{"id":11463,"date":"2024-06-29T14:27:15","date_gmt":"2024-06-29T07:27:15","guid":{"rendered":"https:\/\/serverhot.vn\/product\/uncategorized\/nvidia-t4-tensor-core-gpu-16gb-gddr6-pcie-3-0-passive-ma-san-pham-900-2g183-0000-001\/"},"modified":"2024-06-29T14:27:17","modified_gmt":"2024-06-29T07:27:17","slug":"nvidia-t4-tensor-core-gpu-16gb-gddr6-pcie-3-0-passive-ma-san-pham-900-2g183-0000-001","status":"publish","type":"product","link":"https:\/\/serverhot.vn\/bn\/-product-\/nvidia-t4-tensor-core-gpu-16gb-gddr6-pcie-3-0-passive-ma-san-pham-900-2g183-0000-001\/","title":{"rendered":"NVIDIA T4 Tensor Core GPU 16GB GDDR6 PCIe 3.0 Passive M\u00e3 s\u1ea3n ph\u1ea9m: 900-2G183-0000-001"},"content":{"rendered":"
\n
\n
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Manufacturer<\/p>\n<\/td>\n

\n

Nvidia<\/p>\n<\/td>\n<\/tr>\n

\n

Model<\/p>\n<\/td>\n

\n

900-2G183-0000-001<\/p>\n<\/td>\n<\/tr>\n

\n

GPU Architecture
\u00a0<\/p>\n<\/td>\n

\n

NVIDIA Turing<\/p>\n<\/td>\n<\/tr>\n

\n

NVIDIA Turing TensorCores
\u00a0<\/p>\n<\/td>\n

\n

320<\/p>\n<\/td>\n<\/tr>\n

\n

NVIDIA CUDA\u00ae Cores
\u00a0<\/p>\n<\/td>\n

\n

2,560<\/p>\n<\/td>\n<\/tr>\n

\n

Single-Precision
\u00a0<\/p>\n<\/td>\n

\n

8.1 TFLOPS<\/p>\n<\/td>\n<\/tr>\n

\n

Mixed-Precision(FP16\/FP32)
\u00a0<\/p>\n<\/td>\n

\n

65 TFLOPS<\/p>\n<\/td>\n<\/tr>\n

\n

INT8<\/p>\n<\/td>\n

\n

130 TOPS<\/p>\n<\/td>\n<\/tr>\n

\n

INT4<\/p>\n<\/td>\n

\n

260 TOPS<\/p>\n<\/td>\n<\/tr>\n

\n

GPU Memory
\u00a0<\/p>\n<\/td>\n

\n

16 GB GDDR6\u00a0300 GB\/sec<\/p>\n<\/td>\n<\/tr>\n

\n

ECC<\/p>\n<\/td>\n

\n

Yes<\/p>\n<\/td>\n<\/tr>\n

\n

Interconnect Bandwidth
\u00a0<\/p>\n<\/td>\n

\n

32 GB\/sec<\/p>\n<\/td>\n<\/tr>\n

\n

System Interface
\u00a0<\/p>\n<\/td>\n

\n

x16 PCIe Gen3<\/p>\n<\/td>\n<\/tr>\n

\n

Form Factor
\u00a0<\/p>\n<\/td>\n

\n

Low-Profile PCIe<\/p>\n<\/td>\n<\/tr>\n

\n

Thermal Solution
\u00a0<\/p>\n<\/td>\n

\n

Passive<\/p>\n<\/td>\n<\/tr>\n

\n

Compute APIs
\u00a0<\/p>\n<\/td>\n

\n

CUDA, NVIDIA TensorRT\u2122, ONNX<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table><\/div>\n


\n
\n
\n
\n \n <\/p><\/div>\n<\/p><\/div>","protected":false},"excerpt":{"rendered":"

    \n
      \n
    • Nh\u00e2n CUDA : 2,560<\/li>\n
    • Hi\u1ec7u su\u1ea5t (FP32): 8.1 TFLOPS<\/li>\n
    • M\u1ee9c \u0111\u1ed9 ch\u00ednh x\u00e1c (FP16\/FP32): 65 FP16 TFLOPS<\/li>\n
    • T\u1ef7 l\u1ec7 INT8 : 130 INT8 TOPS<\/li>\n
    • T\u1ef7 l\u1ec7 INT4 : 260 INT4 TOPS<\/li>\n<\/ul>\n<\/ul>","protected":false},"featured_media":11464,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"pwb-brand":[],"product_cat":[2914],"product_tag":[25],"class_list":{"0":"post-11463","1":"product","2":"type-product","3":"status-publish","4":"has-post-thumbnail","6":"product_cat-vga-card-man-hinh","7":"product_tag-man","9":"first","10":"instock","11":"shipping-taxable","12":"product-type-simple"},"brands":[],"_links":{"self":[{"href":"https:\/\/serverhot.vn\/bn\/wp-json\/wp\/v2\/product\/11463","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/serverhot.vn\/bn\/wp-json\/wp\/v2\/product"}],"about":[{"href":"https:\/\/serverhot.vn\/bn\/wp-json\/wp\/v2\/types\/product"}],"replies":[{"embeddable":true,"href":"https:\/\/serverhot.vn\/bn\/wp-json\/wp\/v2\/comments?post=11463"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/serverhot.vn\/bn\/wp-json\/wp\/v2\/media\/11464"}],"wp:attachment":[{"href":"https:\/\/serverhot.vn\/bn\/wp-json\/wp\/v2\/media?parent=11463"}],"wp:term":[{"taxonomy":"pwb-brand","embeddable":true,"href":"https:\/\/serverhot.vn\/bn\/wp-json\/wp\/v2\/pwb-brand?post=11463"},{"taxonomy":"product_cat","embeddable":true,"href":"https:\/\/serverhot.vn\/bn\/wp-json\/wp\/v2\/product_cat?post=11463"},{"taxonomy":"product_tag","embeddable":true,"href":"https:\/\/serverhot.vn\/bn\/wp-json\/wp\/v2\/product_tag?post=11463"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}