{"title":"Carte graphique Nvidia","description":"\u003ch1 class=\"banner_banner-heading__c3iMj\"\u003eServeurs ou Stations de travail\u003c\/h1\u003e\n\u003cp class=\"banner_subHeading__UHh16\"\u003eSolutions de bureau et pour ordinateur portable conçues pour accélérer l’IA professionnelle, le graphisme, le rendu et les charges de travail de calcul.\u003c\/p\u003e","products":[{"product_id":"pny-rtx-pro-5000-blackwell-48gb-graphics-cards","title":"Cartes graphiques PNY RTX PRO 5000 Blackwell 48 Go","description":"\u003cul class=\"nv-list\"\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eGPU NVIDIA : \u003c\/strong\u003eRTX PRO 5000 Blackwell\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eFormat : \u003c\/strong\u003e4,4\" H x 10,5\" L, double emplacement\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eSolution thermique : \u003c\/strong\u003eActive\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eTaille de la mémoire GPU : \u003c\/strong\u003e48 Go GDDR7 avec ECC\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\n\u003c\/ul\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":48448775356641,"sku":null,"price":5999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/files\/PNYRTXPRO5000Blackwell.jpg?v=1780712129"},{"product_id":"video-graphics-card-pny-rtx-pro-5000-72gb-blackwell","title":"Carte graphique vidéo PNY RTX PRO 5000 72 Go Blackwell","description":"\u003cul class=\"nv-list\"\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eGPU NVIDIA : \u003c\/strong\u003eRTX PRO 5000 Blackwell\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eFormat : \u003c\/strong\u003e4,4” (H) x 10,5” (L) Double emplacement\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eSolution thermique : \u003c\/strong\u003eActive\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eMémoire GPU : \u003c\/strong\u003e72 Go GDDR7 avec ECC\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\n\u003c\/ul\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":48448775815393,"sku":null,"price":6199.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/files\/PNYRTXPRO500072GBBlackwell.jpg?v=1780712338"},{"product_id":"pny-rtx-pro-6000-96-gb-gddr7-blackwell-workstation-edition-nvidia-graphics-card-gpu","title":"PNY RTX PRO 6000 96 Go GDDR7 Blackwell Workstation Edition Carte graphique GPU Nvidia","description":"\u003cdiv class=\"nv-teaser-header\"\u003e\n\n\u003ch3 class=\"card_card-text__DOJJ_\"\u003ePNY RTX PRO 6000 Blackwell Workstation Edition\u003c\/h3\u003e\n\n\n\u003c\/div\u003e\n\u003cdiv class=\"nv-teaser-body\"\u003e\n\n\u003cdiv class=\"card_productInfo__aez_k undefined undefined\"\u003e\n\n\u003cul class=\"nv-list\"\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eGPU NVIDIA : \u003c\/strong\u003eRTX PRO 6000 Blackwell\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eFacteur de forme : \u003c\/strong\u003e13,7 cm H x 30,5 cm L, Double emplacement\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eSolution thermique : \u003c\/strong\u003eDouble flux traversant\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eTaille mémoire GPU : \u003c\/strong\u003e96 Go GDDR7 avec ECC\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\n\u003c\/ul\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":48448776143073,"sku":null,"price":17999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/files\/PNYRTXPRO6000BlackwellWorkstationEdition.png?v=1780712481"},{"product_id":"pny-rtx-pro-4500-blackwell-workstation-edition-nvidia-graphics-cards-gpu","title":"PNY RTX PRO 4500 Blackwell Workstation Edition Cartes Graphiques Nvidia GPU","description":"\u003cdiv class=\"nv-teaser-header\"\u003e\n\n\u003ch3 class=\"card_card-text__DOJJ_\"\u003ePNY RTX PRO 4500 Blackwell Workstation Edition\u003c\/h3\u003e\n\n\n\u003c\/div\u003e\n\u003cdiv class=\"nv-teaser-body\"\u003e\n\n\u003cdiv class=\"card_productInfo__aez_k undefined undefined\"\u003e\n\n\u003cul class=\"nv-list\"\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eGPU NVIDIA : \u003c\/strong\u003eRTX PRO 4500 Blackwell\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eFacteur de forme : \u003c\/strong\u003e4,4” H x 10,5” L Double slot\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eSolution thermique : \u003c\/strong\u003eActive\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eTaille de la mémoire GPU : \u003c\/strong\u003e32 Go GDDR7 avec ECC\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\n\u003c\/ul\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":48448777060577,"sku":null,"price":3999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/files\/PNYRTXPRO4500BlackwellWorkstationEdition.png?v=1780712836"},{"product_id":"nvidia-graphics-cards-gpu-pny-rtx-pro-4000-blackwell-24-gb-gddr7","title":"Cartes graphiques Nvidia GPU PNY RTX PRO 4000 Blackwell 24 Go GDDR7","description":"\u003cdiv class=\"nv-teaser-header\"\u003e\n\n\u003ch3 class=\"card_card-text__DOJJ_\"\u003ePNY RTX PRO 4000 Blackwell\u003c\/h3\u003e\n\n\n\u003c\/div\u003e\n\u003cdiv class=\"nv-teaser-body\"\u003e\n\n\u003cdiv class=\"card_productInfo__aez_k undefined undefined\"\u003e\n\n\u003cul class=\"nv-list\"\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eGPU NVIDIA : \u003c\/strong\u003eRTX PRO 4000 Blackwell\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eFacteur de forme : \u003c\/strong\u003e4,4\" H x 9,5\" L, emplacement unique\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eSolution thermique : \u003c\/strong\u003eActive\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eTaille mémoire GPU : \u003c\/strong\u003e24 Go GDDR7 avec ECC\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\n\u003c\/ul\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":48448777257185,"sku":null,"price":2999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/files\/PNYRTXPRO4000Blackwell.png?v=1780712973"},{"product_id":"nvidia-graphics-cards-gpu-pny-rtx-pro-2000-blackwell-16-gb-gddr7","title":"Cartes graphiques Nvidia GPU PNY RTX PRO 2000 Blackwell 16 Go GDDR7","description":"\u003cdiv class=\"nv-teaser-header\"\u003e\n\n\u003ch3 class=\"card_card-text__DOJJ_\"\u003ePNY RTX PRO 2000 Blackwell\u003c\/h3\u003e\n\n\n\u003c\/div\u003e\n\u003cdiv class=\"nv-teaser-body\"\u003e\n\n\u003cdiv class=\"card_productInfo__aez_k undefined undefined\"\u003e\n\n\u003cul class=\"nv-list\"\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eGPU NVIDIA : \u003c\/strong\u003eRTX PRO 2000 Blackwell\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eFacteur de forme : \u003c\/strong\u003e2,7” H x 6,6” L Double emplacement\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eSolution thermique : \u003c\/strong\u003eActive\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eTaille de la mémoire GPU : \u003c\/strong\u003e16 Go GDDR7\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\n\u003c\/ul\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":48448779419873,"sku":null,"price":999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/files\/PNYRTXPRO2000Blackwell.png?v=1780713187"},{"product_id":"nvidia-graphics-cards-gpu-nvidia-rtx-pro-6000-blackwell-workstation-edition-96gb-gddr7","title":"Cartes graphiques Nvidia GPU NVIDIA RTX PRO 6000 Blackwell Workstation Edition 96 Go GDDR7","description":"\u003cp\u003e\u003cmeta charset=\"utf-8\"\u003e\u003cspan\u003eLa NVIDIA RTX PRO™ 6000 Blackwell Workstation Edition est le GPU de station de travail le plus puissant et redéfinit les performances pour les professionnels. Grâce à une puissance d’IA inégalée, abordez les modèles d’IA les plus avancés et les workflows créatifs les plus exigeants. Basée sur l’architecture NVIDIA Blackwell et équipée de 96 Go de mémoire GDDR7 ultra-rapide, elle vous offre une vitesse et une efficacité sans précédent.\u003c\/span\u003e\u003c\/p\u003e\n\u003cdiv class=\"nv-teaser-header\"\u003e\n\n\u003ch3 class=\"card_card-text__DOJJ_\"\u003eNVIDIA RTX PRO 6000 Blackwell Workstation Edition\u003c\/h3\u003e\n\n\n\u003c\/div\u003e\n\u003cdiv class=\"nv-teaser-body\"\u003e\n\n\u003cdiv class=\"card_productInfo__aez_k undefined undefined\"\u003e\n\n\u003cul class=\"nv-list\"\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eGPU NVIDIA : \u003c\/strong\u003eRTX PRO 6000 Blackwell\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eFacteur de forme : \u003c\/strong\u003e13,7 cm H x 30,5 cm L Double Slot\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eSolution thermique : \u003c\/strong\u003eDouble flux traversant\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eTaille de la mémoire GPU : \u003c\/strong\u003e96 Go GDDR7 avec ECC\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\n\u003c\/ul\u003e\n\n\u003cp\u003e \u003c\/p\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":48448785219809,"sku":null,"price":19999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/files\/OIP-C.webp?v=1780714414"},{"product_id":"nvidia-graphics-cards-gpu-nvidia-rtx-pro-6000-96gb-blackwell-max-q-workstation-edition","title":"Cartes graphiques Nvidia GPU NVIDIA RTX PRO 6000 96 Go Blackwell Max-Q Workstation Edition","description":"\u003cul class=\"nv-list\"\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eGPU NVIDIA : \u003c\/strong\u003eRTX PRO 6000 Blackwell Max-Q\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eFormat : \u003c\/strong\u003e4,4” H x 10,5” L, double emplacement\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eSolution thermique : \u003c\/strong\u003eActive\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eTaille mémoire du GPU : \u003c\/strong\u003e96 Go GDDR7 avec ECC\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\n\u003c\/ul\u003e\n\u003cdiv class=\"productTools-description\"\u003e\n\n\u003cp\u003eL’édition Workstation NVIDIA RTX PRO™ 6000 Blackwell Max-Q est le GPU de station de travail par excellence pour l’informatique évolutive. Avec 96 Go de mémoire GDDR7, abordez des ensembles de données massifs, effectuez des simulations complexes et exécutez des applications optimisées par l’IA avec des performances et une précision inégalées. Idéal pour les applications stratégiques, il offre évolutivité, fiabilité et innovation pour la science des données, l’IA et la visualisation.\u003c\/p\u003e\n\n\u003cp\u003e \u003c\/p\u003e\n\n\n\u003c\/div\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":48448790429921,"sku":null,"price":19999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/files\/NVIDIARTXPRO6000BlackwellMax-QWorkstationEdition-1.webp?v=1780715166"},{"product_id":"nvidia-graphics-cards-gpu-nvidia-rtx-pro-4500-blackwell-workstation-edition-32gb","title":"Cartes graphiques Nvidia GPU NVIDIA RTX PRO 4500 Blackwell Workstation Edition 32 Go","description":"\u003cul class=\"nv-list\"\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eGPU NVIDIA : \u003c\/strong\u003eRTX PRO 4500 Blackwell\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eFormat : \u003c\/strong\u003e4,4\" H x 10,5\" L, double slot\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eSolution thermique : \u003c\/strong\u003eActive\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eTaille de la mémoire GPU : \u003c\/strong\u003e32 Go GDDR7 avec ECC\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\n\u003c\/ul\u003e\n\u003cp\u003e \u003c\/p\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":48448911147233,"sku":null,"price":3899.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/files\/NVIDIARTXPRO4500BlackwellWorkstationEdition.png?v=1780726326"},{"product_id":"nvidia-graphics-cards-gpu-nvidia-rtx-6000-ada-generation-48gb","title":"Cartes graphiques Nvidia GPU NVIDIA RTX 6000 Ada Generation 48GB","description":"\u003cul class=\"nv-list\"\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eGPU NVIDIA : \u003c\/strong\u003eRTX 6000\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eFormat : \u003c\/strong\u003e4,4\" (H) x 10,5\" (L), double emplacement\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eSolution thermique : \u003c\/strong\u003eVentilateur actif (type « Blower »)\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eMémoire GPU : \u003c\/strong\u003e48 Go GDDR6 avec ECC\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\n\u003c\/ul\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":48448911474913,"sku":null,"price":8999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/files\/NVIDIARTX6000AdaGeneration.png?v=1780726473"},{"product_id":"nvidia-graphics-cards-gpu-nvidia-rtx-5000-ada-generation-32gb-gddr6","title":"Cartes graphiques Nvidia GPU NVIDIA RTX 5000 Ada Generation 32 Go GDDR6","description":"\u003cul class=\"nv-list\"\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eGPU NVIDIA : \u003c\/strong\u003eRTX 5000\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eFormat : \u003c\/strong\u003e4,4\" (H) x 10,5\" (L) x double slot\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eSolution thermique : \u003c\/strong\u003eVentilateur actif (Blower)\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eTaille mémoire du GPU : \u003c\/strong\u003e32 Go GDDR6 avec ECC\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\n\u003c\/ul\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":48448911605985,"sku":null,"price":4999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/files\/NVIDIARTX5000AdaGeneration.png?v=1780726591"},{"product_id":"nvidia-graphics-cards-gpu-nvidia-rtx-4500-ada-generation-24-gb-gddr6","title":"Cartes graphiques Nvidia GPU NVIDIA RTX 4500 Ada Generation 24 Go GDDR6","description":"\u003cul class=\"nv-list\"\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eGPU NVIDIA : \u003c\/strong\u003eRTX 4500\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eFacteur de forme : \u003c\/strong\u003e4,4\" (H) x 10,5\"(L) Double emplacement\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eSolution thermique : \u003c\/strong\u003eVentilateur actif à turbine\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eTaille de la mémoire GPU : \u003c\/strong\u003e24 Go GDDR6 avec ECC\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\n\u003c\/ul\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":48448926843105,"sku":null,"price":2599.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/files\/NVIDIARTX4500AdaGeneration.png?v=1780727046"},{"product_id":"nvidia-graphics-cards-gpu-nvidia-rtx-4000-ada-generation-20-gb-gddr6-with-ecc","title":"Cartes graphiques Nvidia GPU NVIDIA RTX 4000 Ada Generation 20 Go GDDR6 avec ECC","description":"\u003cul class=\"nv-list\"\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eGPU NVIDIA :\u003c\/strong\u003e RTX 4000\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eFormat :\u003c\/strong\u003e 4,4\" (H) x 9,5\" (L) emplacement simple\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eSolution thermique :\u003c\/strong\u003e Ventilateur actif\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eTaille mémoire du GPU :\u003c\/strong\u003e 20 Go GDDR6 avec ECC\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\n\u003c\/ul\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":48448934936801,"sku":null,"price":1399.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/files\/NVIDIARTX4000AdaGeneration.png?v=1780727198"},{"product_id":"nvidia-graphics-cards-gpu-nvidia-rtx-4000-sff-ada-generation-20gb-gddr6","title":"Cartes Graphiques Nvidia GPU NVIDIA RTX 4000 SFF Ada Generation 20 Go GDDR6","description":"\u003cul class=\"nv-list\"\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eGPU NVIDIA : \u003c\/strong\u003eRTX 4000\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eFormat : \u003c\/strong\u003e2,7” H x 6,6” L Double Slot\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eSolution thermique : \u003c\/strong\u003eVentirad actif\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eTaille de la mémoire GPU : \u003c\/strong\u003e20 Go GDDR6 avec ECC\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\n\u003c\/ul\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":48448950894817,"sku":null,"price":1599.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/files\/NVIDIARTX4000SFFAdaGeneration.png?v=1780727319"},{"product_id":"nvidia-graphics-cards-gpu-nvidia-rtx-2000-ada-generation-16-gb-gddr6-with-ecc","title":"Cartes graphiques Nvidia GPU NVIDIA RTX 2000 Ada Generation 16 Go GDDR6 avec ECC","description":"\u003cul class=\"nv-list\"\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eFormat: \u003c\/strong\u003e2,7\" (H) x 6,6\" (L), double emplacement, demi-hauteur\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eSolution thermique: \u003c\/strong\u003eVentilateur actif (Blower)\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\u003cli\u003e\n\n\u003cdiv\u003e\n\n\u003cstrong\u003eTaille mémoire GPU: \u003c\/strong\u003e16 Go GDDR6 avec ECC\u003c\/div\u003e\n\n\n\u003c\/li\u003e\n\n\n\u003c\/ul\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":48448980680929,"sku":null,"price":799.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/files\/NVIDIARTX4000SFFAdaGeneration.png?v=1780727319"},{"product_id":"nvidia-graphics-cards-gpu-nvidia-h200-nvl-141gb","title":"Cartes graphiques Nvidia GPU NVIDIA H200 NVL 141 Go","description":"\u003cp\u003e\u003cmeta charset=\"utf-8\"\u003e\u003cspan\u003eLe NVIDIA H200 NVL suralimente l'IA générative et les charges de travail de calcul haute performance (HPC) avec des performances et des capacités de mémoire révolutionnaires. Premier GPU doté de la HBM3e, la mémoire plus grande et plus rapide du H200 accélère l'IA générative et les grands modèles linguistiques (LLM), tout en faisant progresser le calcul scientifique pour les charges de travail HPC.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cmeta charset=\"utf-utf-8\"\u003eFonctionnalités clés\u003c\/p\u003e\n\u003cp\u003e \u0026gt; 141 Go de mémoire GPU HBM3e\u003c\/p\u003e\n\u003cp\u003e\u0026gt; 4 To\/s de bande passante mémoire\u003c\/p\u003e\n\u003cp\u003e\u0026gt; 4 pétaFLOPS de performances FPS\u003c\/p\u003e\n\u003cp\u003e\u0026gt; 2X performances d'inférence LLM\u003c\/p\u003e\n\u003cp\u003e\u0026gt; 110X performances HPC\u003c\/p\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":48450452586721,"sku":null,"price":39999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/files\/NVIDIATESLAH10080GPCIE_2.jpg?v=1780745552"},{"product_id":"nvidia-graphics-cards-gpu-nvidia-rtx-pro-6000-blackwell-server-edition-96gb","title":"Cartes graphiques Nvidia GPU NVIDIA RTX PRO 6000 Blackwell Server Edition 96GB","description":"\u003cp\u003e\u003cmeta charset=\"utf-8\"\u003e\u003cspan\u003eLe serveur NVIDIA RTX PRO™ 6000 Blackwell Server Edition est un puissant GPU pour centres de données destiné à l'IA et au calcul visuel. Il accélère les charges de travail exigeantes des entreprises, y compris l'IA, le calcul scientifique, les graphiques et les applications vidéo.\u003c\/span\u003e\u003c\/p\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":48450645459169,"sku":null,"price":12999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/files\/NVIDIARTXPRO6000BlackwellServerEdition.png?v=1780755090"},{"product_id":"nvidia-l4-tensor-core-video-cards-graohics-cards-gpu-memory-24gb-gpu-memory-bandwidth-300-gb-s-1","title":"NVIDIA L4 Tensor Core  Video Cards Graohics Cards GPU Memory 24GB GPU Memory Bandwidth 300 GB\/s","description":"\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"292\" style=\"border-collapse: collapse; width: 219pt;\"\u003e\n\u003ccolgroup\u003e\n\u003ccol width=\"292\" style=\"mso-width-source: userset; mso-width-alt: 9344; width: 219pt;\"\u003e \u003c\/colgroup\u003e\n\u003ctbody\u003e\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\u003ctd height=\"20\" class=\"xl65\" width=\"292\" style=\"height: 15.0pt; width: 219pt;\"\u003eSpecifications\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\u003ctd height=\"20\" class=\"xl65\" width=\"292\" style=\"height: 15.0pt; width: 219pt;\"\u003eFP32 30.3 teraFLOPs\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\u003ctd height=\"20\" class=\"xl65\" width=\"292\" style=\"height: 15.0pt; width: 219pt;\"\u003eTF32 Tensor Core 120 teraFLOPS*\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\u003ctd height=\"20\" class=\"xl65\" width=\"292\" style=\"height: 15.0pt; width: 219pt;\"\u003eFP16 Tensor Core 242 teraFLOPS*\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\u003ctd height=\"20\" class=\"xl65\" width=\"292\" style=\"height: 15.0pt; width: 219pt;\"\u003eBFLOAT16 Tensor Core 242 teraFLOPS*\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\u003ctd height=\"20\" class=\"xl65\" width=\"292\" style=\"height: 15.0pt; width: 219pt;\"\u003eFP8 Tensor Core 485 teraFLOPs*\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\u003ctd height=\"20\" class=\"xl65\" width=\"292\" style=\"height: 15.0pt; width: 219pt;\"\u003eINT8 Tensor Core 485 TOPs*\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\u003ctd height=\"20\" class=\"xl65\" width=\"292\" style=\"height: 15.0pt; width: 219pt;\"\u003eGPU Memory 24GB\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\u003ctd height=\"20\" class=\"xl65\" width=\"292\" style=\"height: 15.0pt; width: 219pt;\"\u003eGPU Memory Bandwidth 300 GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\u003ctd height=\"20\" class=\"xl65\" width=\"292\" style=\"height: 15.0pt; width: 219pt;\"\u003eNVENC | NVDEC | JPEG Decoders 2 | 4 | 4\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\u003ctd height=\"20\" class=\"xl65\" width=\"292\" style=\"height: 15.0pt; width: 219pt;\"\u003eMax Thermal Design Power (TDP) 72W\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\u003ctd height=\"20\" class=\"xl65\" width=\"292\" style=\"height: 15.0pt; width: 219pt;\"\u003eForm Factor 1-slot low-profile, PCIe\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\u003ctd height=\"20\" class=\"xl65\" width=\"292\" style=\"height: 15.0pt; width: 219pt;\"\u003eInterconnect PCIe Gen4 x16 64GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\u003ctd height=\"20\" class=\"xl65\" width=\"292\" style=\"height: 15.0pt; width: 219pt;\"\u003eServer Options Partner and NVIDIA-Certified Systems with 1‒8 GPUs\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\u003ctd height=\"20\" class=\"xl65\" width=\"292\" style=\"height: 15.0pt; width: 219pt;\"\u003e*Shown with sparsity. Specifications 1\/2 lower without sparsity.\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003cp\u003eAccelerate Video, AI, and Graphics Workloads The NVIDIA Ada Lovelace L4 Tensor Core GPU delivers universal acceleration and energy efficiency for video, AI, virtualized desktop, and graphics applications in the enterprise, in the cloud, and at the edge. With NVIDIA’s AI platform and full-stack approach, L4 is optimized for inference at scale for a broad range of AI applications, including recommendations, voice-based AI avatar assistants, generative AI, visual search, and contact center automation to deliver the best personalized experiences. As the most efficient NVIDIA accelerator for mainstream use, servers equipped with L4 power up to 120X higher AI video performance over CPU solutions and 2.5X higher generative AI performance, as well as over 4X higher graphics performance than the previous GPU generation. NVIDIA L4’s versatility and energy-efficient, single-slot, low-profile form factor make it ideal for global deployments, including edge locations.\u003c\/p\u003e\n\u003cp\u003eSpecifications FP32 30.3 teraFLOPs TF32 Tensor Core 120 teraFLOPS* FP16 Tensor Core 242 teraFLOPS* BFLOAT16 Tensor Core 242 teraFLOPS* FP8 Tensor Core 485 teraFLOPs* INT8 Tensor Core 485 TOPs* GPU Memory 24GB GPU Memory Bandwidth 300 GB\/s NVENC | NVDEC | JPEG Decoders 2 | 4 | 4 Max Thermal Design Power (TDP) 72W Form Factor 1-slot low-profile, PCIe Interconnect PCIe Gen4 x16 64GB\/s Server Options Partner and NVIDIA-Certified Systems with 1‒8 GPUs\u003c\/p\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":48951164076257,"sku":null,"price":5999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/files\/teslaL424GB_1.jpg?v=1787135638"},{"product_id":"nvidia-l20-48gb-pcie-gpu-graphics-card-vidoe-cards","title":"NVIDIA L20 48GB PCIe GPU Graphics Card Vidoe cards","description":"\u003cp\u003e\u003cmeta charset=\"utf-8\"\u003e\u003cspan\u003eThe L20 is a professional graphics card by NVIDIA. Built on the 5 nm process, and based on the AD102 graphics processor, in its AD102 variant, the card supports DirectX 12 Ultimate. The AD102 graphics processor is a large chip with a die area of 609 mm² and 76,300 million transistors. Unlike the fully unlocked TITAN Ada, which uses the same GPU but has all 18432 shaders enabled, NVIDIA has disabled some shading units on the L20 to reach the product's target shader count. It features 11776 shading units, 368 texture mapping units, and 128 ROPs. Also included are 368 Tensor Cores which help improve the speed of machine learning applications with support for the INT4, INT8 and FP8 data formats. The card also has 92 raytracing acceleration cores. NVIDIA has paired 48 GB GDDR6 memory with the L20, which are connected using a 384-bit memory interface. The GPU is operating at a frequency of 1440 MHz, which can be boosted up to 2520 MHz, memory is running at 2250 MHz (18 Gbps effective).\u003c\/span\u003e\u003cbr\u003e\u003cspan\u003eBeing a dual-slot card, the NVIDIA L20 draws power from 1x 16-pin power connector, with power draw rated at 275 W maximum. Display outputs include: 4x DisplayPort 1.4a. L20 is connected to the rest of the system using a PCI-Express 4.0 x16 interface. The card dual-slot cooling solution.\u003c\/span\u003e\u003c\/p\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":48951226204385,"sku":null,"price":6999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/files\/TeslaL4048G_2.jpg?v=1787138345"},{"product_id":"nvidia-l40s-48gb-gddr6-with-ecc-gpu-for-ai-and-graphics-performance-vidoe-card-graphics-cards","title":"Nvidia L40S 48GB GDDR6 with ECC GPU for AI and Graphics Performance Vidoe Card Graphics cards","description":"\u003cdiv\u003e\u003cspan\u003eAccelerate Next-Generation Workloads\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003e\u0026gt;Generative AI\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003e\u003cspan class=\"\"\u003e\u0026gt;LLM inference\u003c\/span\u003e\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003e\u003cspan class=\"\"\u003e\u0026gt;LLM fine-tuning and small-model training\u003c\/span\u003e\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003e\u003cspan class=\"\"\u003e\u0026gt;NVIDIA Omniverse™ Enterprise\u003c\/span\u003e\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003e\u003cspan class=\"\"\u003e\u0026gt;Rendering and 3D graphics\u003c\/span\u003e\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003e\u0026gt;Streaming and video content\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003e\u003c\/span\u003e\u003c\/div\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":48951265919201,"sku":null,"price":13999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/files\/NVIDIATeslaA16_1.jpg?v=1787140360"},{"product_id":"nvidia-tesla-h100-80g-pcie-graphics-cards-video-cards","title":"Nvidia Tesla H100 80G pcie Graphics cards video cards","description":"\u003cp\u003e\u003cmeta charset=\"utf-8\"\u003e\u003cspan\u003eTesla H100 80GB NVIDIA Deep Learning GPU Compute Graphics Card 900-21010-000-000\u003c\/span\u003e\u003c\/p\u003e\n\u003ctable cellpadding=\"0\" cellspacing=\"0\" width=\"840\"\u003e\n\u003cthead\u003e\n\u003ctr class=\"firstRow\"\u003e\n\u003cth\u003eName\u003c\/th\u003e\n\u003cth\u003eH100 SXM\u003c\/th\u003e\n\u003cth\u003eH100 PCIe\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eFP64\u003c\/td\u003e\n\u003ctd\u003e34 teraFLOPS\u003c\/td\u003e\n\u003ctd\u003e26 teraFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFP64 Tensor Core\u003c\/td\u003e\n\u003ctd\u003e67 teraFLOPS\u003c\/td\u003e\n\u003ctd\u003e51 teraFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFP32\u003c\/td\u003e\n\u003ctd\u003e67 teraFLOPS\u003c\/td\u003e\n\u003ctd\u003e51 teraFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTF32 Tensor Core\u003c\/td\u003e\n\u003ctd\u003e989 teraFLOPS*\u003c\/td\u003e\n\u003ctd\u003e756teraFLOPS*\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eBFLOAT16 Tensor Core\u003c\/td\u003e\n\u003ctd\u003e1979 teraFLOPS*\u003c\/td\u003e\n\u003ctd\u003e1,513 teraFLOPS*\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFP16 Tensor Core\u003c\/td\u003e\n\u003ctd\u003e1979 teraFLOPS*\u003c\/td\u003e\n\u003ctd\u003e1,513 teraFLOPS*\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFP8 Tensor Core\u003c\/td\u003e\n\u003ctd\u003e3958 teraFLOPS*\u003c\/td\u003e\n\u003ctd\u003e3026 teraFLOPS*\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eINT8 Tensor Core\u003c\/td\u003e\n\u003ctd\u003e3958 TOPS*\u003c\/td\u003e\n\u003ctd\u003e3026 TOPS*\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU Memory\u003c\/td\u003e\n\u003ctd\u003e80GB\u003c\/td\u003e\n\u003ctd\u003e80GB\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU width\u003c\/td\u003e\n\u003ctd\u003e3.35TB\/s\u003c\/td\u003e\n\u003ctd\u003e2TB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cbr\u003e\u003c\/td\u003e\n\u003ctd\u003e7 NVDEC\u003cbr\u003e7 JPEG\u003c\/td\u003e\n\u003ctd\u003e7 NVDEC\u003cbr\u003e7 JPEG\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTDP\u003c\/td\u003e\n\u003ctd\u003e700W\u003c\/td\u003e\n\u003ctd\u003e300-350 W\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":48951630823649,"sku":null,"price":59999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/files\/NVIDIATESLAH10080GPCIE_1.jpg?v=1787143575"},{"product_id":"nvidia-tesla-h100-80g-8-sxm5-module-graphics-cards-video-cards-gpu-module","title":"Nvidia Tesla H100 80G*8 SXM5 module Graphics cards video cards GPU module","description":"\u003cp\u003e\u003cmeta charset=\"utf-8\"\u003eNvidia Tesla H100 80G*8 SXM5 module Graphics cards video cards GPU module\u003cbr\u003e\u003c\/p\u003e\n\u003ctable cellpadding=\"0\" cellspacing=\"0\" width=\"840\"\u003e\n\u003cthead\u003e\n\u003ctr class=\"firstRow\"\u003e\n\u003cth\u003eName\u003c\/th\u003e\n\u003cth\u003eH100 SXM\u003c\/th\u003e\n\u003cth\u003eH100 PCIe\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eFP64\u003c\/td\u003e\n\u003ctd\u003e34 teraFLOPS\u003c\/td\u003e\n\u003ctd\u003e26 teraFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFP64 Tensor Core\u003c\/td\u003e\n\u003ctd\u003e67 teraFLOPS\u003c\/td\u003e\n\u003ctd\u003e51 teraFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFP32\u003c\/td\u003e\n\u003ctd\u003e67 teraFLOPS\u003c\/td\u003e\n\u003ctd\u003e51 teraFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTF32 Tensor Core\u003c\/td\u003e\n\u003ctd\u003e989 teraFLOPS*\u003c\/td\u003e\n\u003ctd\u003e756teraFLOPS*\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eBFLOAT16 Tensor Core\u003c\/td\u003e\n\u003ctd\u003e1979 teraFLOPS*\u003c\/td\u003e\n\u003ctd\u003e1,513 teraFLOPS*\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFP16 Tensor Core\u003c\/td\u003e\n\u003ctd\u003e1979 teraFLOPS*\u003c\/td\u003e\n\u003ctd\u003e1,513 teraFLOPS*\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFP8 Tensor Core\u003c\/td\u003e\n\u003ctd\u003e3958 teraFLOPS*\u003c\/td\u003e\n\u003ctd\u003e3026 teraFLOPS*\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eINT8 Tensor Core\u003c\/td\u003e\n\u003ctd\u003e3958 TOPS*\u003c\/td\u003e\n\u003ctd\u003e3026 TOPS*\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU Memory\u003c\/td\u003e\n\u003ctd\u003e80GB\u003c\/td\u003e\n\u003ctd\u003e80GB\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU width\u003c\/td\u003e\n\u003ctd\u003e3.35TB\/s\u003c\/td\u003e\n\u003ctd\u003e2TB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cbr\u003e\u003c\/td\u003e\n\u003ctd\u003e7 NVDEC\u003cbr\u003e7 JPEG\u003c\/td\u003e\n\u003ctd\u003e7 NVDEC\u003cbr\u003e7 JPEG\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTDP\u003c\/td\u003e\n\u003ctd\u003e700W\u003c\/td\u003e\n\u003ctd\u003e300-350 W\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":48952015061217,"sku":null,"price":980000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/files\/H100Module.png?v=1787145187"},{"product_id":"nvidia-tesla-h100-80g-1-sxm5-module-graphics-cards-video-cards-gpu-module","title":"Nvidia Tesla H100 80G*1 SXM5 module Graphics cards video cards GPU module","description":"\u003cp\u003e\u003cmeta charset=\"utf-8\"\u003eNvidia Tesla H100 80G SXM5 module Graphics cards video cards GPU module\u003cbr\u003e\u003c\/p\u003e\n\u003ctable cellpadding=\"0\" cellspacing=\"0\" width=\"840\"\u003e\n\u003cthead\u003e\n\u003ctr class=\"firstRow\"\u003e\n\u003cth\u003eName\u003c\/th\u003e\n\u003cth\u003eH100 SXM\u003c\/th\u003e\n\u003cth\u003eH100 PCIe\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eFP64\u003c\/td\u003e\n\u003ctd\u003e34 teraFLOPS\u003c\/td\u003e\n\u003ctd\u003e26 teraFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFP64 Tensor Core\u003c\/td\u003e\n\u003ctd\u003e67 teraFLOPS\u003c\/td\u003e\n\u003ctd\u003e51 teraFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFP32\u003c\/td\u003e\n\u003ctd\u003e67 teraFLOPS\u003c\/td\u003e\n\u003ctd\u003e51 teraFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTF32 Tensor Core\u003c\/td\u003e\n\u003ctd\u003e989 teraFLOPS*\u003c\/td\u003e\n\u003ctd\u003e756teraFLOPS*\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eBFLOAT16 Tensor Core\u003c\/td\u003e\n\u003ctd\u003e1979 teraFLOPS*\u003c\/td\u003e\n\u003ctd\u003e1,513 teraFLOPS*\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFP16 Tensor Core\u003c\/td\u003e\n\u003ctd\u003e1979 teraFLOPS*\u003c\/td\u003e\n\u003ctd\u003e1,513 teraFLOPS*\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFP8 Tensor Core\u003c\/td\u003e\n\u003ctd\u003e3958 teraFLOPS*\u003c\/td\u003e\n\u003ctd\u003e3026 teraFLOPS*\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eINT8 Tensor Core\u003c\/td\u003e\n\u003ctd\u003e3958 TOPS*\u003c\/td\u003e\n\u003ctd\u003e3026 TOPS*\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU Memory\u003c\/td\u003e\n\u003ctd\u003e80GB\u003c\/td\u003e\n\u003ctd\u003e80GB\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU width\u003c\/td\u003e\n\u003ctd\u003e3.35TB\/s\u003c\/td\u003e\n\u003ctd\u003e2TB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cbr\u003e\u003c\/td\u003e\n\u003ctd\u003e7 NVDEC\u003cbr\u003e7 JPEG\u003c\/td\u003e\n\u003ctd\u003e7 NVDEC\u003cbr\u003e7 JPEG\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTDP\u003c\/td\u003e\n\u003ctd\u003e700W\u003c\/td\u003e\n\u003ctd\u003e300-350 W\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":48952018436321,"sku":null,"price":79000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/files\/H100SXM5GPU.png?v=1787145376"},{"product_id":"nvidia-b200-specs-b200-sxm6-hmb3e-192gb-blackwell-gpu-for-ai-2026-video-card","title":"NVIDIA B200 Specs B200 SXM6 HMB3E 192GB Blackwell GPU for AI (2026) Video Card","description":"\u003cp\u003eNVIDIA B200 Specs B200 SXM6 HMB3E 192GB Blackwell GPU for AI (2026) Video Card\u003c\/p\u003e\n\u003ch2 class=\"text-3xl font-semibold mt-12 mb-6\"\u003eB200 Key Specifications\u003c\/h2\u003e\n\u003cdiv class=\"overflow-x-auto my-8\"\u003e\n\u003ctable class=\"min-w-full border-collapse\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth class=\"px-4 py-2 text-left font-semibold\"\u003eSpecification\u003c\/th\u003e\n\u003cth class=\"px-4 py-2 text-left font-semibold\"\u003eB200\u003c\/th\u003e\n\u003cth class=\"px-4 py-2 text-left font-semibold\"\u003eH200 (for reference)\u003c\/th\u003e\n\u003cth class=\"px-4 py-2 text-left font-semibold\"\u003eH100 (for reference)\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd class=\"px-4 py-2\"\u003eArchitecture\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003eBlackwell (GB200)\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003eHopper\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003eHopper\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"px-4 py-2\"\u003eTransistors\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003e208 billion\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003e80 billion\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003e80 billion\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"px-4 py-2\"\u003eMemory\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003e192GB HBM3e\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003e141GB HBM3e\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003e80GB HBM3\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"px-4 py-2\"\u003eMemory Bandwidth\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003eUp to 8 TB\/s\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003e4.8 TB\/s\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003e3.35 TB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"px-4 py-2\"\u003eFP4 Tensor Core\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003eYes (native)\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003eNo\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003eNo\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"px-4 py-2\"\u003eFP8 Tensor Core\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003eYes (2nd gen)\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003eYes\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003eYes\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"px-4 py-2\"\u003eTransformer Engine\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003e2nd generation\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003e1st generation\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003e1st generation\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"px-4 py-2\"\u003eNVLink\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003e5th gen (1.8 TB\/s)\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003e4th gen (900 GB\/s)\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003e4th gen (900 GB\/s)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"px-4 py-2\"\u003eTDP\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003eUp to 1000W\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003eUp to 700W\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003eUp to 700W\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"px-4 py-2\"\u003eManufacturing\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003eTSMC 4NP\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003eTSMC 4N\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003eTSMC 4N\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003c\/div\u003e\n\u003ch2 class=\"text-3xl font-semibold mt-12 mb-6\"\u003eArchitecture: What Blackwell Changes\u003c\/h2\u003e\n\u003ch3 class=\"text-xl font-semibold mt-4 mb-2\"\u003eSecond-Generation Transformer Engine\u003c\/h3\u003e\n\u003cp class=\"my-6 text-lg leading-relaxed\"\u003eThe biggest improvement for AI workloads is the 2nd-gen Transformer Engine with native FP4 support:\u003c\/p\u003e\n\u003cul class=\"text-lg list-disc pl-12\"\u003e\n\u003cli class=\"my-1\"\u003e\n\u003cstrong\u003eFP4 precision\u003c\/strong\u003e\u003cspan\u003e \u003c\/span\u003e— 4-bit floating point for inference. Halves memory usage vs FP8, enabling larger models or higher batch sizes on a single GPU\u003c\/li\u003e\n\u003cli class=\"my-1\"\u003e\n\u003cstrong\u003eDynamic precision management\u003c\/strong\u003e\u003cspan\u003e \u003c\/span\u003e— automatically switches between FP4, FP8, and FP16 based on what each layer needs\u003c\/li\u003e\n\u003cli class=\"my-1\"\u003e\n\u003cstrong\u003eHigher throughput\u003c\/strong\u003e\u003cspan\u003e \u003c\/span\u003e— combined with architectural improvements, NVIDIA claims up to 4x inference performance vs H100\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp class=\"my-6 text-lg leading-relaxed\"\u003eFP4 is particularly impactful for LLM inference. A model that needs 80GB in FP8 on H100 would need only ~40GB in FP4 on B200, leaving 150GB+ free for KV cache and batching.\u003c\/p\u003e\n\u003ch3 class=\"text-xl font-semibold mt-4 mb-2\"\u003e192GB HBM3e Memory\u003c\/h3\u003e\n\u003cp class=\"my-6 text-lg leading-relaxed\"\u003eThe memory jump is massive:\u003c\/p\u003e\n\u003cdiv class=\"overflow-x-auto my-8\"\u003e\n\u003ctable class=\"min-w-full border-collapse\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth class=\"px-4 py-2 text-left font-semibold\"\u003eGPU\u003c\/th\u003e\n\u003cth class=\"px-4 py-2 text-left font-semibold\"\u003eMemory\u003c\/th\u003e\n\u003cth class=\"px-4 py-2 text-left font-semibold\"\u003eMemory Bandwidth\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd class=\"px-4 py-2\"\u003eH100\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003e80GB HBM3\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003e3.35 TB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"px-4 py-2\"\u003eH200\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003e141GB HBM3e\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003e4.8 TB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"px-4 py-2\"\u003eB200\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003e192GB HBM3e\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003eUp to 8 TB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003c\/div\u003e\n\u003cp class=\"my-6 text-lg leading-relaxed\"\u003e192GB means:\u003c\/p\u003e\n\u003cul class=\"text-lg list-disc pl-12\"\u003e\n\u003cli class=\"my-1\"\u003e\n\u003cstrong\u003eLlama 70B in FP16\u003c\/strong\u003e\u003cspan\u003e \u003c\/span\u003efits on a single GPU (140GB) with 52GB to spare for KV cache\u003c\/li\u003e\n\u003cli class=\"my-1\"\u003e\n\u003cstrong\u003eLlama 70B in FP8\u003c\/strong\u003e\u003cspan\u003e \u003c\/span\u003eneeds only ~70GB, leaving 122GB for massive batch sizes\u003c\/li\u003e\n\u003cli class=\"my-1\"\u003e\n\u003cstrong\u003eLlama 405B in FP4\u003c\/strong\u003e\u003cspan\u003e \u003c\/span\u003ecould potentially fit on 2 B200s\u003c\/li\u003e\n\u003cli class=\"my-1\"\u003e\n\u003cstrong\u003eMultiple models\u003c\/strong\u003e\u003cspan\u003e \u003c\/span\u003eserved simultaneously from a single GPU\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch3 class=\"text-xl font-semibold mt-4 mb-2\"\u003eNVLink 5th Generation\u003c\/h3\u003e\n\u003cp class=\"my-6 text-lg leading-relaxed\"\u003eNVLink bandwidth doubles from 900 GB\/s (H100\/H200) to 1.8 TB\/s per GPU. For multi-GPU training, this means:\u003c\/p\u003e\n\u003cul class=\"text-lg list-disc pl-12\"\u003e\n\u003cli class=\"my-1\"\u003eFaster gradient synchronization during distributed training\u003c\/li\u003e\n\u003cli class=\"my-1\"\u003eMore efficient tensor parallelism for large model inference\u003c\/li\u003e\n\u003cli class=\"my-1\"\u003eBetter scaling efficiency when using 4-8 GPUs per node\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch3 class=\"text-xl font-semibold mt-4 mb-2\"\u003eGB200 and NVL72\u003c\/h3\u003e\n\u003cp class=\"my-6 text-lg leading-relaxed\"\u003eNVIDIA is also shipping the B200 in pre-configured rack-scale systems:\u003c\/p\u003e\n\u003cul class=\"text-lg list-disc pl-12\"\u003e\n\u003cli class=\"my-1\"\u003e\n\u003cstrong\u003eGB200\u003c\/strong\u003e\u003cspan\u003e \u003c\/span\u003e— a compute module with 2 B200 GPUs + 1 Grace CPU, connected via NVLink\u003c\/li\u003e\n\u003cli class=\"my-1\"\u003e\n\u003cstrong\u003eGB200 NVL72\u003c\/strong\u003e\u003cspan\u003e \u003c\/span\u003e— a full rack with 36 Grace CPUs and 72 B200 GPUs interconnected via NVLink, delivering 720 petaFLOPS of FP4 compute\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp class=\"my-6 text-lg leading-relaxed\"\u003eThese are designed for large-scale training and inference at the datacenter level.\u003c\/p\u003e\n\u003ch2 class=\"text-3xl font-semibold mt-12 mb-6\"\u003eB200 vs H100 vs H200\u003c\/h2\u003e\n\u003ch3 class=\"text-xl font-semibold mt-4 mb-2\"\u003eFor LLM Inference\u003c\/h3\u003e\n\u003cdiv class=\"overflow-x-auto my-8\"\u003e\n\u003ctable class=\"min-w-full border-collapse\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth class=\"px-4 py-2 text-left font-semibold\"\u003eMetric\u003c\/th\u003e\n\u003cth class=\"px-4 py-2 text-left font-semibold\"\u003eB200\u003c\/th\u003e\n\u003cth class=\"px-4 py-2 text-left font-semibold\"\u003eH200\u003c\/th\u003e\n\u003cth class=\"px-4 py-2 text-left font-semibold\"\u003eH100\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd class=\"px-4 py-2\"\u003eLlama 70B (FP8) tokens\/sec\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003e~4x H100*\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003e~1.9x H100\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003e1x (baseline)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"px-4 py-2\"\u003eMemory for Llama 70B FP8\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003e70GB (122GB free)\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003e70GB (71GB free)\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003e70GB (10GB free)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"px-4 py-2\"\u003eMemory for Llama 70B FP4\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003e~35GB (157GB free)\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003eN\/A (no FP4)\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003eN\/A (no FP4)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"px-4 py-2\"\u003eMax batch size (70B FP8)\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003eVery large\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003eModerate\u003c\/td\u003e\n\u003ctd class=\"px-4 py-2\"\u003eSmall\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003c\/div\u003e\n\u003cp class=\"my-6 text-lg leading-relaxed\"\u003e*NVIDIA published claims. Real-world performance will vary by implementation and workload.\u003c\/p\u003e\n\u003cp class=\"my-6 text-lg leading-relaxed\"\u003eThe B200's combination of more memory, higher bandwidth, and FP4 support could make single-GPU serving of 70B models practical at scale — something that's tight on H100 and comfortable but not optimal on H200.\u003c\/p\u003e\n\u003ch3 class=\"text-xl font-semibold mt-4 mb-2\"\u003eFor Training\u003c\/h3\u003e\n\u003cp class=\"my-6 text-lg leading-relaxed\"\u003eNVIDIA claims up to 4x training performance on GPT-class models compared to H100, primarily from:\u003c\/p\u003e\n\u003cul class=\"text-lg list-disc pl-12\"\u003e\n\u003cli class=\"my-1\"\u003eHigher Tensor Core throughput\u003c\/li\u003e\n\u003cli class=\"my-1\"\u003eFP8 training improvements (2nd-gen Transformer Engine)\u003c\/li\u003e\n\u003cli class=\"my-1\"\u003e2x NVLink bandwidth for better multi-GPU scaling\u003c\/li\u003e\n\u003cli class=\"my-1\"\u003eMore memory reducing the need for memory optimization techniques\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp class=\"my-6 text-lg leading-relaxed\"\u003eFor large model training, the B200 could reduce training time (and cost) by 3-4x compared to H100, assuming the software stack fully utilizes the new hardware features.\u003c\/p\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":48952407261409,"sku":null,"price":89999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/files\/B200_c3851d04-b413-4993-b640-3d91fc5a5e62.png?v=1787151738"},{"product_id":"nvidia-hgx-b300-gpu-server-graphics-cards-video-cards-2-1-tb-16-nvidia-blackwell-ultra-gpus-270-gb-hbm3e-7-7-tb-s","title":"NVIDIA HGX B300 GPU server Graphics cards Video cards 2.1 TB 16 × NVIDIA Blackwell Ultra GPUs 270 GB HBM3E 7.7 TB\/s","description":"\u003cp\u003eNVIDIA HGX B300 GPU server Graphics cards Video cards 2.1 TB 16 × NVIDIA Blackwell Ultra GPUs 270 GB HBM3E 7.7 TB\/s\u003c\/p\u003e\n\u003ctable cellspacing=\"0\" cellpadding=\"0\" class=\"t1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003e\u003cb\u003eTechnical Specifications                                                                                 HGX B300\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003ctable cellspacing=\"0\" cellpadding=\"0\" class=\"t1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003e \u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003e\u003cmeta charset=\"utf-8\"\u003e\u003cb\u003eGB300 NVL72\u003c\/b\u003e\u003c\/p\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p2\"\u003e\u003cb\u003eBlackwell Ultra GPUs | Grace CPUs\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003e72 | 36\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003e8 | 0\u003c\/p\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p2\"\u003e\u003cb\u003eCPU Cores\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003e2,592 Arm Neoverse V2 Cores\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003e-\u003c\/p\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p2\"\u003e\u003cb\u003eTotal FP4 Tensor Core\u003c\/b\u003e\u003cspan class=\"s1\"\u003e\u003cb\u003e1\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003e1,440 PFLOPS | 1,080 PFLOPS\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003e144 PFLOPS | 108 PFLOPS\u003c\/p\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p2\"\u003e\u003cb\u003eTotal FP8\/FP6 Tensor Core\u003c\/b\u003e\u003cspan class=\"s1\"\u003e\u003cb\u003e2\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003e720 PFLOPS\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003e72 PFLOPS\u003c\/p\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p2\"\u003e\u003cb\u003eTotal Fast Memory\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003e37 TB\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003e2.1 TB\u003c\/p\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p2\"\u003e\u003cb\u003eTotal Memory Bandwidth\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003e576 TB\/s\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003e62 TB\/s\u003c\/p\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p2\"\u003e\u003cb\u003eTotal NVLink Switch Bandwidth\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003e130 TB\/s\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003e14.4 TB\/s\u003c\/p\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003e\u003cb\u003eIndividual Blackwell Ultra GPU Specifications\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p2\"\u003e\u003cb\u003eFP4 Tensor Core\u003c\/b\u003e\u003cspan class=\"s1\"\u003e\u003cb\u003e1\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003e20 PFLOPS | 15 PFLOPS\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003e18 PFLOPS | 14 PFLOPS\u003c\/p\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p2\"\u003e\u003cb\u003eFP8\/FP6 Tensor Core\u003c\/b\u003e\u003cspan class=\"s1\"\u003e\u003cb\u003e2\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003e10 PFLOPS\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003e9 PFLOPS\u003c\/p\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p2\"\u003e\u003cb\u003eINT8 Tensor Core\u003c\/b\u003e\u003cspan class=\"s1\"\u003e\u003cb\u003e2\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003e330 TOPS\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003e307 TOPS\u003c\/p\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p2\"\u003e\u003cb\u003eFP16\/BF16 Tensor Core\u003c\/b\u003e\u003cspan class=\"s1\"\u003e\u003cb\u003e2\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003e5 PFLOPS\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003e4.5 PLFOPS\u003c\/p\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p2\"\u003e\u003cb\u003eTF32 Tensor Core\u003c\/b\u003e\u003cspan class=\"s1\"\u003e\u003cb\u003e2\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003e2.5 PFLOPS\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003e2.2 PFLOPS\u003c\/p\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p2\"\u003e\u003cb\u003eFP32\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003e80 TFLOPS\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003e75 TFLOPS\u003c\/p\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p2\"\u003e\u003cb\u003eFP64\/FP64 Tensor Core\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003e1.3 TFLOPS\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003e1.2 TFLOPS\u003c\/p\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p2\"\u003e\u003cb\u003eGPU Memory | Bandwidth\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003e279 GB HBM3E | 8 TB\/s\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003e270 GB HBM3E | 7.7 TB\/s\u003c\/p\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p2\"\u003e\u003cb\u003eMulti-Instance GPU (MIG)\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003e7\u003c\/p\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p2\"\u003e\u003cb\u003eDecompression Engine\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003eYes\u003c\/p\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p2\"\u003e\u003cb\u003eDecoders\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p3\"\u003e7 NVDEC\u003cspan class=\"s1\"\u003e3\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"p1\"\u003e7 nvJPEG\u003c\/p\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p2\"\u003e\u003cb\u003eMax Thermal Design Power (TDP)\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003eConfigurable up to 1,400 W\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003eConfigurable up to 1,100 W\u003c\/p\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p2\"\u003e\u003cb\u003eInterconnect\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p3\"\u003eFifth-Generation NVLink: 1.8 TB\/s\u003c\/p\u003e\n\u003cp class=\"p1\"\u003ePCIe Gen6: 256 GB\/s\u003c\/p\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p2\"\u003e\u003cb\u003eServer Options\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003eNVIDIA GB300 NVL72 partner and NVIDIA-Certified Systems\u003cspan class=\"s2\"\u003e™ \u003c\/span\u003e\u003c\/p\u003e\n\u003c\/td\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p1\"\u003eNVIDIA HGX B300 partner and NVIDIA-Certified Systems\u003c\/p\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\u003cp class=\"p4\"\u003e1. Specification in Sparse | Dense\u003c\/p\u003e\n\u003cp class=\"p4\"\u003e2. Specification in sparse. Dense is ½ sparse spec shown.\u003c\/p\u003e\n\u003cp class=\"p4\"\u003e3. Supported formats provide these speed-ups over H100 GPUs: 2x H.264, 1.25x HEVC, 1.25x VP9. AV1\u003c\/p\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":48952504844513,"sku":null,"price":1399999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/files\/B300-3.jpg?v=1787149826"},{"product_id":"nvidia-b300-192gb-hbm3e-blackwell-gpu-for-ai-2026-graphics-cards-video-cards-nvidia-blackwell-ultra-gpus-hbm3e","title":"NVIDIA B300 192GB HBM3e Blackwell GPU for AI (2026) Graphics cards Video cards NVIDIA Blackwell Ultra GPUs HBM3E","description":"\u003cp\u003eNVIDIA B300 192GB HBM3e Blackwell GPU for AI (2026) Graphics cards Video cards NVIDIA Blackwell Ultra GPUs HBM3E\u003c\/p\u003e\n\u003cdiv class=\"nv-table aem-GridColumn--default--none aem-GridColumn--laptop--none aem-GridColumn--offset--laptop--1 aem-GridColumn--offset--default--2 aem-GridColumn--offset--phone--0 aem-GridColumn--tablet--12 aem-GridColumn--offset--tablet--0 aem-GridColumn--phone--none aem-GridColumn--phone--12 aem-GridColumn--tablet--none aem-GridColumn aem-GridColumn--default--8 aem-GridColumn--laptop--10\"\u003e\n\u003cdiv\u003e\n\u003cdiv class=\"nv-table-wrapper\"\u003e\n\u003cdiv class=\"nv-table stickyscroll overflow-right allowstickyth sticky-columns-col1\"\u003e\n\u003ctable class=\"alt-row-2 even-columns\" style=\"width: 100%;\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth scope=\"col\" class=\"second-column  even-column odd-row\" style=\"width: 37.5%;\"\u003eHGX B300\u003csup\u003e4\u003c\/sup\u003e\n\u003c\/th\u003e\n\u003cth scope=\"col\" class=\"third-column odd-column odd-row\" style=\"width: 33.75%;\"\u003eHGX B200\u003csup\u003e4\u003c\/sup\u003e\n\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\" class=\"first-column   odd-column even-row\" style=\"width: 37.5%;\"\u003eForm Factor\u003c\/th\u003e\n\u003ctd class=\"even-column even-row second-column\" style=\"width: 33.75%;\"\u003e8x NVIDIA Blackwell Ultra SXM\u003c\/td\u003e\n\u003ctd class=\"odd-column even-row third-column\" style=\"width: 28.0357%;\"\u003e8x NVIDIA Blackwell SXM\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\" class=\"first-column   odd-column odd-row\" style=\"width: 37.5%;\"\u003eFP4 Tensor Core\u003csup\u003e1\u003c\/sup\u003e\n\u003c\/th\u003e\n\u003ctd class=\"even-column odd-row second-column\" style=\"width: 33.75%;\"\u003e144 PFLOPS | 108 PFLOPS\u003c\/td\u003e\n\u003ctd class=\"odd-column odd-row third-column\" style=\"width: 28.0357%;\"\u003e144 PFLOPS | 72 PFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\" class=\"first-column   odd-column even-row\" style=\"width: 37.5%;\"\u003eFP8\/FP6 Tensor Core\u003csup\u003e2\u003c\/sup\u003e\n\u003c\/th\u003e\n\u003ctd class=\"even-column even-row second-column\" style=\"width: 33.75%;\"\u003e72 PFLOPS\u003c\/td\u003e\n\u003ctd class=\"odd-column even-row third-column\" style=\"width: 28.0357%;\"\u003e72 PFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\" class=\"first-column   odd-column odd-row\" style=\"width: 37.5%;\"\u003eINT8 Tensor Core\u003csup\u003e2\u003c\/sup\u003e\n\u003c\/th\u003e\n\u003ctd class=\"even-column odd-row second-column\" style=\"width: 33.75%;\"\u003e3 POPS\u003c\/td\u003e\n\u003ctd class=\"odd-column odd-row third-column\" style=\"width: 28.0357%;\"\u003e72 POPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\" class=\"first-column   odd-column even-row\" style=\"width: 37.5%;\"\u003eFP16\/BF16 Tensor Core\u003csup\u003e2\u003c\/sup\u003e\n\u003c\/th\u003e\n\u003ctd class=\"even-column even-row second-column\" style=\"width: 33.75%;\"\u003e36 PFLOPS\u003c\/td\u003e\n\u003ctd class=\"odd-column even-row third-column\" style=\"width: 28.0357%;\"\u003e36 PFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\" class=\"first-column   odd-column odd-row\" style=\"width: 37.5%;\"\u003eTF32 Tensor Core\u003csup\u003e2\u003c\/sup\u003e\n\u003c\/th\u003e\n\u003ctd class=\"even-column odd-row second-column\" style=\"width: 33.75%;\"\u003e18 PFLOPS\u003c\/td\u003e\n\u003ctd class=\"odd-column odd-row third-column\" style=\"width: 28.0357%;\"\u003e18 PFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\" class=\"first-column   odd-column even-row\" style=\"width: 37.5%;\"\u003eFP32\u003c\/th\u003e\n\u003ctd class=\"even-column even-row second-column\" style=\"width: 33.75%;\"\u003e600 TFLOPS\u003c\/td\u003e\n\u003ctd class=\"odd-column even-row third-column\" style=\"width: 28.0357%;\"\u003e600 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\" class=\"first-column   odd-column odd-row\" style=\"width: 37.5%;\"\u003eFP64\/FP64 Tensor Core\u003c\/th\u003e\n\u003ctd class=\"even-column odd-row second-column\" style=\"width: 33.75%;\"\u003e10 TFLOPS\u003c\/td\u003e\n\u003ctd class=\"odd-column odd-row third-column\" style=\"width: 28.0357%;\"\u003e296 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\" class=\"first-column   odd-column even-row\" style=\"width: 37.5%;\"\u003eTotal Memory\u003c\/th\u003e\n\u003ctd class=\"even-column even-row second-column\" style=\"width: 33.75%;\"\u003e2.1 TB\u003c\/td\u003e\n\u003ctd class=\"odd-column even-row third-column\" style=\"width: 28.0357%;\"\u003e1.4 TB\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\" class=\"first-column   odd-column odd-row\" style=\"width: 37.5%;\"\u003eNVIDIA NVLink\u003c\/th\u003e\n\u003ctd class=\"even-column odd-row second-column\" style=\"width: 33.75%;\"\u003eFifth generation\u003c\/td\u003e\n\u003ctd class=\"odd-column odd-row third-column\" style=\"width: 28.0357%;\"\u003eFifth generation\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\" class=\"first-column   odd-column even-row\" style=\"width: 37.5%;\"\u003eNVIDIA NVLink Switch™\u003c\/th\u003e\n\u003ctd class=\"even-column even-row second-column\" style=\"width: 33.75%;\"\u003eNVLink 5 Switch\u003c\/td\u003e\n\u003ctd class=\"odd-column even-row third-column\" style=\"width: 28.0357%;\"\u003eNVLink 5 Switch\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\" class=\"first-column   odd-column odd-row\" style=\"width: 37.5%;\"\u003eNVLink GPU-to-GPU Bandwidth\u003c\/th\u003e\n\u003ctd class=\"even-column odd-row second-column\" style=\"width: 33.75%;\"\u003e1.8 TB\/s\u003c\/td\u003e\n\u003ctd class=\"odd-column odd-row third-column\" style=\"width: 28.0357%;\"\u003e1.8 TB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\" class=\"first-column   odd-column even-row\" style=\"width: 37.5%;\"\u003eTotal NVLink Bandwidth\u003c\/th\u003e\n\u003ctd class=\"even-column even-row second-column\" style=\"width: 33.75%;\"\u003e14.4 TB\/s\u003c\/td\u003e\n\u003ctd class=\"odd-column even-row third-column\" style=\"width: 28.0357%;\"\u003e14.4 TB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\" class=\"first-column   odd-column odd-row\" style=\"width: 37.5%;\"\u003eNetworking Bandwidth\u003c\/th\u003e\n\u003ctd class=\"even-column odd-row second-column\" style=\"width: 33.75%;\"\u003e1.6 TB\/s\u003c\/td\u003e\n\u003ctd class=\"odd-column odd-row third-column\" style=\"width: 28.0357%;\"\u003e0.8 TB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\" class=\"first-column   odd-column even-row\" style=\"width: 37.5%;\"\u003eAttention Performance\u003csup\u003e3\u003c\/sup\u003e\n\u003c\/th\u003e\n\u003ctd class=\"even-column even-row second-column\" style=\"width: 33.75%;\"\u003e2x\u003c\/td\u003e\n\u003ctd class=\"odd-column even-row third-column\" style=\"width: 28.0357%;\"\u003e1x\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"nv-text text aem-GridColumn--tablet--12 aem-GridColumn--offset--tablet--0 aem-GridColumn--default--none aem-GridColumn--phone--none aem-GridColumn--phone--12 aem-GridColumn--tablet--none aem-GridColumn aem-GridColumn--default--8 aem-GridColumn--offset--default--2 aem-GridColumn--offset--phone--0\"\u003e\n\u003cdiv id=\"nv-text-6188745090\" class=\"general-container-text\"\u003e\n\u003cdiv class=\"text-left lap-text-left tab-text-left mob-text-left\"\u003e\n\u003cdiv class=\"description\"\u003e\n\u003cp\u003e1. Specification in Sparse | Dense\u003cbr\u003e2. Specification in Sparse. Dense is ½ sparse spec shown.\u003cbr\u003e3. vs. NVIDIA Blackwell.\u003cbr\u003e4. HGX B300 and HGX B200 shipping now.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"nv-separator separator aem-GridColumn aem-GridColumn--default--12\"\u003e\u003cbr\u003e\u003c\/div\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":48952570314977,"sku":null,"price":99999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/files\/B300-4.png?v=1787150920"},{"product_id":"nvidia-hgx-b200-180-gb-hbm3e-7-7-tb-s-8x-nvidia-blackwell-ultra-sxm-graphics-cards-video-cards-nvidia-blackwell-ultra-gpus-hbm3e","title":"NVIDIA HGX B200 180 GB HBM3E 7.7 TB\/s 8x NVIDIA Blackwell Ultra SXM Graphics cards Video cards NVIDIA Blackwell Ultra GPUs HBM3E","description":"\u003cp\u003eNVIDIA HGX B200 180 GB HBM3E 7.7 TB\/s 8x NVIDIA Blackwell Ultra SXM Graphics cards Video cards NVIDIA Blackwell Ultra GPUs HBM3E\u003c\/p\u003e\n\u003cdiv class=\"nv-table aem-GridColumn--default--none aem-GridColumn--laptop--none aem-GridColumn--offset--laptop--1 aem-GridColumn--offset--default--2 aem-GridColumn--offset--phone--0 aem-GridColumn--tablet--12 aem-GridColumn--offset--tablet--0 aem-GridColumn--phone--none aem-GridColumn--phone--12 aem-GridColumn--tablet--none aem-GridColumn aem-GridColumn--default--8 aem-GridColumn--laptop--10\"\u003e\n\u003cdiv\u003e\n\u003cdiv class=\"nv-table-wrapper\"\u003e\n\u003cdiv class=\"nv-table stickyscroll overflow-right allowstickyth sticky-columns-col1\"\u003e\n\u003ctable class=\"alt-row-2 even-columns\" style=\"width: 100%;\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth scope=\"col\" class=\"second-column  even-column odd-row\" style=\"width: 37.5%;\"\u003eHGX B300\u003csup\u003e4\u003c\/sup\u003e\n\u003c\/th\u003e\n\u003cth scope=\"col\" class=\"third-column odd-column odd-row\" style=\"width: 33.75%;\"\u003eHGX B200\u003csup\u003e4\u003c\/sup\u003e\n\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\" class=\"first-column   odd-column even-row\" style=\"width: 37.5%;\"\u003eForm Factor\u003c\/th\u003e\n\u003ctd class=\"even-column even-row second-column\" style=\"width: 33.75%;\"\u003e8x NVIDIA Blackwell Ultra SXM\u003c\/td\u003e\n\u003ctd class=\"odd-column even-row third-column\" style=\"width: 28.0357%;\"\u003e8x NVIDIA Blackwell SXM\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\" class=\"first-column   odd-column odd-row\" style=\"width: 37.5%;\"\u003eFP4 Tensor Core\u003csup\u003e1\u003c\/sup\u003e\n\u003c\/th\u003e\n\u003ctd class=\"even-column odd-row second-column\" style=\"width: 33.75%;\"\u003e144 PFLOPS | 108 PFLOPS\u003c\/td\u003e\n\u003ctd class=\"odd-column odd-row third-column\" style=\"width: 28.0357%;\"\u003e144 PFLOPS | 72 PFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\" class=\"first-column   odd-column even-row\" style=\"width: 37.5%;\"\u003eFP8\/FP6 Tensor Core\u003csup\u003e2\u003c\/sup\u003e\n\u003c\/th\u003e\n\u003ctd class=\"even-column even-row second-column\" style=\"width: 33.75%;\"\u003e72 PFLOPS\u003c\/td\u003e\n\u003ctd class=\"odd-column even-row third-column\" style=\"width: 28.0357%;\"\u003e72 PFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\" class=\"first-column   odd-column odd-row\" style=\"width: 37.5%;\"\u003eINT8 Tensor Core\u003csup\u003e2\u003c\/sup\u003e\n\u003c\/th\u003e\n\u003ctd class=\"even-column odd-row second-column\" style=\"width: 33.75%;\"\u003e3 POPS\u003c\/td\u003e\n\u003ctd class=\"odd-column odd-row third-column\" style=\"width: 28.0357%;\"\u003e72 POPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\" class=\"first-column   odd-column even-row\" style=\"width: 37.5%;\"\u003eFP16\/BF16 Tensor Core\u003csup\u003e2\u003c\/sup\u003e\n\u003c\/th\u003e\n\u003ctd class=\"even-column even-row second-column\" style=\"width: 33.75%;\"\u003e36 PFLOPS\u003c\/td\u003e\n\u003ctd class=\"odd-column even-row third-column\" style=\"width: 28.0357%;\"\u003e36 PFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\" class=\"first-column   odd-column odd-row\" style=\"width: 37.5%;\"\u003eTF32 Tensor Core\u003csup\u003e2\u003c\/sup\u003e\n\u003c\/th\u003e\n\u003ctd class=\"even-column odd-row second-column\" style=\"width: 33.75%;\"\u003e18 PFLOPS\u003c\/td\u003e\n\u003ctd class=\"odd-column odd-row third-column\" style=\"width: 28.0357%;\"\u003e18 PFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\" class=\"first-column   odd-column even-row\" style=\"width: 37.5%;\"\u003eFP32\u003c\/th\u003e\n\u003ctd class=\"even-column even-row second-column\" style=\"width: 33.75%;\"\u003e600 TFLOPS\u003c\/td\u003e\n\u003ctd class=\"odd-column even-row third-column\" style=\"width: 28.0357%;\"\u003e600 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\" class=\"first-column   odd-column odd-row\" style=\"width: 37.5%;\"\u003eFP64\/FP64 Tensor Core\u003c\/th\u003e\n\u003ctd class=\"even-column odd-row second-column\" style=\"width: 33.75%;\"\u003e10 TFLOPS\u003c\/td\u003e\n\u003ctd class=\"odd-column odd-row third-column\" style=\"width: 28.0357%;\"\u003e296 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\" class=\"first-column   odd-column even-row\" style=\"width: 37.5%;\"\u003eTotal Memory\u003c\/th\u003e\n\u003ctd class=\"even-column even-row second-column\" style=\"width: 33.75%;\"\u003e2.1 TB\u003c\/td\u003e\n\u003ctd class=\"odd-column even-row third-column\" style=\"width: 28.0357%;\"\u003e1.4 TB\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\" class=\"first-column   odd-column odd-row\" style=\"width: 37.5%;\"\u003eNVIDIA NVLink\u003c\/th\u003e\n\u003ctd class=\"even-column odd-row second-column\" style=\"width: 33.75%;\"\u003eFifth generation\u003c\/td\u003e\n\u003ctd class=\"odd-column odd-row third-column\" style=\"width: 28.0357%;\"\u003eFifth generation\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\" class=\"first-column   odd-column even-row\" style=\"width: 37.5%;\"\u003eNVIDIA NVLink Switch™\u003c\/th\u003e\n\u003ctd class=\"even-column even-row second-column\" style=\"width: 33.75%;\"\u003eNVLink 5 Switch\u003c\/td\u003e\n\u003ctd class=\"odd-column even-row third-column\" style=\"width: 28.0357%;\"\u003eNVLink 5 Switch\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\" class=\"first-column   odd-column odd-row\" style=\"width: 37.5%;\"\u003eNVLink GPU-to-GPU Bandwidth\u003c\/th\u003e\n\u003ctd class=\"even-column odd-row second-column\" style=\"width: 33.75%;\"\u003e1.8 TB\/s\u003c\/td\u003e\n\u003ctd class=\"odd-column odd-row third-column\" style=\"width: 28.0357%;\"\u003e1.8 TB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\" class=\"first-column   odd-column even-row\" style=\"width: 37.5%;\"\u003eTotal NVLink Bandwidth\u003c\/th\u003e\n\u003ctd class=\"even-column even-row second-column\" style=\"width: 33.75%;\"\u003e14.4 TB\/s\u003c\/td\u003e\n\u003ctd class=\"odd-column even-row third-column\" style=\"width: 28.0357%;\"\u003e14.4 TB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\" class=\"first-column   odd-column odd-row\" style=\"width: 37.5%;\"\u003eNetworking Bandwidth\u003c\/th\u003e\n\u003ctd class=\"even-column odd-row second-column\" style=\"width: 33.75%;\"\u003e1.6 TB\/s\u003c\/td\u003e\n\u003ctd class=\"odd-column odd-row third-column\" style=\"width: 28.0357%;\"\u003e0.8 TB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003cth scope=\"row\" class=\"first-column   odd-column even-row\" style=\"width: 37.5%;\"\u003eAttention Performance\u003csup\u003e3\u003c\/sup\u003e\n\u003c\/th\u003e\n\u003ctd class=\"even-column even-row second-column\" style=\"width: 33.75%;\"\u003e2x\u003c\/td\u003e\n\u003ctd class=\"odd-column even-row third-column\" style=\"width: 28.0357%;\"\u003e1x\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"nv-text text aem-GridColumn--tablet--12 aem-GridColumn--offset--tablet--0 aem-GridColumn--default--none aem-GridColumn--phone--none aem-GridColumn--phone--12 aem-GridColumn--tablet--none aem-GridColumn aem-GridColumn--default--8 aem-GridColumn--offset--default--2 aem-GridColumn--offset--phone--0\"\u003e\n\u003cdiv id=\"nv-text-6188745090\" class=\"general-container-text\"\u003e\n\u003cdiv class=\"text-left lap-text-left tab-text-left mob-text-left\"\u003e\n\u003cdiv class=\"description\"\u003e\n\u003cp\u003e1. Specification in Sparse | Dense\u003cbr\u003e2. Specification in Sparse. Dense is ½ sparse spec shown.\u003cbr\u003e3. vs. NVIDIA Blackwell.\u003cbr\u003e4. HGX B300 and HGX B200 shipping now.\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":48952583782625,"sku":null,"price":10999999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/files\/B200-1.png?v=1787151514"},{"product_id":"nvidia-tesla-h100-80gb-pcie-gpu-graphics-cards-video-card","title":"NVIDIA Tesla H100 80GB pcie GPU Graphics Cards Video Card For Server","description":"\u003cp\u003eNVIDIA H100 80GB pcie GPU Graphics Cards Video Card\u003c\/p\u003e\n\u003cdiv\u003e\u003cspan\u003eSupercharge Large Language Model Inference With H100 NVL\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eFor LLMs up to 70 billion parameters (Llama 2 70B), the PCIe-based NVIDIA H100 NVL with NVLink bridge utilizes Transformer Engine, NVLink, and 188GB HBM3 memory to provide optimum performance and easy scaling across any data center, bringing LLMs to the mainstream. Servers equipped with H100 NVL GPUs increase Llama 2 70B performance up to 5x over NVIDIA A100 systems while maintaining low latency in power-constrained data center environments.\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv class=\"nv-title text h--medium aem-GridColumn aem-GridColumn--default--12\"\u003e\n\u003cdiv id=\"nv-title-2eaa66d122\" class=\"general-container-text\"\u003e\n\u003cdiv class=\"text-center lap-text-center tab-text-center mob-text-center\"\u003e\n\u003ch2 class=\"title\"\u003eProduct Specifications\u003c\/h2\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"nv-rawhtml aem-GridColumn--tablet--12 aem-GridColumn--offset--tablet--0 aem-GridColumn--default--none aem-GridColumn--phone--none aem-GridColumn--tablet--none aem-GridColumn--phone--10 aem-GridColumn aem-GridColumn--default--10 aem-GridColumn--offset--phone--1 aem-GridColumn--offset--default--1\"\u003e\n\u003cdiv id=\"nv-rawhtml-d0e2ba1d51\" class=\"general-container-text\"\u003e\n\u003cdiv\u003e\n\u003cdiv id=\"SpecsChart\" class=\"h100-specs scrolling\"\u003e\n\u003ctable id=\"tab-h100\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e \u003c\/th\u003e\n\u003cth\u003eH100 SXM\u003c\/th\u003e\n\u003cth\u003eH100 NVL\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eFP64\u003c\/td\u003e\n\u003ctd\u003e34 teraFLOPS\u003c\/td\u003e\n\u003ctd\u003e30 teraFLOPs\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFP64 Tensor Core\u003c\/td\u003e\n\u003ctd\u003e67 teraFLOPS\u003c\/td\u003e\n\u003ctd\u003e60 teraFLOPs\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFP32\u003c\/td\u003e\n\u003ctd\u003e67 teraFLOPS\u003c\/td\u003e\n\u003ctd\u003e60 teraFLOPs\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTF32 Tensor Core\u003csup\u003e*\u003c\/sup\u003e\n\u003c\/td\u003e\n\u003ctd\u003e989 teraFLOPS\u003c\/td\u003e\n\u003ctd\u003e835 teraFLOPs\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eBFLOAT16 Tensor Core\u003csup\u003e*\u003c\/sup\u003e\n\u003c\/td\u003e\n\u003ctd\u003e1,979 teraFLOPS\u003c\/td\u003e\n\u003ctd\u003e1,671 teraFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFP16 Tensor Core\u003csup\u003e*\u003c\/sup\u003e\n\u003c\/td\u003e\n\u003ctd\u003e1,979 teraFLOPS\u003c\/td\u003e\n\u003ctd\u003e1,671 teraFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFP8 Tensor Core\u003csup\u003e*\u003c\/sup\u003e\n\u003c\/td\u003e\n\u003ctd\u003e3,958 teraFLOPS\u003c\/td\u003e\n\u003ctd\u003e3,341 teraFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eINT8 Tensor Core\u003csup\u003e*\u003c\/sup\u003e\n\u003c\/td\u003e\n\u003ctd\u003e3,958 TOPS\u003c\/td\u003e\n\u003ctd\u003e3,341 TOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU Memory\u003c\/td\u003e\n\u003ctd\u003e80GB\u003c\/td\u003e\n\u003ctd\u003e94GB\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU Memory Bandwidth\u003c\/td\u003e\n\u003ctd\u003e3.35TB\/s\u003c\/td\u003e\n\u003ctd\u003e3.9TB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDecoders\u003c\/td\u003e\n\u003ctd\u003e7 NVDEC\u003cbr\u003e7 JPEG\u003c\/td\u003e\n\u003ctd\u003e7 NVDEC\u003cbr\u003e7 JPEG\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMax Thermal Design Power (TDP)\u003c\/td\u003e\n\u003ctd\u003eUp to 700W (configurable)\u003c\/td\u003e\n\u003ctd\u003e350-400W (configurable)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMulti-Instance GPUs\u003c\/td\u003e\n\u003ctd\u003eUp to 7 MIGS @ 10GB each\u003c\/td\u003e\n\u003ctd\u003eUp to 7 MIGS @ 12GB each\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eForm Factor\u003c\/td\u003e\n\u003ctd\u003eSXM\u003c\/td\u003e\n\u003ctd\u003ePCIe\u003cbr\u003edual-slot air-cooled\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eInterconnect\u003c\/td\u003e\n\u003ctd\u003eNVIDIA NVLink™: 900GB\/s\u003cbr\u003ePCIe Gen5: 128GB\/s\u003c\/td\u003e\n\u003ctd\u003eNVIDIA NVLink: 600GB\/s\u003cbr\u003ePCIe Gen5: 128GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eServer Options\u003c\/td\u003e\n\u003ctd\u003eNVIDIA HGX H100 Partner and NVIDIA-\u003cbr\u003eCertified Systems\u003csup\u003e™\u003c\/sup\u003e\u003cspan\u003e \u003c\/span\u003ewith 4 or 8 GPUs\u003cbr\u003eNVIDIA DGX H100 with 8 GPUs\u003c\/td\u003e\n\u003ctd\u003ePartner and NVIDIA-Certified Systems with 1–8 GPUs\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVIDIA AI Enterprise\u003c\/td\u003e\n\u003ctd\u003eAdd-on\u003c\/td\u003e\n\u003ctd\u003eIncluded\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"description h100-specs-legal\"\u003e\u003cspan class=\"p--small\"\u003e\u003cbr\u003e* With sparsity\u003c\/span\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":48954247905505,"sku":null,"price":59999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/files\/NVIDIATESLAH10080GPCIE_1.jpg?v=1787143575"},{"product_id":"nvidia-h100-nvl-graphic-card-video-cards-94gb-hbm3-pcie-5-0-x16-2x-slot-passive-900-21010-0020-000","title":"NVIDIA H100 NVL Graphic Card Video Cards 94GB HBM3 PCIe 5.0 x16 2x Slot Passive 900-21010-0020-000,","description":"\u003cp\u003eNV Tesla H100 94G pcie Graphics cards video cards\u003c\/p\u003e\n\u003cdiv\u003e\u003cspan\u003eFP64: 30 teraFLOPs\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eFP64 Tensor Core: 60 teraFLOPs\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eFP32: 60 teraFLOPs\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eTF32 Tensor Core*: 835 teraFLOPs\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eBFLOAT16 Tensor Core*: 1,671 teraFLOPS\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eFP16 Tensor Core*: 1,671 teraFLOPS\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eFP8 Tensor Core*: 3,341 teraFLOPS\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eINT8 Tensor Core*: 3,341 TOPS\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eGPU Memory: 94GB\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eGPU Memory Bandwidth: 3.9TB\/s\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eDecoders:7 NVDEC 7 JPEG\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eMax Thermal Design Power (TDP): 350-400W (configurable)\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eMulti-Instance GPUs: Up to 7 MIGS @ 12GB each\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eForm Factor: PCIe dual-slot air-cooled\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eInterconnect: NVIDIA NVLink: 600GB\/s PCIe Gen5: 128GB\/s\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003e\u003c\/span\u003e\u003c\/div\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":48954333626593,"sku":null,"price":72000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/files\/teslaL424GB_1.jpg?v=1787135638"},{"product_id":"nvidia-h100-pcie-96gb-graphics-cards-video-cards","title":"NVIDIA H100 PCIe 96GB Graphics cards video cards","description":"\u003cp\u003eNVIDIA H100 PCIe 96GB Graphics cards video cards\u003c\/p\u003e\n\u003cp\u003e\u003cmeta charset=\"utf-8\"\u003e\u003cspan\u003eThe H100 SXM5 96 GB is a professional graphics card by NVIDIA, Built on the 5 nm process, and based on the GH100 graphics processor, the card does not support DirectX. Since H100 SXM5 96 GB does not support DirectX 11 or DirectX 12, it might not be able to run all the latest games. The GH100 graphics processor is a large chip with a die area of 814 mm² and 80,000 million transistors. It features 16896 shading units, 528 texture mapping units, and 24 ROPs. Also included are 528 Tensor Cores which help improve the speed of machine learning applications with support for the INT8 and FP8 data formats. NVIDIA has paired 96 GB HBM3 memory with the H100 SXM5 96 GB, which are connected using a 5120-bit memory interface. The GPU is operating at a frequency of 1350 MHz, which can be boosted up to 1980 MHz, memory is running at 1313 MHz.\u003c\/span\u003e\u003cbr\u003e\u003cspan\u003eBeing a sxm module card, the NVIDIA H100 SXM5 96 GB draws power from an 8-pin EPS power connector, with power draw rated at 700 W maximum. This device has no display connectivity, as it is not designed to have monitors connected to it. H100 SXM5 96 GB is connected to the rest of the system using a PCI-Express 5.0 x16 interface.\u003c\/span\u003e\u003c\/p\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":48954350534881,"sku":null,"price":89999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/files\/nvidia-h100-sxm5.jpg?v=1787201515"},{"product_id":"nvidia-h800-tensor-core-gpu-pcie-80gb-graphics-card-for-ai-data-data-center","title":"Nvidia H800 Tensor Core GPU PCIE 80GB Graphics card For Ai Data Data Center","description":"\u003cp\u003eNvidia H800 Tensor Core GPU PCIE 80GB Graphics card For Ai Data Data Center\u003c\/p\u003e\n\u003cdiv\u003e\u003cspan\u003eNVIDIA GPU computing card and accelerator card: A2 16G, T4 16G, A10 24G, A16 64G, A30 24G, A40 48G, L4, L40, L40S,\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003e\u003cspan class=\"hover-sent\"\u003eA100 40G, A100 80G, A800 80G, H100 80G, V100 16G, V100 32G, V100S 32G, P40 24G, M10 32G, M60 16G\u003c\/span\u003e\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cspan\u003e\u003cspan class=\"\"\u003e(if you have any needs, please feel free to inquire about the price \u003c\/span\u003e\u003c\/span\u003e\u003cspan\u003esales@kingm.com\u003c\/span\u003e\u003cspan\u003e)\u003c\/span\u003e\n\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003e\u003cspan class=\"click-sent\"\u003eNVIDIA Tesla A100 80G Deep Learning GPU Computing Graphics Card (PCI-E) Official Warranty 3\u003c\/span\u003e\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eThe official version is not a customized version of SXM4 and can provide a 3-year official warranty service in the United States\u003c\/span\u003e\u003c\/div\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":48954369245409,"sku":null,"price":49999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/files\/NVIDIATESLAH10080GPCIE_1.jpg?v=1787143575"},{"product_id":"nvidia-tesla-h20-96gb-pcie-graphics-cards-video-card","title":"Nvidia Tesla H20 96GB pcie Graphics cards Video Card","description":"\u003cdiv\u003e\u003cspan\u003eApplications of H20\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003e1. AI Inference \u0026amp; Large Language Models (LLMs)\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eOptimized for large AI models such as ChatGPT, Gemini, and Claude.\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eDesigned for fast, efficient inference in cloud environments.\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eReduces power consumption while maintaining high AI compute performance.\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003e2. Cloud Computing \u0026amp; AI SaaS Services\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eIdeal for deployment on AWS, Google Cloud, Alibaba Cloud, and other cloud platforms.\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eSupports AI-based speech recognition, machine translation, and virtual assistants.\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003e\u003cspan class=\"\"\u003eProvides a scalable, cost-effective AI infrastructure.\u003c\/span\u003e\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003e\u003cspan class=\"\"\u003e3. Medical AI (Medical Imaging \u0026amp; Genomic Analysis)\u003c\/span\u003e\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003e\u003cspan class=\"\"\u003eEnhances medical imaging recognition (CT\/MRI analysis).\u003c\/span\u003e\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eAccelerates protein folding prediction (AlphaFold) and genetic sequencing.\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eReduces processing times for AI-driven diagnostics.\u003c\/span\u003e\u003c\/div\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":48954376323297,"sku":null,"price":59999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/files\/teslaL424GB_1.jpg?v=1787135638"},{"product_id":"nvidia-tesla-h20-141-gb-gpu-sxm5-modified-version-pcie-gen-5-x16","title":"NVIDIA Tesla H20 141 GB GPU SXM5 modified version PCIe Gen 5 x16","description":"\u003cp\u003e\u003cmeta charset=\"utf-8\"\u003e\u003cspan\u003eThe H200 SXM 141 GB is a professional graphics card by NVIDIA.Built on the 5 nm process, and based on the GH100 graphics processor, the card does not support DirectX. Since H200 SXM 141 GB does not support DirectX 11 or DirectX 12, it might not be able to run all the latest games. The GH100 graphics processor is a large chip with a die area of 814 mm² and 80,000 million transistors. It features 16896 shading units, 528 texture mapping units, and 24 ROPs. Also included are 528 Tensor Cores which help improve the speed of machine learning applications with support for the INT8 and FP8 data formats. NVIDIA has paired 141 GB HBM3e memory with the H200 SXM 141 GB, which are connected using a 6144-bit memory interface. The GPU is operating at a frequency of 1500 MHz, which can be boosted up to 1980 MHz, memory is running at 1593 MHz.\u003c\/span\u003e\u003cbr\u003e\u003cspan\u003eBeing a sxm module card, the NVIDIA H200 SXM 141 GB draws power from an 8-pin EPS power connector, with power draw rated at 700 W maximum. This device has no display connectivity, as it is not designed to have monitors connected to it. H200 SXM 141 GB is connected to the rest of the system using a PCI-Express 5.0 x16 interface.\u003c\/span\u003e\u003c\/p\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":48955449966817,"sku":null,"price":59999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/files\/SXM.jpg?v=1787220903"},{"product_id":"nvidia-tesla-l40-48gb-gddr6-pci-express-4-0-x16-cuda-accelerator-graphics-card","title":"NVIDIA Tesla L40 48GB GDDR6 PCI Express 4.0 x16 CUDA Accelerator Graphics Card","description":"\u003cp\u003e\u003cmeta charset=\"utf-8\"\u003e\u003cspan\u003eNVIDIA L40 (48GB) is a high-performance, data center-ready GPU based on the Ada Lovelace architecture, designed for professional visualization, 3D rendering, AI inference, and generative AI workloads. Launched in October 2022, it features 48GB of GDDR6 ECC memory and 3rd Generation RT Cores, providing a robust solution for NVIDIA Omniverse applications and high-end virtual workstations.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2\u003eHighlighted Features    \u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003eNVIDIA\u003c\/li\u003e\n\u003cli\u003eTesla L40\u003c\/li\u003e\n\u003cli\u003e48GB\u003c\/li\u003e\n\u003cli\u003eGDDR6\u003c\/li\u003e\n\u003cli\u003ePCI Express 4.0 x16\u003c\/li\u003e\n\u003cli\u003eAccelerator Graphics Card\u003c\/li\u003e\n\u003cli\u003ePart #: NVIDIA Tesla L40 48GB\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":48956050735329,"sku":null,"price":11999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/files\/NVIDIATESLAH10080GPCIE_1.jpg?v=1787143575"},{"product_id":"nv-tesla-l40s-48gb-gpu-deep-learning-computing-edge-computing-ai-server-graphics-card-1","title":"NV Tesla L40S 48GB GPU Deep Learning Computing Edge Computing AI Server Graphics Card","description":"\u003cp\u003eNVIDIA L40s 48GB GPU Graphics Cards Video cards\u003c\/p\u003e\n\u003cdiv class=\"elementor-element elementor-element-58efda92 elementor-widget elementor-widget-icon-box\"\u003e\n\u003cdiv class=\"elementor-icon-box-wrapper\"\u003e\n\u003cdiv class=\"elementor-icon-box-content\"\u003e\n\u003ch3 class=\"elementor-icon-box-title\"\u003e\u003cspan\u003eProduct Name\u003c\/span\u003e\u003c\/h3\u003e\n\u003cp class=\"elementor-icon-box-description\"\u003eNVIDIA L40s GPU\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"elementor-element elementor-element-76c6d860 elementor-widget elementor-widget-icon-box\"\u003e\n\u003cdiv class=\"elementor-icon-box-wrapper\"\u003e\n\u003cdiv class=\"elementor-icon-box-content\"\u003e\n\u003ch3 class=\"elementor-icon-box-title\"\u003e\u003cspan\u003eArchitecture\u003c\/span\u003e\u003c\/h3\u003e\n\u003cp class=\"elementor-icon-box-description\"\u003eNVIDIA Ada Lovelace\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"elementor-element elementor-element-7cca50b6 elementor-widget elementor-widget-icon-box\"\u003e\n\u003cdiv class=\"elementor-icon-box-wrapper\"\u003e\n\u003cdiv class=\"elementor-icon-box-content\"\u003e\n\u003ch3 class=\"elementor-icon-box-title\"\u003e\u003cspan\u003eMemory\u003c\/span\u003e\u003c\/h3\u003e\n\u003cp class=\"elementor-icon-box-description\"\u003e48GB GDDR6 with ECC\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"elementor-element elementor-element-7cee318a elementor-widget elementor-widget-icon-box\"\u003e\n\u003cdiv class=\"elementor-icon-box-wrapper\"\u003e\n\u003cdiv class=\"elementor-icon-box-content\"\u003e\n\u003ch3 class=\"elementor-icon-box-title\"\u003e\u003cspan\u003eCompute Power\u003c\/span\u003e\u003c\/h3\u003e\n\u003cp class=\"elementor-icon-box-description\"\u003eUp to 1,466 TOPS (FP8 Tensor Core, with sparsity)\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"elementor-element elementor-element-1fa71b5d elementor-widget elementor-widget-icon-box\"\u003e\n\u003cdiv class=\"elementor-icon-box-wrapper\"\u003e\n\u003cdiv class=\"elementor-icon-box-content\"\u003e\n\u003ch3 class=\"elementor-icon-box-title\"\u003e\u003cspan\u003eUse Cases\u003c\/span\u003e\u003c\/h3\u003e\n\u003cp class=\"elementor-icon-box-description\"\u003eGenerative AI, LLM inference, LLM fine-tuning and small-model training, NVIDIA Omniverse Enterprise, rendering, 3D graphics, streaming, and video content\u003c\/p\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":48956275720417,"sku":null,"price":12999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/files\/NVIDIATeslaA216G_2.jpg?v=1787140014"}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0833\/6654\/3585\/collections\/NVIDIA-RTX-PRO-6000-Blackwell-Workstation-Edition-3QTR-Back-Right.png?v=1783043049","url":"https:\/\/kingm.com\/fr\/collections\/nvidia-graphics-card.oembed?page=2","provider":"Neobitti Inc. In ai GPU server Processors Motherboard Storage Graphics Card Memory","version":"1.0","type":"link"}