o AI Inference. Inference kh\u00f4ng c\u00f2n t\u1eadp trung v\u00e0o vi\u1ec7c \u201cd\u1ea1y\u201d model. Thay v\u00e0o \u0111\u00f3, h\u1ec7 th\u1ed1ng s\u1eed d\u1ee5ng model \u0111\u00e3 c\u00f3 \u0111\u1ec3 x\u1eed l\u00fd d\u1eef li\u1ec7u m\u1edbi.<\/p>\n

V\u00ed d\u1ee5, m\u1ed9t doanh nghi\u1ec7p x\u00e2y d\u1ef1ng chatbot n\u1ed9i b\u1ed9 b\u1eb1ng m\u1ed9t m\u00f4 h\u00ecnh ng\u00f4n ng\u1eef l\u1edbn (Large Language Model – LLM). Qu\u00e1 tr\u00ecnh fine-tuning ho\u1eb7c training model l\u00e0 AI Training. Khi nh\u00e2n vi\u00ean g\u1eedi c\u00e2u h\u1ecfi v\u00e0 chatbot t\u1ea1o c\u00e2u tr\u1ea3 l\u1eddi, \u0111\u00f3 l\u00e0 AI Inference.<\/p>\n

M\u1ed9t s\u1ed1 \u1ee9ng d\u1ee5ng ph\u1ed5 bi\u1ebfn c\u1ee7a AI Inference g\u1ed3m chatbot, tr\u1ee3 l\u00fd AI, t\u00ecm ki\u1ebfm th\u00f4ng minh, RAG, AI Agent, nh\u1eadn di\u1ec7n h\u00ecnh \u1ea3nh, ph\u00e2n t\u00edch video, x\u1eed l\u00fd gi\u1ecdng n\u00f3i v\u00e0 c\u00e1c \u1ee9ng d\u1ee5ng Generative AI.<\/p>\n

\u0110i\u1ec3m kh\u00e1c bi\u1ec7t n\u00e0y c\u0169ng \u1ea3nh h\u01b0\u1edfng \u0111\u1ebfn c\u00e1ch x\u00e2y d\u1ef1ng h\u1ea1 t\u1ea7ng. Training th\u01b0\u1eddng c\u1ea7n l\u01b0\u1ee3ng t\u00e0i nguy\u00ean t\u00ednh to\u00e1n r\u1ea5t l\u1edbn trong t\u1eebng giai \u0111o\u1ea1n hu\u1ea5n luy\u1ec7n. Inference l\u1ea1i c\u00f3 th\u1ec3 ph\u00e1t sinh li\u00ean t\u1ee5c khi \u1ee9ng d\u1ee5ng \u0111\u01b0\u1ee3c \u0111\u01b0a v\u00e0o v\u1eadn h\u00e0nh. V\u00ec v\u1eady, m\u1ed9t h\u1ec7 th\u1ed1ng Inference c\u1ea7n quan t\u00e2m nhi\u1ec1u \u0111\u1ebfn t\u1ed1c \u0111\u1ed9 ph\u1ea3n h\u1ed3i, s\u1ed1 request \u0111\u1ed3ng th\u1eddi, kh\u1ea3 n\u0103ng m\u1edf r\u1ed9ng v\u00e0 chi ph\u00ed cho m\u1ed7i workload.<\/p>\n

>>> Xem th\u00eam H\u1ea1 t\u1ea7ng AI l\u00e0 g\u00ec? C\u00e1ch tri\u1ec3n khai AI hi\u1ec7u qu\u1ea3 doanh nghi\u1ec7p<\/a><\/strong><\/p>\n

\"AI<\/a><\/h2>\n

AI Inference ho\u1ea1t \u0111\u1ed9ng nh\u01b0 th\u1ebf n\u00e0o tr\u00ean GPU?<\/h2>\n

\u1ede m\u1ee9c c\u01a1 b\u1ea3n, quy tr\u00ecnh c\u00f3 th\u1ec3 hi\u1ec3u \u0111\u01a1n gi\u1ea3n: D\u1eef li\u1ec7u \u0111\u1ea7u v\u00e0o \u2192 Model \u2192 GPU x\u1eed l\u00fd \u2192 K\u1ebft qu\u1ea3 \u0111\u1ea7u ra<\/strong><\/p>\n

GPU \u0111\u00f3ng vai tr\u00f2 x\u1eed l\u00fd c\u00e1c ph\u00e9p t\u00ednh song song c\u1ea7n thi\u1ebft \u0111\u1ec3 m\u00f4 h\u00ecnh t\u1ea1o ra k\u1ebft qu\u1ea3. \u0110\u00e2y l\u00e0 l\u00fd do GPU th\u01b0\u1eddng \u0111\u01b0\u1ee3c s\u1eed d\u1ee5ng cho c\u00e1c workload AI c\u00f3 y\u00eau c\u1ea7u t\u00ednh to\u00e1n cao.Tuy nhi\u00ean, l\u1ef1a ch\u1ecdn GPU cho Inference kh\u00f4ng ch\u1ec9 d\u1ef1a tr\u00ean vi\u1ec7c GPU c\u00f3 m\u1ea1nh hay kh\u00f4ng.<\/p>\n

VRAM<\/h3>\n

VRAM l\u00e0 m\u1ed9t trong nh\u1eefng y\u1ebfu t\u1ed1 quan tr\u1ecdng nh\u1ea5t.<\/p>\n

Model c\u1ea7n \u0111\u01b0\u1ee3c t\u1ea3i v\u00e0o b\u1ed9 nh\u1edb GPU \u0111\u1ec3 th\u1ef1c thi. Model c\u00e0ng l\u1edbn th\u00ec nhu c\u1ea7u b\u1ed9 nh\u1edb c\u00e0ng cao. V\u1edbi LLM, GPU c\u00f2n ph\u1ea3i d\u00e0nh b\u1ed9 nh\u1edb cho KV Cache trong qu\u00e1 tr\u00ecnh x\u1eed l\u00fd request.<\/p>\n

Khi c\u00f3 nhi\u1ec1u ng\u01b0\u1eddi d\u00f9ng c\u00f9ng g\u1eedi y\u00eau c\u1ea7u, l\u01b0\u1ee3ng KV Cache c\u1ea7n qu\u1ea3n l\u00fd c\u0169ng c\u00f3 th\u1ec3 t\u0103ng. V\u00ec v\u1eady, GPU c\u00f3 \u0111\u1ee7 VRAM l\u00e0 \u0111i\u1ec1u ki\u1ec7n quan tr\u1ecdng \u0111\u1ec3 tri\u1ec3n khai model \u1ed5n \u0111\u1ecbnh.<\/p>\n

GPU Compute<\/h3>\n

N\u0103ng l\u1ef1c t\u00ednh to\u00e1n c\u1ee7a GPU \u1ea3nh h\u01b0\u1edfng \u0111\u1ebfn t\u1ed1c \u0111\u1ed9 x\u1eed l\u00fd model. Tuy nhi\u00ean, \u0111\u00e2y ch\u1ec9 l\u00e0 m\u1ed9t ph\u1ea7n c\u1ee7a b\u00e0i to\u00e1n. Hai GPU c\u00f3 c\u1ea5u h\u00ecnh kh\u00e1c nhau c\u00f3 th\u1ec3 cho hi\u1ec7u n\u0103ng Inference kh\u00e1c nhau t\u00f9y model v\u00e0 workload.<\/p>\n

Memory Bandwidth<\/h3>\n

\u0110\u1eb7c bi\u1ec7t v\u1edbi LLM, t\u1ed1c \u0111\u1ed9 di chuy\u1ec3n d\u1eef li\u1ec7u gi\u1eefa b\u1ed9 nh\u1edb GPU v\u00e0 c\u00e1c \u0111\u01a1n v\u1ecb t\u00ednh to\u00e1n c\u00f3 \u1ea3nh h\u01b0\u1edfng l\u1edbn \u0111\u1ebfn hi\u1ec7u n\u0103ng. LLM Inference th\u01b0\u1eddng g\u1ed3m hai giai \u0111o\u1ea1n ch\u00ednh l\u00e0 Prefill<\/strong> v\u00e0 Decode<\/strong>.<\/p>\n

Prefill x\u1eed l\u00fd to\u00e0n b\u1ed9 prompt \u0111\u1ea7u v\u00e0o \u0111\u1ec3 t\u1ea1o tr\u1ea1ng th\u00e1i c\u1ea7n thi\u1ebft cho qu\u00e1 tr\u00ecnh sinh n\u1ed9i dung. Giai \u0111o\u1ea1n n\u00e0y thi\u00ean v\u1ec1 kh\u1ea3 n\u0103ng t\u00ednh to\u00e1n song song.<\/p>\n

Decode t\u1ea1o ra c\u00e2u tr\u1ea3 l\u1eddi t\u1eebng token. Giai \u0111o\u1ea1n n\u00e0y ph\u1ee5 thu\u1ed9c nhi\u1ec1u v\u00e0o t\u1ed1c \u0111\u1ed9 truy xu\u1ea5t model weights v\u00e0 KV Cache t\u1eeb b\u1ed9 nh\u1edb GPU.<\/p>\n

Do \u0111\u00f3, m\u1ed9t GPU ph\u00f9 h\u1ee3p cho AI Inference c\u1ea7n c\u00f3 s\u1ef1 c\u00e2n b\u1eb1ng gi\u1eefa n\u0103ng l\u1ef1c t\u00ednh to\u00e1n, VRAM v\u00e0 memory bandwidth.<\/p>\n

Inference Engine<\/h3>\n

GPU c\u0169ng kh\u00f4ng ho\u1ea1t \u0111\u1ed9ng \u0111\u1ed9c l\u1eadp. Model c\u1ea7n \u0111\u01b0\u1ee3c tri\u1ec3n khai th\u00f4ng qua c\u00e1c ph\u1ea7n m\u1ec1m ph\u1ee5c v\u1ee5 Inference. M\u1ed9t s\u1ed1 c\u00f4ng c\u1ee5 ph\u1ed5 bi\u1ebfn hi\u1ec7n nay g\u1ed3m vLLM, NVIDIA TensorRT-LLM, NVIDIA Triton Inference Server v\u00e0 SGLang.<\/p>\n

C\u00e1c inference engine c\u00f3 th\u1ec3 h\u1ed7 tr\u1ee3 nhi\u1ec1u k\u1ef9 thu\u1eadt t\u1ed1i \u01b0u nh\u01b0 batching, qu\u1ea3n l\u00fd KV Cache, quantization ho\u1eb7c t\u1ed1i \u01b0u qu\u00e1 tr\u00ecnh th\u1ef1c thi model. V\u00ec v\u1eady, hi\u1ec7u n\u0103ng Inference th\u1ef1c t\u1ebf ph\u1ee5 thu\u1ed9c v\u00e0o c\u1ea3 GPU + model + inference engine + workload<\/strong>.<\/p>\n

>>> Xem th\u00eam NVIDIA T4 Tensor Core GPU – T\u1ed1i \u0111a suy lu\u1eadn AI Inference<\/a><\/strong><\/p>\n

L\u00e0m th\u1ebf n\u00e0o \u0111\u1ec3 t\u1ed1i \u01b0u AI Inference tr\u00ean GPU?<\/h2>\n

Khi s\u1ed1 l\u01b0\u1ee3ng request t\u0103ng, ch\u1ec9 b\u1ed5 sung GPU ch\u01b0a ch\u1eafc \u0111\u00e3 gi\u1ea3i quy\u1ebft to\u00e0n b\u1ed9 v\u1ea5n \u0111\u1ec1. H\u1ec7 th\u1ed1ng c\u1ea7n \u0111\u01b0\u1ee3c t\u1ed1i \u01b0u \u0111\u1ed3ng th\u1eddi \u1edf c\u1ea3 ph\u1ea7n c\u1ee9ng v\u00e0 ph\u1ea7n m\u1ec1m.<\/p>\n

Batching<\/h3>\n

Thay v\u00ec x\u1eed l\u00fd t\u1eebng request ri\u00eang l\u1ebb, h\u1ec7 th\u1ed1ng c\u00f3 th\u1ec3 gom nhi\u1ec1u request \u0111\u1ec3 GPU x\u1eed l\u00fd hi\u1ec7u qu\u1ea3 h\u01a1n.<\/p>\n

V\u1edbi LLM, continuous batching cho ph\u00e9p h\u1ec7 th\u1ed1ng li\u00ean t\u1ee5c \u0111\u01b0a request m\u1edbi v\u00e0o khi nh\u1eefng request kh\u00e1c ho\u00e0n th\u00e0nh. C\u00e1ch n\u00e0y gi\u00fap t\u1eadn d\u1ee5ng GPU t\u1ed1t h\u01a1n khi c\u00f3 nhi\u1ec1u ng\u01b0\u1eddi d\u00f9ng \u0111\u1ed3ng th\u1eddi.<\/p>\n

Quantization<\/h3>\n

Quantization gi\u1ea3m \u0111\u1ed9 ch\u00ednh x\u00e1c bi\u1ec3u di\u1ec5n c\u1ee7a model, ch\u1eb3ng h\u1ea1n t\u1eeb FP16 xu\u1ed1ng INT8 ho\u1eb7c INT4.<\/p>\n

M\u1ee5c ti\u00eau l\u00e0 gi\u1ea3m dung l\u01b0\u1ee3ng model v\u00e0 nhu c\u1ea7u b\u1ed9 nh\u1edb. Trong m\u1ed9t s\u1ed1 tr\u01b0\u1eddng h\u1ee3p, c\u00e1ch n\u00e0y c\u0169ng gi\u00fap t\u0103ng t\u1ed1c Inference.<\/p>\n

Tuy nhi\u00ean, quantization c\u00f3 th\u1ec3 t\u1ea1o ra trade-off v\u1ec1 ch\u1ea5t l\u01b0\u1ee3ng. Doanh nghi\u1ec7p n\u00ean benchmark tr\u00ean ch\u00ednh model v\u00e0 workload th\u1ef1c t\u1ebf tr\u01b0\u1edbc khi tri\u1ec3n khai production.<\/p>\n

KV Cache v\u00e0 PagedAttention<\/h3>\n

LLM c\u1ea7n l\u01b0u tr\u1eef KV Cache trong qu\u00e1 tr\u00ecnh sinh n\u1ed9i dung. N\u1ebfu qu\u1ea3n l\u00fd b\u1ed9 nh\u1edb kh\u00f4ng hi\u1ec7u qu\u1ea3, VRAM c\u00f3 th\u1ec3 b\u1ecb s\u1eed d\u1ee5ng nhi\u1ec1u v\u00e0 g\u00e2y gi\u1edbi h\u1ea1n v\u1ec1 s\u1ed1 request \u0111\u1ed3ng th\u1eddi.<\/p>\n

C\u00e1c inference engine hi\u1ec7n \u0111\u1ea1i s\u1eed d\u1ee5ng nh\u1eefng k\u1ef9 thu\u1eadt nh\u01b0 PagedAttention \u0111\u1ec3 qu\u1ea3n l\u00fd KV Cache hi\u1ec7u qu\u1ea3 h\u01a1n.<\/p>\n

Prefix Caching<\/h3>\n

N\u1ebfu nhi\u1ec1u request s\u1eed d\u1ee5ng c\u00f9ng m\u1ed9t ph\u1ea7n prompt, h\u1ec7 th\u1ed1ng c\u00f3 th\u1ec3 t\u00e1i s\u1eed d\u1ee5ng k\u1ebft qu\u1ea3 \u0111\u00e3 x\u1eed l\u00fd thay v\u00ec t\u00ednh to\u00e1n l\u1ea1i to\u00e0n b\u1ed9.<\/p>\n

\u0110i\u1ec1u n\u00e0y \u0111\u1eb7c bi\u1ec7t h\u1eefu \u00edch v\u1edbi c\u00e1c \u1ee9ng d\u1ee5ng c\u00f3 system prompt d\u00e0i ho\u1eb7c nhi\u1ec1u request s\u1eed d\u1ee5ng chung m\u1ed9t context.<\/p>\n

Ch\u1ecdn GPU theo workload<\/h3>\n

Kh\u00f4ng c\u00f3 m\u1ed9t GPU ph\u00f9 h\u1ee3p cho m\u1ecdi b\u00e0i to\u00e1n. M\u1ed9t model nh\u1ecf d\u00f9ng \u0111\u1ec3 ph\u00e1t tri\u1ec3n v\u00e0 th\u1eed nghi\u1ec7m c\u00f3 th\u1ec3 kh\u00f4ng c\u1ea7n GPU c\u00f9ng ph\u00e2n kh\u00fac v\u1edbi m\u1ed9t LLM l\u1edbn ph\u1ee5c v\u1ee5 h\u00e0ng tr\u0103m request \u0111\u1ed3ng th\u1eddi.<\/p>\n

Khi l\u1ef1a ch\u1ecdn GPU, n\u00ean x\u00e1c \u0111\u1ecbnh tr\u01b0\u1edbc model, k\u00edch th\u01b0\u1edbc model, VRAM c\u1ea7n thi\u1ebft, \u0111\u1ed9 d\u00e0i context, s\u1ed1 ng\u01b0\u1eddi d\u00f9ng \u0111\u1ed3ng th\u1eddi, y\u00eau c\u1ea7u latency v\u00e0 throughput. N\u00ean \u0111\u1eb7t c\u00e2u h\u1ecfi: GPU n\u00e0o \u0111\u00e1p \u1ee9ng \u0111\u01b0\u1ee3c workload AI Inference v\u1edbi hi\u1ec7u n\u0103ng v\u00e0 chi ph\u00ed ph\u00f9 h\u1ee3p?<\/strong><\/p>\n

\"Tri\u1ec3n<\/a><\/p>\n

Tri\u1ec3n khai AI Inference tr\u00ean Cloud GPU nh\u01b0 th\u1ebf n\u00e0o?<\/h2>\n

Cloud GPU cung c\u1ea5p GPU th\u00f4ng qua m\u00f4i tr\u01b0\u1eddng cloud. Ng\u01b0\u1eddi d\u00f9ng c\u00f3 th\u1ec3 t\u1ea1o m\u00e1y \u1ea3o ho\u1eb7c instance c\u00f3 GPU, c\u00e0i \u0111\u1eb7t m\u00f4i tr\u01b0\u1eddng AI v\u00e0 tri\u1ec3n khai model m\u00e0 kh\u00f4ng c\u1ea7n \u0111\u1ea7u t\u01b0 to\u00e0n b\u1ed9 m\u1ed9t GPU Server v\u1eadt l\u00fd ngay t\u1eeb \u0111\u1ea7u.<\/p>\n

Quy tr\u00ecnh tri\u1ec3n khai c\u00f3 th\u1ec3 g\u1ed3m c\u00e1c b\u01b0\u1edbc sau.<\/p>\n

B\u01b0\u1edbc 1: X\u00e1c \u0111\u1ecbnh model<\/strong><\/p>\n

X\u00e1c \u0111\u1ecbnh model s\u1ebd s\u1eed d\u1ee5ng. V\u00ed d\u1ee5 Llama, Qwen, DeepSeek ho\u1eb7c m\u1ed9t model Computer Vision.<\/p>\n

B\u01b0\u1edbc 2: X\u00e1c \u0111\u1ecbnh y\u00eau c\u1ea7u t\u00e0i nguy\u00ean<\/strong><\/p>\n

Ki\u1ec3m tra k\u00edch th\u01b0\u1edbc model, y\u00eau c\u1ea7u VRAM, context length v\u00e0 s\u1ed1 request \u0111\u1ed3ng th\u1eddi.<\/p>\n

B\u01b0\u1edbc 3: Ch\u1ecdn GPU<\/strong><\/p>\n

Ch\u1ecdn GPU d\u1ef1a tr\u00ean workload thay v\u00ec ch\u1ec9 d\u1ef1a v\u00e0o t\u00ean s\u1ea3n ph\u1ea9m. V\u1edbi model nh\u1ecf ho\u1eb7c giai \u0111o\u1ea1n development, c\u00f3 th\u1ec3 s\u1eed d\u1ee5ng GPU c\u00f3 VRAM v\u1eeba ph\u1ea3i. V\u1edbi model l\u1edbn ho\u1eb7c production workload, c\u00f3 th\u1ec3 c\u1ea7n GPU c\u00f3 VRAM cao h\u01a1n ho\u1eb7c nhi\u1ec1u GPU.<\/p>\n

B\u01b0\u1edbc 4: Chu\u1ea9n b\u1ecb m\u00f4i tr\u01b0\u1eddng AI<\/strong><\/p>\n

C\u00e0i \u0111\u1eb7t driver, CUDA, framework v\u00e0 inference engine c\u1ea7n thi\u1ebft. C\u00e1c framework ph\u1ed5 bi\u1ebfn g\u1ed3m PyTorch v\u00e0 TensorFlow.<\/p>\n

B\u01b0\u1edbc 5: Deploy model<\/strong><\/p>\n

\u0110\u01b0a model l\u00ean m\u00f4i tr\u01b0\u1eddng GPU v\u00e0 kh\u1edfi ch\u1ea1y inference server. V\u1edbi LLM, c\u00f3 th\u1ec3 s\u1eed d\u1ee5ng c\u00e1c inference engine nh\u01b0 vLLM ho\u1eb7c TensorRT-LLM.<\/p>\n

B\u01b0\u1edbc 6: Benchmark<\/strong><\/p>\n

\u0110o c\u00e1c ch\u1ec9 s\u1ed1 nh\u01b0 latency, Time to First Token (TTFT), throughput, tokens per second v\u00e0 GPU utilization.<\/p>\n

B\u01b0\u1edbc 7: T\u1ed1i \u01b0u<\/strong><\/p>\n

N\u1ebfu h\u1ec7 th\u1ed1ng ch\u01b0a \u0111\u00e1p \u1ee9ng y\u00eau c\u1ea7u, c\u00f3 th\u1ec3 \u0111i\u1ec1u ch\u1ec9nh batch, quantization, KV Cache ho\u1eb7c inference engine. Tr\u01b0\u1eddng h\u1ee3p c\u1ea7n thi\u1ebft c\u00f3 th\u1ec3 n\u00e2ng c\u1ea5p GPU ho\u1eb7c m\u1edf r\u1ed9ng th\u00e0nh nhi\u1ec1u GPU. Cloud GPU \u0111\u1eb7c bi\u1ec7t ph\u00f9 h\u1ee3p v\u1edbi giai \u0111o\u1ea1n ph\u00e1t tri\u1ec3n v\u00e0 th\u1eed nghi\u1ec7m AI. Doanh nghi\u1ec7p c\u00f3 th\u1ec3 th\u1eed nhi\u1ec1u c\u1ea5u h\u00ecnh GPU tr\u01b0\u1edbc khi quy\u1ebft \u0111\u1ecbnh \u0111\u1ea7u t\u01b0 GPU Server ri\u00eang.<\/p>\n

V\u1edbi workload thay \u0111\u1ed5i theo th\u1eddi gian, Cloud GPU c\u0169ng gi\u00fap doanh nghi\u1ec7p linh ho\u1ea1t h\u01a1n trong vi\u1ec7c ph\u00e2n b\u1ed5 t\u00e0i nguy\u00ean. Khi workload \u0111\u00e3 \u1ed5n \u0111\u1ecbnh v\u00e0 s\u1eed d\u1ee5ng li\u00ean t\u1ee5c, doanh nghi\u1ec7p c\u00f3 th\u1ec3 c\u00e2n nh\u1eafc GPU Server ri\u00eang ho\u1eb7c m\u00f4 h\u00ecnh hybrid gi\u1eefa Cloud GPU v\u00e0 h\u1ea1 t\u1ea7ng on-premise.<\/p>\n

\"Server<\/a>

Server GPU NVIDIA A100 t\u1ea1i VNSO<\/p><\/div>\n

Ch\u1ecdn H\u1ea1 t\u1ea7ng GPU h\u00e0ng \u0111\u1ea7u cho AI Inference<\/h2>\n

AI Inference \u0111ang tr\u1edf th\u00e0nh m\u1ed9t ph\u1ea7n quan tr\u1ecdng trong qu\u00e1 tr\u00ecnh \u0111\u01b0a AI t\u1eeb giai \u0111o\u1ea1n th\u1eed nghi\u1ec7m v\u00e0o c\u00e1c \u1ee9ng d\u1ee5ng th\u1ef1c t\u1ebf.<\/p>\n

Theo Gartner, chi ti\u00eau to\u00e0n c\u1ea7u cho AI-optimized IaaS \u0111\u01b0\u1ee3c d\u1ef1 b\u00e1o \u0111\u1ea1t kho\u1ea3ng 42,3 t\u1ef7 USD trong n\u0103m 2026, t\u0103ng 96,4%. \u0110\u00e1ng ch\u00fa \u00fd, chi ti\u00eau cho workload Inference \u0111\u01b0\u1ee3c d\u1ef1 b\u00e1o \u0111\u1ea1t 23,3 t\u1ef7 USD, cao h\u01a1n 19 t\u1ef7 USD d\u00e0nh cho Training. \u0110i\u1ec1u n\u00e0y cho th\u1ea5y nhu c\u1ea7u h\u1ea1 t\u1ea7ng ph\u1ee5c v\u1ee5 AI Inference \u0111ang ng\u00e0y c\u00e0ng t\u0103ng.<\/p>\n

V\u1edbi doanh nghi\u1ec7p v\u00e0 \u0111\u1ed9i ng\u0169 AI\/Dev, Cloud GPU l\u00e0 m\u1ed9t l\u1ef1a ch\u1ecdn \u0111\u1ec3 b\u1eaft \u0111\u1ea7u tri\u1ec3n khai Inference m\u00e0 kh\u00f4ng c\u1ea7n \u0111\u1ea7u t\u01b0 ngay m\u1ed9t h\u1ec7 th\u1ed1ng GPU Server ri\u00eang.<\/p>\n

VNSO cung c\u1ea5p Cloud GPU<\/a> v\u1edbi nhi\u1ec1u c\u1ea5u h\u00ecnh GPU,<\/strong> h\u1ed7 tr\u1ee3 thu\u00ea theo gi\u1edd ho\u1eb7c theo th\u00e1ng. Ng\u01b0\u1eddi d\u00f9ng c\u00f3 th\u1ec3 l\u1ef1a ch\u1ecdn t\u00e0i nguy\u00ean d\u1ef1a tr\u00ean nhu c\u1ea7u th\u1ef1c t\u1ebf v\u00e0 tri\u1ec3n khai m\u00f4i tr\u01b0\u1eddng AI cho c\u00e1c workload nh\u01b0 LLM, RAG, Computer Vision v\u00e0 Generative AI.<\/p>\n

M\u00f4i tr\u01b0\u1eddng Cloud GPU VNSO h\u1ed7 tr\u1ee3 c\u00e1c c\u00f4ng c\u1ee5 v\u00e0 framework ph\u1ed5 bi\u1ebfn nh\u01b0 CUDA, cuDNN, Python, PyTorch, TensorFlow v\u00e0 OpenCV, gi\u00fap r\u00fat ng\u1eafn th\u1eddi gian chu\u1ea9n b\u1ecb m\u00f4i tr\u01b0\u1eddng.<\/p>\n

T\u00f9y workload, doanh nghi\u1ec7p c\u00f3 th\u1ec3 b\u1eaft \u0111\u1ea7u t\u1eeb m\u1ed9t GPU \u0111\u1ec3 development v\u00e0 testing, sau \u0111\u00f3 benchmark v\u00e0 n\u00e2ng c\u1ea5p t\u00e0i nguy\u00ean khi \u0111\u01b0a \u1ee9ng d\u1ee5ng v\u00e0o v\u1eadn h\u00e0nh.<\/p>\n

B\u1ea1n \u0111ang c\u1ea7n GPU \u0111\u1ec3 ch\u1ea1y LLM, RAG, AI Agent ho\u1eb7c c\u00e1c m\u00f4 h\u00ecnh AI kh\u00e1c?<\/strong><\/p>\n

>>> Tham kh\u1ea3o Cloud GPU VNSO<\/strong> \u0111\u1ec3 l\u1ef1a ch\u1ecdn c\u1ea5u h\u00ecnh GPU ph\u00f9 h\u1ee3p v\u1edbi workload v\u00e0 b\u1eaft \u0111\u1ea7u tri\u1ec3n khai AI Inference.<\/p>\n

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