{"title":"Carte graphique","description":"\u003cp\u003e\u003cmeta charset=\"utf-8\"\u003eMarque de cartes graphiques GPU : PNY, Sapphire, Nvidia, MSI, ASUS, Gigabyte, MSI, Asrock, Colorful, etc.\u003c\/p\u003e","products":[{"product_id":"nv-tesla-l40s-48gb-gpu-deep-learning-computing-edge-computing-ai-server-graphics-card-1","title":"Carte graphique NV Tesla L40S 48 Go GPU Deep Learning Computing Edge Computing AI Server","description":"\u003cp\u003eCartes graphiques NVIDIA L40s 48 Go GPU\u003c\/p\u003e\n\u003cdiv class=\"elementor-element elementor-element-58efda92 elementor-widget elementor-widget-icon-box\"\u003e\n\n\u003cdiv class=\"elementor-icon-box-wrapper\"\u003e\n\n\u003cdiv class=\"elementor-icon-box-content\"\u003e\n\n\u003ch3 class=\"elementor-icon-box-title\"\u003e\u003cspan\u003eNom du produit\u003c\/span\u003e\u003c\/h3\u003e\n\n\u003cp class=\"elementor-icon-box-description\"\u003eNVIDIA L40s GPU\u003c\/p\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\u003cdiv class=\"elementor-element elementor-element-76c6d860 elementor-widget elementor-widget-icon-box\"\u003e\n\n\u003cdiv class=\"elementor-icon-box-wrapper\"\u003e\n\n\u003cdiv class=\"elementor-icon-box-content\"\u003e\n\n\u003ch3 class=\"elementor-icon-box-title\"\u003e\u003cspan\u003eArchitecture\u003c\/span\u003e\u003c\/h3\u003e\n\n\u003cp class=\"elementor-icon-box-description\"\u003eNVIDIA Ada Lovelace\u003c\/p\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\u003cdiv class=\"elementor-element elementor-element-7cca50b6 elementor-widget elementor-widget-icon-box\"\u003e\n\n\u003cdiv class=\"elementor-icon-box-wrapper\"\u003e\n\n\u003cdiv class=\"elementor-icon-box-content\"\u003e\n\n\u003ch3 class=\"elementor-icon-box-title\"\u003e\u003cspan\u003eMémoire\u003c\/span\u003e\u003c\/h3\u003e\n\n\u003cp class=\"elementor-icon-box-description\"\u003e48 Go GDDR6 avec ECC\u003c\/p\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\u003cdiv class=\"elementor-element elementor-element-7cee318a elementor-widget elementor-widget-icon-box\"\u003e\n\n\u003cdiv class=\"elementor-icon-box-wrapper\"\u003e\n\n\u003cdiv class=\"elementor-icon-box-content\"\u003e\n\n\u003ch3 class=\"elementor-icon-box-title\"\u003e\u003cspan\u003ePuissance de calcul\u003c\/span\u003e\u003c\/h3\u003e\n\n\u003cp class=\"elementor-icon-box-description\"\u003eJusqu'à 1 466 TOPS (FP8 Tensor Core, avec parcimonie)\u003c\/p\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\u003cdiv class=\"elementor-element elementor-element-1fa71b5d elementor-widget elementor-widget-icon-box\"\u003e\n\n\u003cdiv class=\"elementor-icon-box-wrapper\"\u003e\n\n\u003cdiv class=\"elementor-icon-box-content\"\u003e\n\n\u003ch3 class=\"elementor-icon-box-title\"\u003e\u003cspan\u003eCas d'utilisation\u003c\/span\u003e\u003c\/h3\u003e\n\n\u003cp class=\"elementor-icon-box-description\"\u003eIA générative, inférence LLM, réglage fin LLM et entraînement de petits modèles, NVIDIA Omniverse Enterprise, rendu, graphiques 3D, streaming et contenu vidéo\u003c\/p\u003e\n\n\n\u003c\/div\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":50526637359340,"sku":null,"price":12999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0848\/8537\/3164\/files\/NVIDIATeslaA216G_2_98875c65-f2a6-430a-9880-c12c1c31de9b.jpg?v=1790326125"},{"product_id":"nvidia-tesla-l40-48gb-gddr6-pci-express-4-0-x16-cuda-accelerator-graphics-card","title":"Carte graphique d'accélérateur CUDA NVIDIA Tesla L40 48 Go GDDR6 PCI Express 4.0 x16","description":"\u003cp\u003e\u003cmeta charset=\"utf-8\"\u003e\u003cspan\u003eLe NVIDIA L40 (48 Go) est un GPU haute performance, prêt pour les centres de données, basé sur l'architecture Ada Lovelace, conçu pour la visualisation professionnelle, le rendu 3D, l'inférence IA et les charges de travail d'IA générative. Lancé en octobre 2022, il dispose de 48 Go de mémoire GDDR6 ECC et de cœurs RT de 3e génération, offrant une solution robuste pour les applications NVIDIA Omniverse et les stations de travail virtuelles haut de gamme.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch2\u003eCaractéristiques principales\u003c\/h2\u003e\n\u003cul\u003e\n\n\u003cli\u003eNVIDIA\u003c\/li\u003e\n\n\u003cli\u003eTesla L40\u003c\/li\u003e\n\n\u003cli\u003e48 Go\u003c\/li\u003e\n\n\u003cli\u003eGDDR6\u003c\/li\u003e\n\n\u003cli\u003ePCI Express 4.0 x16\u003c\/li\u003e\n\n\u003cli\u003eCarte graphique accélératrice\u003c\/li\u003e\n\n\u003cli\u003eRéférence : NVIDIA Tesla L40 48 Go\u003c\/li\u003e\n\n\n\u003c\/ul\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":50526637392108,"sku":null,"price":11999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0848\/8537\/3164\/files\/NVIDIATESLAH10080GPCIE_1_9bd444bd-afb4-4e18-a4ad-fbdf83e9dfb0.jpg?v=1790326126"},{"product_id":"nvidia-tesla-h20-141-gb-gpu-sxm5-modified-version-pcie-gen-5-x16","title":"NVIDIA Tesla H20 141 Go GPU SXM5 version modifiée PCIe Gen 5 x16","description":"\u003cp\u003e\u003cmeta charset=\"utf-8\"\u003e\u003cspan\u003eLa H200 SXM 141 Go est une carte graphique professionnelle de NVIDIA. Construite sur un processus de gravure de 5 nm et basée sur le processeur graphique GH100, la carte ne prend pas en charge DirectX. Comme la H200 SXM 141 Go ne prend pas en charge DirectX 11 ou DirectX 12, elle pourrait ne pas être capable d'exécuter tous les derniers jeux. Le processeur graphique GH100 est une grande puce avec une surface de die de 814 mm² et 80 000 millions de transistors. Il dispose de 16896 unités de shader, 528 unités de mappage de textures et 24 ROPs. Sont également inclus 528 cœurs Tensor qui aident à améliorer la vitesse des applications d'apprentissage automatique avec la prise en charge des formats de données INT8 et FP8. NVIDIA a associé 141 Go de mémoire HBM3e à la H200 SXM 141 Go, qui sont connectés via une interface mémoire de 6144 bits. Le GPU fonctionne à une fréquence de 1500 MHz, qui peut être augmentée jusqu'à 1980 MHz, la mémoire fonctionnant à 1593 MHz.\u003c\/span\u003e\u003cbr\u003e\u003cspan\u003eÉtant une carte module SXM, la NVIDIA H200 SXM 141 Go tire son alimentation d'un connecteur d'alimentation EPS à 8 broches, avec une consommation électrique nominale de 700 W maximum. Cet appareil n'a pas de connectivité d'affichage, car il n'est pas conçu pour y connecter des moniteurs. La H200 SXM 141 Go est connectée au reste du système via une interface PCI-Express 5.0 x16.\u003c\/span\u003e\u003c\/p\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":50526637424876,"sku":null,"price":59999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0848\/8537\/3164\/files\/SXM.jpg?v=1790326127"},{"product_id":"nvidia-tesla-h20-96gb-pcie-graphics-cards-video-card","title":"Carte graphique Nvidia Tesla H20 96 Go pcie","description":"\u003cdiv\u003e\u003cspan\u003eApplications de H20\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003e1. Inférence IA et grands modèles linguistiques (LLM)\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eOptimisé pour les grands modèles d'IA tels que ChatGPT, Gemini et Claude.\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eConçu pour une inférence rapide et efficace dans les environnements cloud.\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eRéduit la consommation d'énergie tout en maintenant des performances de calcul IA élevées.\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003e2. Services de cloud computing et d'IA SaaS\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eIdéal pour le déploiement sur AWS, Google Cloud, Alibaba Cloud et d'autres plateformes cloud.\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003ePrend en charge la reconnaissance vocale basée sur l'IA, la traduction automatique et les assistants virtuels.\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003e\u003cspan class=\"\"\u003eFournit une infrastructure IA évolutive et rentable.\u003c\/span\u003e\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003e\u003cspan class=\"\"\u003e3. IA médicale (imagerie médicale et analyse génomique)\u003c\/span\u003e\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003e\u003cspan class=\"\"\u003eAméliore la reconnaissance de l'imagerie médicale (analyse CT\/IRM).\u003c\/span\u003e\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eAccélère la prédiction du repliement des protéines (AlphaFold) et le séquençage génétique.\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eRéduit les temps de traitement pour les diagnostics basés sur l'IA.\u003c\/span\u003e\u003c\/div\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":50526637457644,"sku":null,"price":59999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0848\/8537\/3164\/files\/teslaL424GB_1_b98c3cf8-d04c-4e26-af30-213db67a4901.jpg?v=1790326128"},{"product_id":"nvidia-h800-tensor-core-gpu-pcie-80gb-graphics-card-for-ai-data-data-center","title":"Carte graphique Nvidia H800 Tensor Core GPU PCIE 80Go pour centres de données et IA","description":"\u003cp\u003eCarte graphique Nvidia H800 Tensor Core GPU PCIE 80 Go pour centre de données IA\u003c\/p\u003e\n\u003cdiv\u003e\u003cspan\u003eCarte de calcul GPU NVIDIA et carte accélératrice : 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\n\u003cspan\u003e\u003cspan class=\"\"\u003e(si vous avez des besoins, n'hésitez pas à demander le prix à \u003c\/span\u003e\u003c\/span\u003e\u003cspan\u003esales@kingm.com\u003c\/span\u003e\u003cspan\u003e)\u003c\/span\u003e\n\n\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003e\u003cspan class=\"click-sent\"\u003eCarte graphique de calcul GPU Deep Learning NVIDIA Tesla A100 80G (PCI-E) Garantie officielle 3\u003c\/span\u003e\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eLa version officielle n'est pas une version personnalisée de SXM4 et peut offrir un service de garantie officiel de 3 ans aux États-Unis\u003c\/span\u003e\u003c\/div\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":50526637490412,"sku":null,"price":49999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0848\/8537\/3164\/files\/NVIDIATESLAH10080GPCIE_1_5335682e-9c3d-4092-92b7-6091345d0353.jpg?v=1790326129"},{"product_id":"nvidia-h100-pcie-96gb-graphics-cards-video-cards","title":"Cartes graphiques NVIDIA H100 PCIe 96 Go","description":"\u003cp\u003eCartes graphiques NVIDIA H100 PCIe 96 Go\u003c\/p\u003e\n\u003cp\u003e\u003cmeta charset=\"utf-8\"\u003e\u003cspan\u003eLa H100 SXM5 96 Go est une carte graphique professionnelle de NVIDIA. Construite selon le procédé de fabrication en 5 nm et basée sur le processeur graphique GH100, la carte ne prend pas en charge DirectX. Puisque la H100 SXM5 96 Go ne prend pas en charge DirectX 11 ou DirectX 12, elle pourrait ne pas être en mesure de faire fonctionner tous les jeux les plus récents. Le processeur graphique GH100 est une grande puce avec une surface de 814 mm² et 80 000 millions de transistors. Elle dispose de 16896 unités de shader, 528 unités de mappage de textures et 24 ROPs. Sont également inclus 528 Tensor Cores qui aident à améliorer la vitesse des applications d'apprentissage automatique avec la prise en charge des formats de données INT8 et FP8. NVIDIA a associé 96 Go de mémoire HBM3 à la H100 SXM5 96 Go, qui sont connectés via une interface mémoire de 5120 bits. Le GPU fonctionne à une fréquence de 1350 MHz, qui peut être boostée jusqu'à 1980 MHz, la mémoire fonctionne à 1313 MHz.\u003c\/span\u003e\u003cbr\u003e\u003cspan\u003eÉtant une carte à module SXM, la NVIDIA H100 SXM5 96 Go tire son alimentation d'un connecteur d'alimentation EPS à 8 broches, avec une consommation électrique maximale nominale de 700 W. Cet appareil n'a pas de connectivité d'affichage, car il n'est pas conçu pour y connecter des moniteurs. La H100 SXM5 96 Go est connectée au reste du système à l'aide d'une interface PCI-Express 5.0 x16.\u003c\/span\u003e\u003c\/p\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":50526637523180,"sku":null,"price":89999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0848\/8537\/3164\/files\/nvidia-h100-sxm5.jpg?v=1790326130"},{"product_id":"nvidia-h100-nvl-graphic-card-video-cards-94gb-hbm3-pcie-5-0-x16-2x-slot-passive-900-21010-0020-000","title":"Carte graphique NVIDIA H100 NVL Cartes vidéo 94 Go HBM3 PCIe 5.0 x16 2 emplacements Passif 900-21010-0020-000,","description":"\u003cp\u003eCartes graphiques NV Tesla H100 94G pcie\u003c\/p\u003e\n\u003cdiv\u003e\u003cspan\u003eFP64 : 30 téraFLOPS\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eCœur Tensor FP64 : 60 téraFLOPS\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eFP32 : 60 téraFLOPS\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eCœur Tensor TF32* : 835 téraFLOPS\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eCœur Tensor BFLOAT16* : 1 671 téraFLOPS\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eCœur Tensor FP16* : 1 671 téraFLOPS\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eCœur Tensor FP8* : 3 341 téraFLOPS\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eCœur Tensor INT8* : 3 341 TOPS\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eMémoire GPU : 94 Go\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eBande passante mémoire GPU : 3,9 To\/s\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eDécodeurs : 7 NVDEC 7 JPEG\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003ePuissance thermique maximale (TDP) : 350-400 W (configurable)\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eGPU multi-instances : jusqu'à 7 MIGS de 12 Go chacun\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eFacteur de forme : PCIe double-slot à refroidissement par air\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003eInterconnexion : NVIDIA NVLink : 600 Go\/s PCIe Gen5 : 128 Go\/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":50526637555948,"sku":null,"price":72000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0848\/8537\/3164\/files\/teslaL424GB_1_824e5f93-29cf-4eb5-8062-ba9304aa60f2.jpg?v=1790326131"},{"product_id":"nvidia-tesla-h100-80gb-pcie-gpu-graphics-cards-video-card","title":"Cartes graphiques GPU NVIDIA Tesla H100 80GB PCIe pour serveur","description":"\u003cp\u003eCarte graphique GPU PCIe NVIDIA H100 80 Go\u003c\/p\u003e\n\u003cdiv\u003e\u003cspan\u003eSurmultipliez l'inférence de grands modèles de langage avec la H100 NVL\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003ePour les LLM allant jusqu'à 70 milliards de paramètres (Llama 2 70B), la NVIDIA H100 NVL basée sur PCIe avec pont NVLink utilise Transformer Engine, NVLink et 188 Go de mémoire HBM3 pour offrir des performances optimales et une mise à l'échelle facile dans n'importe quel centre de données, démocratisant ainsi les LLM. Les serveurs équipés de GPU H100 NVL augmentent les performances de Llama 2 70B jusqu'à 5 fois par rapport aux systèmes NVIDIA A100 tout en maintenant une faible latence dans les environnements de centre de données à puissance limitée.\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\n\u003cdiv class=\"nv-title text h--medium aem-GridColumn aem-GridColumn--default--12\"\u003e\n\n\u003cdiv id=\"nv-title-2eaa66d122\" class=\"general-container-text\"\u003e\n\n\u003cdiv class=\"text-center lap-text-center tab-text-center mob-text-center\"\u003e\n\n\u003ch2 class=\"title\"\u003eSpécifications du produit\u003c\/h2\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\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\n\u003cdiv id=\"nv-rawhtml-d0e2ba1d51\" class=\"general-container-text\"\u003e\n\n\u003cdiv\u003e\n\n\u003cdiv id=\"SpecsChart\" class=\"h100-specs scrolling\"\u003e\n\n\u003ctable id=\"tab-h100\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n\n\u003cthead\u003e\n\n\u003ctr\u003e\n\n\u003cth\u003e \u003c\/th\u003e\n\n\u003cth\u003eH100 SXM\u003c\/th\u003e\n\n\u003cth\u003eH100 NVL\u003c\/th\u003e\n\n\n\u003c\/tr\u003e\n\n\n\u003c\/thead\u003e\n\n\u003ctbody\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eFP64\u003c\/td\u003e\n\n\u003ctd\u003e34 téraFLOPS\u003c\/td\u003e\n\n\u003ctd\u003e30 téraFLOPS\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eFP64 Tensor Core\u003c\/td\u003e\n\n\u003ctd\u003e67 téraFLOPS\u003c\/td\u003e\n\n\u003ctd\u003e60 téraFLOPS\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eFP32\u003c\/td\u003e\n\n\u003ctd\u003e67 téraFLOPS\u003c\/td\u003e\n\n\u003ctd\u003e60 téraFLOPS\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eTF32 Tensor Core\u003csup\u003e*\u003c\/sup\u003e\n\n\u003c\/td\u003e\n\n\u003ctd\u003e989 téraFLOPS\u003c\/td\u003e\n\n\u003ctd\u003e835 téraFLOPS\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eBFLOAT16 Tensor Core\u003csup\u003e*\u003c\/sup\u003e\n\n\u003c\/td\u003e\n\n\u003ctd\u003e1 979 téraFLOPS\u003c\/td\u003e\n\n\u003ctd\u003e1 671 téraFLOPS\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eFP16 Tensor Core\u003csup\u003e*\u003c\/sup\u003e\n\n\u003c\/td\u003e\n\n\u003ctd\u003e1 979 téraFLOPS\u003c\/td\u003e\n\n\u003ctd\u003e1 671 téraFLOPS\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eFP8 Tensor Core\u003csup\u003e*\u003c\/sup\u003e\n\n\u003c\/td\u003e\n\n\u003ctd\u003e3 958 téraFLOPS\u003c\/td\u003e\n\n\u003ctd\u003e3 341 téraFLOPS\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eINT8 Tensor Core\u003csup\u003e*\u003c\/sup\u003e\n\n\u003c\/td\u003e\n\n\u003ctd\u003e3 958 TOPS\u003c\/td\u003e\n\n\u003ctd\u003e3 341 TOPS\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eMémoire GPU\u003c\/td\u003e\n\n\u003ctd\u003e80 Go\u003c\/td\u003e\n\n\u003ctd\u003e94 Go\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eBande passante mémoire GPU\u003c\/td\u003e\n\n\u003ctd\u003e3,35 To\/s\u003c\/td\u003e\n\n\u003ctd\u003e3,9 To\/s\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eDécodeurs\u003c\/td\u003e\n\n\u003ctd\u003e7 NVDEC\u003cbr\u003e7 JPEG\u003c\/td\u003e\n\n\u003ctd\u003e7 NVDEC\u003cbr\u003e7 JPEG\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003ePuissance de dissipation thermique maximale (TDP)\u003c\/td\u003e\n\n\u003ctd\u003eJusqu'à 700 W (configurable)\u003c\/td\u003e\n\n\u003ctd\u003e350-400 W (configurable)\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eGPU multi-instances\u003c\/td\u003e\n\n\u003ctd\u003eJusqu'à 7 MIGS à 10 Go chacun\u003c\/td\u003e\n\n\u003ctd\u003eJusqu'à 7 MIGS à 12 Go chacun\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eFacteur de forme\u003c\/td\u003e\n\n\u003ctd\u003eSXM\u003c\/td\u003e\n\n\u003ctd\u003ePCIe\u003cbr\u003edouble slot refroidi par air\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eInterconnexion\u003c\/td\u003e\n\n\u003ctd\u003eNVIDIA NVLink™ : 900 Go\/s\u003cbr\u003ePCIe Gen5 : 128 Go\/s\u003c\/td\u003e\n\n\u003ctd\u003eNVIDIA NVLink : 600 Go\/s\u003cbr\u003ePCIe Gen5 : 128 Go\/s\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eOptions de serveur\u003c\/td\u003e\n\n\u003ctd\u003eSystèmes certifiés NVIDIA HGX H100 Partner et NVIDIA-\u003cbr\u003eCertified Systems\u003csup\u003e™\u003c\/sup\u003e\u003cspan\u003e \u003c\/span\u003eavec 4 ou 8 GPU\u003cbr\u003eNVIDIA DGX H100 avec 8 GPU\u003c\/td\u003e\n\n\u003ctd\u003eSystèmes certifiés Partner et NVIDIA avec 1 à 8 GPU\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eNVIDIA AI Enterprise\u003c\/td\u003e\n\n\u003ctd\u003eAdd-on\u003c\/td\u003e\n\n\u003ctd\u003eInclus\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\n\n\n\u003c\/table\u003e\n\n\n\u003c\/div\u003e\n\n\u003cdiv class=\"description h100-specs-legal\"\u003e\u003cspan class=\"p--small\"\u003e\u003cbr\u003e* Avec parcimonie\u003c\/span\u003e\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\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":50526637588716,"sku":null,"price":59999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0848\/8537\/3164\/files\/NVIDIATESLAH10080GPCIE_1_d318dbb8-a6a5-4795-b442-3acf2648b2c0.jpg?v=1790326132"},{"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 Go HBM3E 7,7 To\/s 8 cartes graphiques NVIDIA Blackwell Ultra SXM Cartes vidéo GPU NVIDIA Blackwell Ultra HBM3E","description":"\u003cp\u003eNVIDIA HGX B200 180 Go HBM3E 7,7 To\/s 8x Cartes graphiques NVIDIA Blackwell Ultra SXM Cartes vidéo GPU NVIDIA Blackwell Ultra 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\n\u003cdiv\u003e\n\n\u003cdiv class=\"nv-table-wrapper\"\u003e\n\n\u003cdiv class=\"nv-table stickyscroll overflow-right allowstickyth sticky-columns-col1\"\u003e\n\n\u003ctable class=\"alt-row-2 even-columns\" style=\"width: 100%;\"\u003e\n\n\u003cthead\u003e\n\n\u003ctr\u003e\n\n\u003cth scope=\"col\" class=\"second-column  even-column odd-row\" style=\"width: 37.5%;\"\u003eHGX B300\u003csup\u003e4\u003c\/sup\u003e\n\n\u003c\/th\u003e\n\n\u003cth scope=\"col\" class=\"third-column odd-column odd-row\" style=\"width: 33.75%;\"\u003eHGX B200\u003csup\u003e4\u003c\/sup\u003e\n\n\u003c\/th\u003e\n\n\n\u003c\/tr\u003e\n\n\n\u003c\/thead\u003e\n\n\u003ctbody\u003e\n\n\u003ctr\u003e\n\n\u003cth scope=\"row\" class=\"first-column   odd-column even-row\" style=\"width: 37.5%;\"\u003eFacteur de forme\u003c\/th\u003e\n\n\u003ctd class=\"even-column even-row second-column\" style=\"width: 33.75%;\"\u003e8x NVIDIA Blackwell Ultra SXM\u003c\/td\u003e\n\n\u003ctd class=\"odd-column even-row third-column\" style=\"width: 28.0357%;\"\u003e8x NVIDIA Blackwell SXM\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003cth scope=\"row\" class=\"first-column   odd-column odd-row\" style=\"width: 37.5%;\"\u003eCœur tensoriel FP4\u003csup\u003e1\u003c\/sup\u003e\n\n\u003c\/th\u003e\n\n\u003ctd class=\"even-column odd-row second-column\" style=\"width: 33.75%;\"\u003e144 PFLOPS | 108 PFLOPS\u003c\/td\u003e\n\n\u003ctd class=\"odd-column odd-row third-column\" style=\"width: 28.0357%;\"\u003e144 PFLOPS | 72 PFLOPS\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003cth scope=\"row\" class=\"first-column   odd-column even-row\" style=\"width: 37.5%;\"\u003eCœur tensoriel FP8\/FP6\u003csup\u003e2\u003c\/sup\u003e\n\n\u003c\/th\u003e\n\n\u003ctd class=\"even-column even-row second-column\" style=\"width: 33.75%;\"\u003e72 PFLOPS\u003c\/td\u003e\n\n\u003ctd class=\"odd-column even-row third-column\" style=\"width: 28.0357%;\"\u003e72 PFLOPS\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003cth scope=\"row\" class=\"first-column   odd-column odd-row\" style=\"width: 37.5%;\"\u003eCœur tensoriel INT8\u003csup\u003e2\u003c\/sup\u003e\n\n\u003c\/th\u003e\n\n\u003ctd class=\"even-column odd-row second-column\" style=\"width: 33.75%;\"\u003e3 POPS\u003c\/td\u003e\n\n\u003ctd class=\"odd-column odd-row third-column\" style=\"width: 28.0357%;\"\u003e72 POPS\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003cth scope=\"row\" class=\"first-column   odd-column even-row\" style=\"width: 37.5%;\"\u003eCœur tensoriel FP16\/BF16\u003csup\u003e2\u003c\/sup\u003e\n\n\u003c\/th\u003e\n\n\u003ctd class=\"even-column even-row second-column\" style=\"width: 33.75%;\"\u003e36 PFLOPS\u003c\/td\u003e\n\n\u003ctd class=\"odd-column even-row third-column\" style=\"width: 28.0357%;\"\u003e36 PFLOPS\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003cth scope=\"row\" class=\"first-column   odd-column odd-row\" style=\"width: 37.5%;\"\u003eCœur tensoriel TF32\u003csup\u003e2\u003c\/sup\u003e\n\n\u003c\/th\u003e\n\n\u003ctd class=\"even-column odd-row second-column\" style=\"width: 33.75%;\"\u003e18 PFLOPS\u003c\/td\u003e\n\n\u003ctd class=\"odd-column odd-row third-column\" style=\"width: 28.0357%;\"\u003e18 PFLOPS\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003cth scope=\"row\" class=\"first-column   odd-column even-row\" style=\"width: 37.5%;\"\u003eFP32\u003c\/th\u003e\n\n\u003ctd class=\"even-column even-row second-column\" style=\"width: 33.75%;\"\u003e600 TFLOPS\u003c\/td\u003e\n\n\u003ctd class=\"odd-column even-row third-column\" style=\"width: 28.0357%;\"\u003e600 TFLOPS\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003cth scope=\"row\" class=\"first-column   odd-column odd-row\" style=\"width: 37.5%;\"\u003eCœur tensoriel FP64\/FP64\u003c\/th\u003e\n\n\u003ctd class=\"even-column odd-row second-column\" style=\"width: 33.75%;\"\u003e10 TFLOPS\u003c\/td\u003e\n\n\u003ctd class=\"odd-column odd-row third-column\" style=\"width: 28.0357%;\"\u003e296 TFLOPS\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003cth scope=\"row\" class=\"first-column   odd-column even-row\" style=\"width: 37.5%;\"\u003eMémoire totale\u003c\/th\u003e\n\n\u003ctd class=\"even-column even-row second-column\" style=\"width: 33.75%;\"\u003e2,1 To\u003c\/td\u003e\n\n\u003ctd class=\"odd-column even-row third-column\" style=\"width: 28.0357%;\"\u003e1,4 To\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003cth scope=\"row\" class=\"first-column   odd-column odd-row\" style=\"width: 37.5%;\"\u003eNVIDIA NVLink\u003c\/th\u003e\n\n\u003ctd class=\"even-column odd-row second-column\" style=\"width: 33.75%;\"\u003eCinquième génération\u003c\/td\u003e\n\n\u003ctd class=\"odd-column odd-row third-column\" style=\"width: 28.0357%;\"\u003eCinquième génération\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003cth scope=\"row\" class=\"first-column   odd-column even-row\" style=\"width: 37.5%;\"\u003eCommutateur NVIDIA NVLink™\u003c\/th\u003e\n\n\u003ctd class=\"even-column even-row second-column\" style=\"width: 33.75%;\"\u003eCommutateur NVLink 5\u003c\/td\u003e\n\n\u003ctd class=\"odd-column even-row third-column\" style=\"width: 28.0357%;\"\u003eCommutateur NVLink 5\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003cth scope=\"row\" class=\"first-column   odd-column odd-row\" style=\"width: 37.5%;\"\u003eBande passante NVLink GPU-à-GPU\u003c\/th\u003e\n\n\u003ctd class=\"even-column odd-row second-column\" style=\"width: 33.75%;\"\u003e1,8 To\/s\u003c\/td\u003e\n\n\u003ctd class=\"odd-column odd-row third-column\" style=\"width: 28.0357%;\"\u003e1,8 To\/s\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003cth scope=\"row\" class=\"first-column   odd-column even-row\" style=\"width: 37.5%;\"\u003eBande passante NVLink totale\u003c\/th\u003e\n\n\u003ctd class=\"even-column even-row second-column\" style=\"width: 33.75%;\"\u003e14,4 To\/s\u003c\/td\u003e\n\n\u003ctd class=\"odd-column even-row third-column\" style=\"width: 28.0357%;\"\u003e14,4 To\/s\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003cth scope=\"row\" class=\"first-column   odd-column odd-row\" style=\"width: 37.5%;\"\u003eBande passante réseau\u003c\/th\u003e\n\n\u003ctd class=\"even-column odd-row second-column\" style=\"width: 33.75%;\"\u003e1,6 To\/s\u003c\/td\u003e\n\n\u003ctd class=\"odd-column odd-row third-column\" style=\"width: 28.0357%;\"\u003e0,8 To\/s\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003cth scope=\"row\" class=\"first-column   odd-column even-row\" style=\"width: 37.5%;\"\u003ePerformance d'attention\u003csup\u003e3\u003c\/sup\u003e\n\n\u003c\/th\u003e\n\n\u003ctd class=\"even-column even-row second-column\" style=\"width: 33.75%;\"\u003e2x\u003c\/td\u003e\n\n\u003ctd class=\"odd-column even-row third-column\" style=\"width: 28.0357%;\"\u003e1x\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\n\n\n\u003c\/table\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\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\n\u003cdiv id=\"nv-text-6188745090\" class=\"general-container-text\"\u003e\n\n\u003cdiv class=\"text-left lap-text-left tab-text-left mob-text-left\"\u003e\n\n\u003cdiv class=\"description\"\u003e\n\n\u003cp\u003e1. Spécification en Sparse | Dense\u003cbr\u003e2. Spécification en Sparse. Dense est la ½ spécification sparse affichée.\u003cbr\u003e3. vs NVIDIA Blackwell.\u003cbr\u003e4. HGX B300 et HGX B200 sont désormais disponibles.\u003c\/p\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\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":50526637621484,"sku":null,"price":10999999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0848\/8537\/3164\/files\/B200-1.png?v=1790326133"},{"product_id":"nvidia-b300-192gb-hbm3e-blackwell-gpu-for-ai-2026-graphics-cards-video-cards-nvidia-blackwell-ultra-gpus-hbm3e","title":"NVIDIA B300 192 Go HBM3e Blackwell GPU pour l'IA (2026) Cartes graphiques Cartes vidéo NVIDIA Blackwell Ultra GPUs HBM3E","description":"\u003cp\u003eNVIDIA B300 192 Go HBM3e Blackwell GPU pour l'IA (2026) Cartes graphiques Cartes vidéo NVIDIA Blackwell Ultra GPU 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\n\u003cdiv\u003e\n\n\u003cdiv class=\"nv-table-wrapper\"\u003e\n\n\u003cdiv class=\"nv-table stickyscroll overflow-right allowstickyth sticky-columns-col1\"\u003e\n\n\u003ctable class=\"alt-row-2 even-columns\" style=\"width: 100%;\"\u003e\n\n\u003cthead\u003e\n\n\u003ctr\u003e\n\n\u003cth scope=\"col\" class=\"second-column  even-column odd-row\" style=\"width: 37.5%;\"\u003eHGX B300\u003csup\u003e4\u003c\/sup\u003e\n\n\u003c\/th\u003e\n\n\u003cth scope=\"col\" class=\"third-column odd-column odd-row\" style=\"width: 33.75%;\"\u003eHGX B200\u003csup\u003e4\u003c\/sup\u003e\n\n\u003c\/th\u003e\n\n\n\u003c\/tr\u003e\n\n\n\u003c\/thead\u003e\n\n\u003ctbody\u003e\n\n\u003ctr\u003e\n\n\u003cth scope=\"row\" class=\"first-column   odd-column even-row\" style=\"width: 37.5%;\"\u003eFacteur de forme\u003c\/th\u003e\n\n\u003ctd class=\"even-column even-row second-column\" style=\"width: 33.75%;\"\u003e8x NVIDIA Blackwell Ultra SXM\u003c\/td\u003e\n\n\u003ctd class=\"odd-column even-row third-column\" style=\"width: 28.0357%;\"\u003e8x NVIDIA Blackwell SXM\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003cth scope=\"row\" class=\"first-column   odd-column odd-row\" style=\"width: 37.5%;\"\u003eCœur Tensor FP4\u003csup\u003e1\u003c\/sup\u003e\n\n\u003c\/th\u003e\n\n\u003ctd class=\"even-column odd-row second-column\" style=\"width: 33.75%;\"\u003e144 PFLOPS | 108 PFLOPS\u003c\/td\u003e\n\n\u003ctd class=\"odd-column odd-row third-column\" style=\"width: 28.0357%;\"\u003e144 PFLOPS | 72 PFLOPS\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003cth scope=\"row\" class=\"first-column   odd-column even-row\" style=\"width: 37.5%;\"\u003eCœur Tensor FP8\/FP6\u003csup\u003e2\u003c\/sup\u003e\n\n\u003c\/th\u003e\n\n\u003ctd class=\"even-column even-row second-column\" style=\"width: 33.75%;\"\u003e72 PFLOPS\u003c\/td\u003e\n\n\u003ctd class=\"odd-column even-row third-column\" style=\"width: 28.0357%;\"\u003e72 PFLOPS\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003cth scope=\"row\" class=\"first-column   odd-column odd-row\" style=\"width: 37.5%;\"\u003eCœur Tensor INT8\u003csup\u003e2\u003c\/sup\u003e\n\n\u003c\/th\u003e\n\n\u003ctd class=\"even-column odd-row second-column\" style=\"width: 33.75%;\"\u003e3 POPS\u003c\/td\u003e\n\n\u003ctd class=\"odd-column odd-row third-column\" style=\"width: 28.0357%;\"\u003e72 POPS\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003cth scope=\"row\" class=\"first-column   odd-column even-row\" style=\"width: 37.5%;\"\u003eCœur Tensor FP16\/BF16\u003csup\u003e2\u003c\/sup\u003e\n\n\u003c\/th\u003e\n\n\u003ctd class=\"even-column even-row second-column\" style=\"width: 33.75%;\"\u003e36 PFLOPS\u003c\/td\u003e\n\n\u003ctd class=\"odd-column even-row third-column\" style=\"width: 28.0357%;\"\u003e36 PFLOPS\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003cth scope=\"row\" class=\"first-column   odd-column odd-row\" style=\"width: 37.5%;\"\u003eCœur Tensor TF32\u003csup\u003e2\u003c\/sup\u003e\n\n\u003c\/th\u003e\n\n\u003ctd class=\"even-column odd-row second-column\" style=\"width: 33.75%;\"\u003e18 PFLOPS\u003c\/td\u003e\n\n\u003ctd class=\"odd-column odd-row third-column\" style=\"width: 28.0357%;\"\u003e18 PFLOPS\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003cth scope=\"row\" class=\"first-column   odd-column even-row\" style=\"width: 37.5%;\"\u003eFP32\u003c\/th\u003e\n\n\u003ctd class=\"even-column even-row second-column\" style=\"width: 33.75%;\"\u003e600 TFLOPS\u003c\/td\u003e\n\n\u003ctd class=\"odd-column even-row third-column\" style=\"width: 28.0357%;\"\u003e600 TFLOPS\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003cth scope=\"row\" class=\"first-column   odd-column odd-row\" style=\"width: 37.5%;\"\u003eCœur Tensor FP64\/FP64\u003c\/th\u003e\n\n\u003ctd class=\"even-column odd-row second-column\" style=\"width: 33.75%;\"\u003e10 TFLOPS\u003c\/td\u003e\n\n\u003ctd class=\"odd-column odd-row third-column\" style=\"width: 28.0357%;\"\u003e296 TFLOPS\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003cth scope=\"row\" class=\"first-column   odd-column even-row\" style=\"width: 37.5%;\"\u003eMémoire totale\u003c\/th\u003e\n\n\u003ctd class=\"even-column even-row second-column\" style=\"width: 33.75%;\"\u003e2,1 To\u003c\/td\u003e\n\n\u003ctd class=\"odd-column even-row third-column\" style=\"width: 28.0357%;\"\u003e1,4 To\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003cth scope=\"row\" class=\"first-column   odd-column odd-row\" style=\"width: 37.5%;\"\u003eNVIDIA NVLink\u003c\/th\u003e\n\n\u003ctd class=\"even-column odd-row second-column\" style=\"width: 33.75%;\"\u003eCinquième génération\u003c\/td\u003e\n\n\u003ctd class=\"odd-column odd-row third-column\" style=\"width: 28.0357%;\"\u003eCinquième génération\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003cth scope=\"row\" class=\"first-column   odd-column even-row\" style=\"width: 37.5%;\"\u003eSwitch NVIDIA NVLink™\u003c\/th\u003e\n\n\u003ctd class=\"even-column even-row second-column\" style=\"width: 33.75%;\"\u003eNVLink 5 Switch\u003c\/td\u003e\n\n\u003ctd class=\"odd-column even-row third-column\" style=\"width: 28.0357%;\"\u003eNVLink 5 Switch\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003cth scope=\"row\" class=\"first-column   odd-column odd-row\" style=\"width: 37.5%;\"\u003eBande passante NVLink GPU-à-GPU\u003c\/th\u003e\n\n\u003ctd class=\"even-column odd-row second-column\" style=\"width: 33.75%;\"\u003e1,8 To\/s\u003c\/td\u003e\n\n\u003ctd class=\"odd-column odd-row third-column\" style=\"width: 28.0357%;\"\u003e1,8 To\/s\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003cth scope=\"row\" class=\"first-column   odd-column even-row\" style=\"width: 37.5%;\"\u003eBande passante NVLink totale\u003c\/th\u003e\n\n\u003ctd class=\"even-column even-row second-column\" style=\"width: 33.75%;\"\u003e14,4 To\/s\u003c\/td\u003e\n\n\u003ctd class=\"odd-column even-row third-column\" style=\"width: 28.0357%;\"\u003e14,4 To\/s\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003cth scope=\"row\" class=\"first-column   odd-column odd-row\" style=\"width: 37.5%;\"\u003eBande passante réseau\u003c\/th\u003e\n\n\u003ctd class=\"even-column odd-row second-column\" style=\"width: 33.75%;\"\u003e1,6 To\/s\u003c\/td\u003e\n\n\u003ctd class=\"odd-column odd-row third-column\" style=\"width: 28.0357%;\"\u003e0,8 To\/s\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003cth scope=\"row\" class=\"first-column   odd-column even-row\" style=\"width: 37.5%;\"\u003ePerformance d'attention\u003csup\u003e3\u003c\/sup\u003e\n\n\u003c\/th\u003e\n\n\u003ctd class=\"even-column even-row second-column\" style=\"width: 33.75%;\"\u003e2x\u003c\/td\u003e\n\n\u003ctd class=\"odd-column even-row third-column\" style=\"width: 28.0357%;\"\u003e1x\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\n\n\n\u003c\/table\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\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\n\u003cdiv id=\"nv-text-6188745090\" class=\"general-container-text\"\u003e\n\n\u003cdiv class=\"text-left lap-text-left tab-text-left mob-text-left\"\u003e\n\n\u003cdiv class=\"description\"\u003e\n\n\u003cp\u003e1. Spécification en Sparse | Dense\u003cbr\u003e2. Spécification en Sparse. Dense est ½ de la spécification sparse affichée.\u003cbr\u003e3. vs. NVIDIA Blackwell.\u003cbr\u003e4. HGX B300 et HGX B200 sont actuellement livrés.\u003c\/p\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\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":50526637654252,"sku":null,"price":99999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0848\/8537\/3164\/files\/B300-4.png?v=1790326134"},{"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":"Serveur GPU NVIDIA HGX B300 Cartes graphiques 2,1 To 16 × GPU NVIDIA Blackwell Ultra 270 Go HBM3E 7,7 To\/s","description":"\u003cp\u003eServeur GPU NVIDIA HGX B300 Cartes graphiques Cartes vidéo 2,1 To 16 × GPU NVIDIA Blackwell Ultra 270 Go HBM3E 7,7 To\/s\u003c\/p\u003e\n\u003ctable cellspacing=\"0\" cellpadding=\"0\" class=\"t1\"\u003e\n\n\u003ctbody\u003e\n\n\u003ctr\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003e\u003cb\u003eSpécifications techniques                                                                                 HGX B300\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\n\n\n\u003c\/table\u003e\n\u003ctable cellspacing=\"0\" cellpadding=\"0\" class=\"t1\"\u003e\n\n\u003ctbody\u003e\n\n\u003ctr\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003e \u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003e\u003cmeta charset=\"utf-8\"\u003e\u003cb\u003eGB300 NVL72\u003c\/b\u003e\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p2\"\u003e\u003cb\u003eGPU Blackwell Ultra | CPU Grace\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003e72 | 36\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003e8 | 0\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p2\"\u003e\u003cb\u003eCœurs de CPU\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003e2 592 cœurs Arm Neoverse V2\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003e-\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p2\"\u003e\u003cb\u003eCœur tensoriel FP4 total\u003c\/b\u003e\u003cspan class=\"s1\"\u003e\u003cb\u003e1\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/span\u003e\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003e1 440 PFLOPS | 1 080 PFLOPS\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003e144 PFLOPS | 108 PFLOPS\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p2\"\u003e\u003cb\u003eCœur tensoriel FP8\/FP6 total\u003c\/b\u003e\u003cspan class=\"s1\"\u003e\u003cb\u003e2\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/span\u003e\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003e720 PFLOPS\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003e72 PFLOPS\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p2\"\u003e\u003cb\u003eMémoire rapide totale\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003e37 To\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003e2,1 To\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p2\"\u003e\u003cb\u003eBande passante mémoire totale\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003e576 To\/s\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003e62 To\/s\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p2\"\u003e\u003cb\u003eBande passante totale du commutateur NVLink\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003e130 To\/s\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003e14,4 To\/s\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003e\u003cb\u003eSpécifications individuelles du GPU Blackwell Ultra\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p2\"\u003e\u003cb\u003eCœur tensoriel FP4\u003c\/b\u003e\u003cspan class=\"s1\"\u003e\u003cb\u003e1\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/span\u003e\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003e20 PFLOPS | 15 PFLOPS\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003e18 PFLOPS | 14 PFLOPS\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p2\"\u003e\u003cb\u003eCœur tensoriel FP8\/FP6\u003c\/b\u003e\u003cspan class=\"s1\"\u003e\u003cb\u003e2\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/span\u003e\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003e10 PFLOPS\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003e9 PFLOPS\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p2\"\u003e\u003cb\u003eCœur tensoriel INT8\u003c\/b\u003e\u003cspan class=\"s1\"\u003e\u003cb\u003e2\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/span\u003e\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003e330 TOPS\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003e307 TOPS\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p2\"\u003e\u003cb\u003eCœur tensoriel FP16\/BF16\u003c\/b\u003e\u003cspan class=\"s1\"\u003e\u003cb\u003e2\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/span\u003e\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003e5 PFLOPS\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003e4,5 PFLOPS\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p2\"\u003e\u003cb\u003eCœur tensoriel TF32\u003c\/b\u003e\u003cspan class=\"s1\"\u003e\u003cb\u003e2\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/span\u003e\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003e2,5 PFLOPS\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003e2,2 PFLOPS\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p2\"\u003e\u003cb\u003eFP32\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003e80 TFLOPS\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003e75 TFLOPS\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p2\"\u003e\u003cb\u003eCœur tensoriel FP64\/FP64\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003e1,3 TFLOPS\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003e1,2 TFLOPS\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p2\"\u003e\u003cb\u003eMémoire GPU | Bande passante\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003e279 Go HBM3E | 8 To\/s\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003e270 Go HBM3E | 7,7 To\/s\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p2\"\u003e\u003cb\u003eGPU multi-instances (MIG)\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003e7\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p2\"\u003e\u003cb\u003eMoteur de décompression\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003eOui\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p2\"\u003e\u003cb\u003eDécodeurs\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p3\"\u003e7 NVDEC\u003cspan class=\"s1\"\u003e3\u003c\/span\u003e\u003c\/p\u003e\n\n\u003cp class=\"p1\"\u003e7 nvJPEG\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p2\"\u003e\u003cb\u003ePuissance de conception thermique (TDP) max.\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003eConfigurable jusqu’à 1 400 W\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003eConfigurable jusqu’à 1 100 W\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p2\"\u003e\u003cb\u003eInterconnexion\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p3\"\u003eNVLink de cinquième génération : 1,8 To\/s\u003c\/p\u003e\n\n\u003cp class=\"p1\"\u003ePCIe Gen6 : 256 Go\/s\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p2\"\u003e\u003cb\u003eOptions de serveur\u003c\/b\u003e\u003cb\u003e\u003c\/b\u003e\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003ePartenaire NVIDIA GB300 NVL72 et systèmes certifiés NVIDIA\u003cspan class=\"s2\"\u003e™ \u003c\/span\u003e\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p1\"\u003ePartenaire NVIDIA HGX B300 et systèmes certifiés NVIDIA\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd valign=\"top\" class=\"td1\"\u003e\n\n\u003cp class=\"p4\"\u003e1. Spécification en Sparse | Dense\u003c\/p\u003e\n\n\u003cp class=\"p4\"\u003e2. Spécification en sparse. La spécification Dense est la ½ de la spécification sparse affichée.\u003c\/p\u003e\n\n\u003cp class=\"p4\"\u003e3. Les formats pris en charge offrent les accélérations suivantes par rapport aux GPU H100 : 2x H.264, 1,25x HEVC, 1,25x VP9. AV1\u003c\/p\u003e\n\n\n\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\n\n\n\u003c\/table\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":50526637687020,"sku":null,"price":1399999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0848\/8537\/3164\/files\/B300-3.jpg?v=1790326135"},{"product_id":"nvidia-b200-specs-b200-sxm6-hmb3e-192gb-blackwell-gpu-for-ai-2026-video-card","title":"NVIDIA B200 Specs B200 SXM6 HBM3E 192GB Blackwell GPU pour IA (2026) Carte graphique","description":"\u003cp\u003eSpécifications du NVIDIA B200 B200 SXM6 HMB3E 192 Go Blackwell GPU pour l'IA (2026) Carte graphique\u003c\/p\u003e\n\u003ch2 class=\"text-3xl font-semibold mt-12 mb-6\"\u003eSpécifications clés du B200\u003c\/h2\u003e\n\u003cdiv class=\"overflow-x-auto my-8\"\u003e\n\n\u003ctable class=\"min-w-full border-collapse\"\u003e\n\n\u003cthead\u003e\n\n\u003ctr\u003e\n\n\u003cth class=\"px-4 py-2 text-left font-semibold\"\u003eSpécification\u003c\/th\u003e\n\n\u003cth class=\"px-4 py-2 text-left font-semibold\"\u003eB200\u003c\/th\u003e\n\n\u003cth class=\"px-4 py-2 text-left font-semibold\"\u003eH200 (pour référence)\u003c\/th\u003e\n\n\u003cth class=\"px-4 py-2 text-left font-semibold\"\u003eH100 (pour référence)\u003c\/th\u003e\n\n\n\u003c\/tr\u003e\n\n\n\u003c\/thead\u003e\n\n\u003ctbody\u003e\n\n\u003ctr\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eArchitecture\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eBlackwell (GB200)\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eHopper\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eHopper\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eTransistors\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003e208 milliards\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003e80 milliards\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003e80 milliards\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eMémoire\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003e192 Go HBM3e\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003e141 Go HBM3e\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003e80 Go HBM3\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eBande passante mémoire\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eJusqu'à 8 To\/s\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003e4,8 To\/s\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003e3,35 To\/s\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eTensor Core FP4\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eOui (natif)\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eNon\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eNon\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eTensor Core FP8\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eOui (2e gén.)\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eOui\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eOui\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eMoteur de transformateur\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003e2e génération\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003e1re génération\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003e1re génération\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eNVLink\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003e5e gén. (1,8 To\/s)\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003e4e gén. (900 Go\/s)\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003e4e gén. (900 Go\/s)\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eTDP\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eJusqu'à 1000W\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eJusqu'à 700W\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eJusqu'à 700W\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eFabrication\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eTSMC 4NP\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eTSMC 4N\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eTSMC 4N\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\n\n\n\u003c\/table\u003e\n\n\n\u003c\/div\u003e\n\u003ch2 class=\"text-3xl font-semibold mt-12 mb-6\"\u003eArchitecture : Ce que Blackwell change\u003c\/h2\u003e\n\u003ch3 class=\"text-xl font-semibold mt-4 mb-2\"\u003eMoteur de transformateur de deuxième génération\u003c\/h3\u003e\n\u003cp class=\"my-6 text-lg leading-relaxed\"\u003eLa plus grande amélioration pour les charges de travail d'IA est le moteur de transformateur de 2e génération avec prise en charge native du FP4 :\u003c\/p\u003e\n\u003cul class=\"text-lg list-disc pl-12\"\u003e\n\n\u003cli class=\"my-1\"\u003e\n\n\u003cstrong\u003ePrécision FP4\u003c\/strong\u003e\u003cspan\u003e \u003c\/span\u003e— point flottant 4 bits pour l'inférence. Réduit de moitié l'utilisation de la mémoire par rapport au FP8, permettant des modèles plus grands ou des tailles de lot plus élevées sur un seul GPU\u003c\/li\u003e\n\n\u003cli class=\"my-1\"\u003e\n\n\u003cstrong\u003eGestion dynamique de la précision\u003c\/strong\u003e\u003cspan\u003e \u003c\/span\u003e— bascule automatiquement entre FP4, FP8 et FP16 en fonction des besoins de chaque couche\u003c\/li\u003e\n\n\u003cli class=\"my-1\"\u003e\n\n\u003cstrong\u003eDébit plus élevé\u003c\/strong\u003e\u003cspan\u003e \u003c\/span\u003e— combiné aux améliorations architecturales, NVIDIA affirme une performance d'inférence jusqu'à 4 fois supérieure à celle du H100\u003c\/li\u003e\n\n\n\u003c\/ul\u003e\n\u003cp class=\"my-6 text-lg leading-relaxed\"\u003eLe FP4 est particulièrement efficace pour l'inférence LLM. Un modèle qui nécessite 80 Go en FP8 sur H100 n'aurait besoin que d'environ 40 Go en FP4 sur B200, laissant plus de 150 Go libres pour le cache KV et le batching.\u003c\/p\u003e\n\u003ch3 class=\"text-xl font-semibold mt-4 mb-2\"\u003eMémoire HBM3e de 192 Go\u003c\/h3\u003e\n\u003cp class=\"my-6 text-lg leading-relaxed\"\u003eLe saut de mémoire est massif :\u003c\/p\u003e\n\u003cdiv class=\"overflow-x-auto my-8\"\u003e\n\n\u003ctable class=\"min-w-full border-collapse\"\u003e\n\n\u003cthead\u003e\n\n\u003ctr\u003e\n\n\u003cth class=\"px-4 py-2 text-left font-semibold\"\u003eGPU\u003c\/th\u003e\n\n\u003cth class=\"px-4 py-2 text-left font-semibold\"\u003eMémoire\u003c\/th\u003e\n\n\u003cth class=\"px-4 py-2 text-left font-semibold\"\u003eBande passante mémoire\u003c\/th\u003e\n\n\n\u003c\/tr\u003e\n\n\n\u003c\/thead\u003e\n\n\u003ctbody\u003e\n\n\u003ctr\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eH100\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003e80 Go HBM3\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003e3,35 To\/s\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eH200\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003e141 Go HBM3e\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003e4,8 To\/s\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eB200\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003e192 Go HBM3e\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eJusqu'à 8 To\/s\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\n\n\n\u003c\/table\u003e\n\n\n\u003c\/div\u003e\n\u003cp class=\"my-6 text-lg leading-relaxed\"\u003e192 Go signifie :\u003c\/p\u003e\n\u003cul class=\"text-lg list-disc pl-12\"\u003e\n\n\u003cli class=\"my-1\"\u003e\n\n\u003cstrong\u003eLlama 70B en FP16\u003c\/strong\u003e\u003cspan\u003e \u003c\/span\u003etient sur un seul GPU (140 Go) avec 52 Go de libre pour le cache KV\u003c\/li\u003e\n\n\u003cli class=\"my-1\"\u003e\n\n\u003cstrong\u003eLlama 70B en FP8\u003c\/strong\u003e\u003cspan\u003e \u003c\/span\u003ene nécessite qu'environ 70 Go, laissant 122 Go pour des tailles de lot massives\u003c\/li\u003e\n\n\u003cli class=\"my-1\"\u003e\n\n\u003cstrong\u003eLlama 405B en FP4\u003c\/strong\u003e\u003cspan\u003e \u003c\/span\u003epourrait potentiellement tenir sur 2 B200\u003c\/li\u003e\n\n\u003cli class=\"my-1\"\u003e\n\n\u003cstrong\u003ePlusieurs modèles\u003c\/strong\u003e\u003cspan\u003e \u003c\/span\u003eservis simultanément à partir d'un seul GPU\u003c\/li\u003e\n\n\n\u003c\/ul\u003e\n\u003ch3 class=\"text-xl font-semibold mt-4 mb-2\"\u003eNVLink 5e génération\u003c\/h3\u003e\n\u003cp class=\"my-6 text-lg leading-relaxed\"\u003eLa bande passante NVLink double, passant de 900 Go\/s (H100\/H200) à 1,8 To\/s par GPU. Pour l'entraînement multi-GPU, cela signifie :\u003c\/p\u003e\n\u003cul class=\"text-lg list-disc pl-12\"\u003e\n\n\u003cli class=\"my-1\"\u003eSynchronisation plus rapide des gradients lors de l'entraînement distribué\u003c\/li\u003e\n\n\u003cli class=\"my-1\"\u003eParallélisme tensoriel plus efficace pour l'inférence de grands modèles\u003c\/li\u003e\n\n\u003cli class=\"my-1\"\u003eMeilleure efficacité de mise à l'échelle lors de l'utilisation de 4 à 8 GPU par nœud\u003c\/li\u003e\n\n\n\u003c\/ul\u003e\n\u003ch3 class=\"text-xl font-semibold mt-4 mb-2\"\u003eGB200 et NVL72\u003c\/h3\u003e\n\u003cp class=\"my-6 text-lg leading-relaxed\"\u003eNVIDIA propose également le B200 dans des systèmes préconfigurés à l'échelle du rack :\u003c\/p\u003e\n\u003cul class=\"text-lg list-disc pl-12\"\u003e\n\n\u003cli class=\"my-1\"\u003e\n\n\u003cstrong\u003eGB200\u003c\/strong\u003e\u003cspan\u003e \u003c\/span\u003e— un module de calcul avec 2 GPU B200 + 1 CPU Grace, connectés via NVLink\u003c\/li\u003e\n\n\u003cli class=\"my-1\"\u003e\n\n\u003cstrong\u003eGB200 NVL72\u003c\/strong\u003e\u003cspan\u003e \u003c\/span\u003e— un rack complet avec 36 CPU Grace et 72 GPU B200 interconnectés via NVLink, offrant 720 pétaFLOPS de calcul FP4\u003c\/li\u003e\n\n\n\u003c\/ul\u003e\n\u003cp class=\"my-6 text-lg leading-relaxed\"\u003eCeux-ci sont conçus pour l'entraînement et l'inférence à grande échelle au niveau du centre de données.\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\"\u003ePour l'inférence LLM\u003c\/h3\u003e\n\u003cdiv class=\"overflow-x-auto my-8\"\u003e\n\n\u003ctable class=\"min-w-full border-collapse\"\u003e\n\n\u003cthead\u003e\n\n\u003ctr\u003e\n\n\u003cth class=\"px-4 py-2 text-left font-semibold\"\u003eMétrique\u003c\/th\u003e\n\n\u003cth class=\"px-4 py-2 text-left font-semibold\"\u003eB200\u003c\/th\u003e\n\n\u003cth class=\"px-4 py-2 text-left font-semibold\"\u003eH200\u003c\/th\u003e\n\n\u003cth class=\"px-4 py-2 text-left font-semibold\"\u003eH100\u003c\/th\u003e\n\n\n\u003c\/tr\u003e\n\n\n\u003c\/thead\u003e\n\n\u003ctbody\u003e\n\n\u003ctr\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eLlama 70B (FP8) jetons\/sec\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003e~4x H100*\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003e~1.9x H100\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003e1x (base)\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eMémoire pour Llama 70B FP8\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003e70 Go (122 Go libres)\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003e70 Go (71 Go libres)\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003e70 Go (10 Go libres)\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eMémoire pour Llama 70B FP4\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003e~35 Go (157 Go libres)\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eN\/A (pas de FP4)\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eN\/A (pas de FP4)\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eTaille de lot maximale (70B FP8)\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eTrès grande\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003eModérée\u003c\/td\u003e\n\n\u003ctd class=\"px-4 py-2\"\u003ePetite\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\n\n\n\u003c\/table\u003e\n\n\n\u003c\/div\u003e\n\u003cp class=\"my-6 text-lg leading-relaxed\"\u003e*Affirmations publiées par NVIDIA. Les performances réelles varieront selon l'implémentation et la charge de travail.\u003c\/p\u003e\n\u003cp class=\"my-6 text-lg leading-relaxed\"\u003eLa combinaison du B200, avec plus de mémoire, une bande passante plus élevée et la prise en charge du FP4, pourrait rendre le service de modèles 70B sur un seul GPU pratique à l'échelle — ce qui est serré sur le H100 et confortable mais pas optimal sur le H200.\u003c\/p\u003e\n\u003ch3 class=\"text-xl font-semibold mt-4 mb-2\"\u003ePour l'entraînement\u003c\/h3\u003e\n\u003cp class=\"my-6 text-lg leading-relaxed\"\u003eNVIDIA revendique des performances d'entraînement jusqu'à 4 fois supérieures sur les modèles de classe GPT par rapport au H100, principalement grâce à :\u003c\/p\u003e\n\u003cul class=\"text-lg list-disc pl-12\"\u003e\n\n\u003cli class=\"my-1\"\u003eUn débit Tensor Core plus élevé\u003c\/li\u003e\n\n\u003cli class=\"my-1\"\u003eDes améliorations de l'entraînement FP8 (Moteur de transformateur de 2e génération)\u003c\/li\u003e\n\n\u003cli class=\"my-1\"\u003eUne bande passante NVLink doublée pour une meilleure mise à l'échelle multi-GPU\u003c\/li\u003e\n\n\u003cli class=\"my-1\"\u003ePlus de mémoire réduisant le besoin de techniques d'optimisation de la mémoire\u003c\/li\u003e\n\n\n\u003c\/ul\u003e\n\u003cp class=\"my-6 text-lg leading-relaxed\"\u003ePour l'entraînement de grands modèles, le B200 pourrait réduire le temps (et le coût) d'entraînement de 3 à 4 fois par rapport au H100, en supposant que la pile logicielle utilise pleinement les nouvelles fonctionnalités matérielles.\u003c\/p\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":50526637719788,"sku":null,"price":89999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0848\/8537\/3164\/files\/B200_c3851d04-b413-4993-b640-3d91fc5a5e62.png?v=1790326136"},{"product_id":"nvidia-tesla-h100-80g-1-sxm5-module-graphics-cards-video-cards-gpu-module","title":"Nvidia Tesla H100 80G*1 module SXM5 Cartes graphiques cartes vidéo module GPU","description":"\u003cp\u003e\u003cmeta charset=\"utf-8\"\u003eModule GPU pour cartes graphiques vidéo Nvidia Tesla H100 80G SXM5\u003cbr\u003e\u003c\/p\u003e\n\u003ctable cellpadding=\"0\" cellspacing=\"0\" width=\"840\"\u003e\n\n\u003cthead\u003e\n\n\u003ctr class=\"firstRow\"\u003e\n\n\u003cth\u003eNom\u003c\/th\u003e\n\n\u003cth\u003eH100 SXM\u003c\/th\u003e\n\n\u003cth\u003eH100 PCIe\u003c\/th\u003e\n\n\n\u003c\/tr\u003e\n\n\n\u003c\/thead\u003e\n\n\u003ctbody\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eFP64\u003c\/td\u003e\n\n\u003ctd\u003e34 téraFLOPS\u003c\/td\u003e\n\n\u003ctd\u003e26 téraFLOPS\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eCœur Tensor FP64\u003c\/td\u003e\n\n\u003ctd\u003e67 téraFLOPS\u003c\/td\u003e\n\n\u003ctd\u003e51 téraFLOPS\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eFP32\u003c\/td\u003e\n\n\u003ctd\u003e67 téraFLOPS\u003c\/td\u003e\n\n\u003ctd\u003e51 téraFLOPS\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eCœur Tensor TF32\u003c\/td\u003e\n\n\u003ctd\u003e989 téraFLOPS*\u003c\/td\u003e\n\n\u003ctd\u003e756 téraFLOPS*\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eCœur Tensor BFLOAT16\u003c\/td\u003e\n\n\u003ctd\u003e1979 téraFLOPS*\u003c\/td\u003e\n\n\u003ctd\u003e1 513 téraFLOPS*\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eCœur Tensor FP16\u003c\/td\u003e\n\n\u003ctd\u003e1979 téraFLOPS*\u003c\/td\u003e\n\n\u003ctd\u003e1 513 téraFLOPS*\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eCœur Tensor FP8\u003c\/td\u003e\n\n\u003ctd\u003e3958 téraFLOPS*\u003c\/td\u003e\n\n\u003ctd\u003e3026 téraFLOPS*\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eCœur Tensor INT8\u003c\/td\u003e\n\n\u003ctd\u003e3958 TOPS*\u003c\/td\u003e\n\n\u003ctd\u003e3026 TOPS*\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eMémoire GPU\u003c\/td\u003e\n\n\u003ctd\u003e80 Go\u003c\/td\u003e\n\n\u003ctd\u003e80 Go\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eLargeur de bande passante GPU\u003c\/td\u003e\n\n\u003ctd\u003e3.35 To\/s\u003c\/td\u003e\n\n\u003ctd\u003e2 To\/s\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003e\u003cbr\u003e\u003c\/td\u003e\n\n\u003ctd\u003e7 NVDEC\u003cbr\u003e7 JPEG\u003c\/td\u003e\n\n\u003ctd\u003e7 NVDEC\u003cbr\u003e7 JPEG\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eTDP\u003c\/td\u003e\n\n\u003ctd\u003e700 W\u003c\/td\u003e\n\n\u003ctd\u003e300-350 W\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\n\n\n\u003c\/table\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":50526637752556,"sku":null,"price":79000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0848\/8537\/3164\/files\/H100SXM5GPU.png?v=1790326137"},{"product_id":"nvidia-tesla-h100-80g-8-sxm5-module-graphics-cards-video-cards-gpu-module","title":"Module GPU cartes graphiques Nvidia Tesla H100 80G*8 SXM5","description":"\u003cp\u003e\u003cmeta charset=\"utf-8\"\u003eModule GPU pour cartes graphiques Nvidia Tesla H100 80G*8 SXM5\u003cbr\u003e\u003c\/p\u003e\n\u003ctable cellpadding=\"0\" cellspacing=\"0\" width=\"840\"\u003e\n\n\u003cthead\u003e\n\n\u003ctr class=\"firstRow\"\u003e\n\n\u003cth\u003eNom\u003c\/th\u003e\n\n\u003cth\u003eH100 SXM\u003c\/th\u003e\n\n\u003cth\u003eH100 PCIe\u003c\/th\u003e\n\n\n\u003c\/tr\u003e\n\n\n\u003c\/thead\u003e\n\n\u003ctbody\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eFP64\u003c\/td\u003e\n\n\u003ctd\u003e34 téraFLOPS\u003c\/td\u003e\n\n\u003ctd\u003e26 téraFLOPS\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eCœur Tensor FP64\u003c\/td\u003e\n\n\u003ctd\u003e67 téraFLOPS\u003c\/td\u003e\n\n\u003ctd\u003e51 téraFLOPS\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eFP32\u003c\/td\u003e\n\n\u003ctd\u003e67 téraFLOPS\u003c\/td\u003e\n\n\u003ctd\u003e51 téraFLOPS\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eCœur Tensor TF32\u003c\/td\u003e\n\n\u003ctd\u003e989 téraFLOPS*\u003c\/td\u003e\n\n\u003ctd\u003e756 téraFLOPS*\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eCœur Tensor BFLOAT16\u003c\/td\u003e\n\n\u003ctd\u003e1979 téraFLOPS*\u003c\/td\u003e\n\n\u003ctd\u003e1513 téraFLOPS*\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eCœur Tensor FP16\u003c\/td\u003e\n\n\u003ctd\u003e1979 téraFLOPS*\u003c\/td\u003e\n\n\u003ctd\u003e1513 téraFLOPS*\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eCœur Tensor FP8\u003c\/td\u003e\n\n\u003ctd\u003e3958 téraFLOPS*\u003c\/td\u003e\n\n\u003ctd\u003e3026 téraFLOPS*\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eCœur Tensor INT8\u003c\/td\u003e\n\n\u003ctd\u003e3958 TOPS*\u003c\/td\u003e\n\n\u003ctd\u003e3026 TOPS*\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eMémoire GPU\u003c\/td\u003e\n\n\u003ctd\u003e80 Go\u003c\/td\u003e\n\n\u003ctd\u003e80 Go\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eLargeur GPU\u003c\/td\u003e\n\n\u003ctd\u003e3,35 To\/s\u003c\/td\u003e\n\n\u003ctd\u003e2 To\/s\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003e\u003cbr\u003e\u003c\/td\u003e\n\n\u003ctd\u003e7 NVDEC\u003cbr\u003e7 JPEG\u003c\/td\u003e\n\n\u003ctd\u003e7 NVDEC\u003cbr\u003e7 JPEG\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eTDP\u003c\/td\u003e\n\n\u003ctd\u003e700 W\u003c\/td\u003e\n\n\u003ctd\u003e300-350 W\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\n\n\n\u003c\/table\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":50526637785324,"sku":null,"price":980000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0848\/8537\/3164\/files\/H100Module.png?v=1790326138"},{"product_id":"nvidia-tesla-h100-80g-pcie-graphics-cards-video-cards","title":"Cartes graphiques Nvidia Tesla H100 80G pcie","description":"\u003cp\u003e\u003cmeta charset=\"utf-8\"\u003e\u003cspan\u003eCarte graphique de calcul GPU NVIDIA Deep Learning Tesla H100 80GB 900-21010-000-000\u003c\/span\u003e\u003c\/p\u003e\n\u003ctable cellpadding=\"0\" cellspacing=\"0\" width=\"840\"\u003e\n\n\u003cthead\u003e\n\n\u003ctr class=\"firstRow\"\u003e\n\n\u003cth\u003eNom\u003c\/th\u003e\n\n\u003cth\u003eH100 SXM\u003c\/th\u003e\n\n\u003cth\u003eH100 PCIe\u003c\/th\u003e\n\n\n\u003c\/tr\u003e\n\n\n\u003c\/thead\u003e\n\n\u003ctbody\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eFP64\u003c\/td\u003e\n\n\u003ctd\u003e34 téraFLOPS\u003c\/td\u003e\n\n\u003ctd\u003e26 téraFLOPS\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eFP64 Tensor Core\u003c\/td\u003e\n\n\u003ctd\u003e67 téraFLOPS\u003c\/td\u003e\n\n\u003ctd\u003e51 téraFLOPS\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eFP32\u003c\/td\u003e\n\n\u003ctd\u003e67 téraFLOPS\u003c\/td\u003e\n\n\u003ctd\u003e51 téraFLOPS\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eTF32 Tensor Core\u003c\/td\u003e\n\n\u003ctd\u003e989 téraFLOPS*\u003c\/td\u003e\n\n\u003ctd\u003e756 téraFLOPS*\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eBFLOAT16 Tensor Core\u003c\/td\u003e\n\n\u003ctd\u003e1979 téraFLOPS*\u003c\/td\u003e\n\n\u003ctd\u003e1 513 téraFLOPS*\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eFP16 Tensor Core\u003c\/td\u003e\n\n\u003ctd\u003e1979 téraFLOPS*\u003c\/td\u003e\n\n\u003ctd\u003e1 513 téraFLOPS*\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eFP8 Tensor Core\u003c\/td\u003e\n\n\u003ctd\u003e3958 téraFLOPS*\u003c\/td\u003e\n\n\u003ctd\u003e3026 téraFLOPS*\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eINT8 Tensor Core\u003c\/td\u003e\n\n\u003ctd\u003e3958 TOPS*\u003c\/td\u003e\n\n\u003ctd\u003e3026 TOPS*\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eMémoire GPU\u003c\/td\u003e\n\n\u003ctd\u003e80GB\u003c\/td\u003e\n\n\u003ctd\u003e80GB\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eLargeur du GPU\u003c\/td\u003e\n\n\u003ctd\u003e3,35 TB\/s\u003c\/td\u003e\n\n\u003ctd\u003e2 TB\/s\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003e\u003cbr\u003e\u003c\/td\u003e\n\n\u003ctd\u003e7 NVDEC\u003cbr\u003e7 JPEG\u003c\/td\u003e\n\n\u003ctd\u003e7 NVDEC\u003cbr\u003e7 JPEG\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr\u003e\n\n\u003ctd\u003eTDP\u003c\/td\u003e\n\n\u003ctd\u003e700W\u003c\/td\u003e\n\n\u003ctd\u003e300-350 W\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\n\n\n\u003c\/table\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":50526637818092,"sku":null,"price":59999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0848\/8537\/3164\/files\/NVIDIATESLAH10080GPCIE_1.jpg?v=1790326139"},{"product_id":"nvidia-l40s-48gb-gddr6-with-ecc-gpu-for-ai-and-graphics-performance-vidoe-card-graphics-cards","title":"Carte graphique Nvidia L40S 48 Go GDDR6 avec ECC GPU pour l'IA et les performances graphiques","description":"\u003cdiv\u003e\u003cspan\u003eAccélérer les charges de travail de nouvelle génération\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003e\u0026gt;IA générative\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003e\u003cspan class=\"\"\u003e\u0026gt;Inférence LLM\u003c\/span\u003e\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003e\u003cspan class=\"\"\u003e\u0026gt;Réglage fin LLM et entraînement de petits modèles\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;Rendu et graphiques 3D\u003c\/span\u003e\u003c\/span\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cspan\u003e\u0026gt;Streaming et contenu vidéo\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":50526637850860,"sku":null,"price":13999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0848\/8537\/3164\/files\/NVIDIATeslaA16_1.jpg?v=1790326140"},{"product_id":"data-center-server-gpu-nvidia-l40-48gb-gpu-for-data-center-video-cards","title":"GPU NVIDIA L40 48 Go pour serveur de centre de données Cartes vidéo pour centre de données","description":"\u003cp class=\"p1\"\u003e\u003ci\u003eAccélérez les charges de travail de nouvelle génération\u003c\/i\u003e\u003c\/p\u003e\n\u003cp class=\"p2\"\u003e\u003cspan class=\"s1\"\u003e\u003ci\u003e\u0026gt; \u003c\/i\u003e\u003c\/span\u003e\u003ci\u003eNVIDIA Omniverse\u003c\/i\u003e\u003cspan class=\"s2\"\u003e\u003ci\u003e™ \u003c\/i\u003e\u003c\/span\u003e\u003ci\u003eEnterprise\u003c\/i\u003e\u003c\/p\u003e\n\u003cp class=\"p2\"\u003e\u003cspan class=\"s1\"\u003e\u003ci\u003e\u0026gt; \u003c\/i\u003e\u003c\/span\u003e\u003ci\u003eRendu et graphiques 3D\u003c\/i\u003e\u003c\/p\u003e\n\u003cp class=\"p2\"\u003e\u003cspan class=\"s1\"\u003e\u003ci\u003e\u0026gt; \u003c\/i\u003e\u003c\/span\u003e\u003ci\u003ePostes de travail virtuels hautes performances avec le logiciel NVIDIA RTX\u003c\/i\u003e\u003cspan class=\"s2\"\u003e\u003ci\u003e™\u003c\/i\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"p2\"\u003e\u003ci\u003eVirtual Workstation (RTX vWS)\u003c\/i\u003e\u003c\/p\u003e\n\u003cp class=\"p2\"\u003e\u003cspan class=\"s1\"\u003e\u003ci\u003e\u0026gt; \u003c\/i\u003e\u003c\/span\u003e\u003ci\u003eFormation IA et science des données\u003c\/i\u003e\u003c\/p\u003e\n\u003cp class=\"p2\"\u003e\u003cspan class=\"s1\"\u003e\u003ci\u003e\u0026gt; \u003c\/i\u003e\u003c\/span\u003e\u003ci\u003eDiffusion en continu et contenu vidéo\u003c\/i\u003e\u003c\/p\u003e\n\u003cp class=\"p2\"\u003e\u003ci\u003eNVIDIA L40 Offre des performances de calcul visuel sans précédent pour le centre de données.\u003c\/i\u003e\u003c\/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"363\" style=\"border-collapse: collapse; width: 516px;\" height=\"1003\"\u003e\n\n\u003ccolgroup\u003e\n\n\u003ccol width=\"292\" style=\"width: 360px;\"\u003e \u003ccol width=\"71\" style=\"width: 156px;\"\u003e \u003c\/colgroup\u003e\n\n\u003ctbody\u003e\n\n\u003ctr height=\"23\" style=\"height: 17.0pt;\"\u003e\n\n\u003ctd height=\"23\" class=\"xl67\" width=\"292\" style=\"height: 17.0pt; width: 219pt;\"\u003eSpécifications techniques*\u003c\/td\u003e\n\n\u003ctd width=\"71\" style=\"width: 53pt;\"\u003e\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" style=\"height: 15.0pt;\"\u003eArchitecture GPU\u003c\/td\u003e\n\n\u003ctd class=\"xl66\"\u003eArchitecture NVIDIA Ada Lovelace\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"23\" style=\"height: 17.0pt;\"\u003e\n\n\u003ctd height=\"23\" class=\"xl67\" style=\"height: 17.0pt;\"\u003eMémoire GPU\u003c\/td\u003e\n\n\u003ctd\u003eGDDR6 48 Go avec ECC\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"23\" style=\"height: 17.0pt;\"\u003e\n\n\u003ctd height=\"23\" class=\"xl68\" style=\"height: 17.0pt;\"\u003eBande passante mémoire\u003c\/td\u003e\n\n\u003ctd\u003e864 Go\/s\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"23\" style=\"height: 17.0pt;\"\u003e\n\n\u003ctd height=\"23\" class=\"xl67\" style=\"height: 17.0pt;\"\u003eInterface d’interconnexion\u003c\/td\u003e\n\n\u003ctd class=\"xl66\"\u003ePCIe Gen4x16 : 64 Go\/s bidirectionnel\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"48\" style=\"height: 36.0pt;\"\u003e\n\n\u003ctd height=\"48\" class=\"xl70\" width=\"292\" style=\"height: 36.0pt; width: 219pt;\"\u003eCœurs CUDA basés sur l’architecture NVIDIA Ada Lovelace\u003c\/td\u003e\n\n\u003ctd class=\"xl69\" align=\"right\"\u003e18 176\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"23\" style=\"height: 17.0pt;\"\u003e\n\n\u003ctd height=\"23\" class=\"xl67\" style=\"height: 17.0pt;\"\u003eCœurs RT NVIDIA de troisième génération\u003c\/td\u003e\n\n\u003ctd class=\"xl66\" align=\"right\"\u003e142\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"23\" style=\"height: 17.0pt;\"\u003e\n\n\u003ctd height=\"23\" class=\"xl68\" style=\"height: 17.0pt;\"\u003eCœurs Tensor NVIDIA de quatrième génération\u003c\/td\u003e\n\n\u003ctd class=\"xl66\" align=\"right\"\u003e586\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"23\" style=\"height: 17.0pt;\"\u003e\n\n\u003ctd height=\"23\" class=\"xl67\" style=\"height: 17.0pt;\"\u003ePerformances des cœurs RT TFLOPS\u003c\/td\u003e\n\n\u003ctd class=\"xl66\" align=\"right\"\u003e209\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"23\" style=\"height: 17.0pt;\"\u003e\n\n\u003ctd height=\"23\" class=\"xl68\" style=\"height: 17.0pt;\"\u003eFP32 TFLOPS\u003c\/td\u003e\n\n\u003ctd class=\"xl66\" align=\"right\"\u003e90.5\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"23\" style=\"height: 17.0pt;\"\u003e\n\n\u003ctd height=\"23\" class=\"xl67\" style=\"height: 17.0pt;\"\u003eTF32 Tensor Core TFLOPS\u003c\/td\u003e\n\n\u003ctd class=\"xl66\"\u003e90.5 | 181**\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"23\" style=\"height: 17.0pt;\"\u003e\n\n\u003ctd height=\"23\" class=\"xl68\" style=\"height: 17.0pt;\"\u003eBFLOAT16 Tensor Core TFLOPS\u003c\/td\u003e\n\n\u003ctd class=\"xl66\"\u003e181.05 | 362.1**\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"23\" style=\"height: 17.0pt;\"\u003e\n\n\u003ctd height=\"23\" class=\"xl67\" style=\"height: 17.0pt;\"\u003eFP16 Tensor Core\u003c\/td\u003e\n\n\u003ctd class=\"xl66\"\u003e181.05 | 362.1**\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"23\" style=\"height: 17.0pt;\"\u003e\n\n\u003ctd height=\"23\" class=\"xl68\" style=\"height: 17.0pt;\"\u003eFP8 Tensor Core\u003c\/td\u003e\n\n\u003ctd class=\"xl66\"\u003e362 | 724**\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"23\" style=\"height: 17.0pt;\"\u003e\n\n\u003ctd height=\"23\" class=\"xl67\" style=\"height: 17.0pt;\"\u003eTOPS Tensor INT8 de pointe\u003c\/td\u003e\n\n\u003ctd class=\"xl67\"\u003e362 | 724**\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"23\" style=\"height: 17.0pt;\"\u003e\n\n\u003ctd height=\"23\" class=\"xl68\" style=\"height: 17.0pt;\"\u003eTOPS Tensor INT4 de pointe\u003c\/td\u003e\n\n\u003ctd class=\"xl68\"\u003e724 | 1448**\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"23\" style=\"height: 17.0pt;\"\u003e\n\n\u003ctd height=\"23\" class=\"xl67\" style=\"height: 17.0pt;\"\u003eFacteur de forme\u003c\/td\u003e\n\n\u003ctd class=\"xl66\"\u003e4,4\" (H) x 10,5\" (L) - double slot\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"23\" style=\"height: 17.0pt;\"\u003e\n\n\u003ctd height=\"23\" class=\"xl68\" style=\"height: 17.0pt;\"\u003ePorts d'affichage\u003c\/td\u003e\n\n\u003ctd class=\"xl66\"\u003e4 x DisplayPort 1.4a\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"23\" style=\"height: 17.0pt;\"\u003e\n\n\u003ctd height=\"23\" class=\"xl68\" style=\"height: 17.0pt;\"\u003eConsommation électrique maximale\u003c\/td\u003e\n\n\u003ctd class=\"xl66\"\u003e300W\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"23\" style=\"height: 17.0pt;\"\u003e\n\n\u003ctd height=\"23\" class=\"xl67\" style=\"height: 17.0pt;\"\u003eConnecteur d'alimentation\u003c\/td\u003e\n\n\u003ctd class=\"xl65\" width=\"71\" style=\"width: 53pt;\"\u003e16 broches\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"23\" style=\"height: 17.0pt;\"\u003e\n\n\u003ctd height=\"23\" class=\"xl68\" style=\"height: 17.0pt;\"\u003eThermique\u003c\/td\u003e\n\n\u003ctd class=\"xl65\" width=\"71\" style=\"width: 53pt;\"\u003ePassif\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"23\" style=\"height: 17.0pt;\"\u003e\n\n\u003ctd height=\"23\" class=\"xl67\" style=\"height: 17.0pt;\"\u003ePrise en charge du logiciel GPU virtuel (vGPU)\u003c\/td\u003e\n\n\u003ctd class=\"xl65\" width=\"71\" style=\"width: 53pt;\"\u003eOui\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"23\" style=\"height: 17.0pt;\"\u003e\n\n\u003ctd height=\"23\" class=\"xl68\" style=\"height: 17.0pt;\"\u003eProfils vGPU pris en charge\u003c\/td\u003e\n\n\u003ctd class=\"xl66\"\u003eVoir le guide de licence GPU virtuel1\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"23\" style=\"height: 17.0pt;\"\u003e\n\n\u003ctd height=\"23\" class=\"xl67\" style=\"height: 17.0pt;\"\u003eNVENC I NVDEC\u003c\/td\u003e\n\n\u003ctd class=\"xl66\"\u003e3x I 3x (Inclut l’encodage et le décodage AV1)\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"21\" style=\"height: 16.0pt;\"\u003e\n\n\u003ctd height=\"21\" class=\"xl65\" width=\"292\" style=\"height: 16.0pt; width: 219pt;\"\u003eDémarrage sécurisé avec racine de confiance\u003c\/td\u003e\n\n\u003ctd class=\"xl65\" width=\"71\" style=\"width: 53pt;\"\u003eoui\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"23\" style=\"height: 17.0pt;\"\u003e\n\n\u003ctd height=\"23\" class=\"xl68\" style=\"height: 17.0pt;\"\u003eCompatible NEBS\u003c\/td\u003e\n\n\u003ctd class=\"xl65\" width=\"71\" style=\"width: 53pt;\"\u003eNiveau 3\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"23\" style=\"height: 17.0pt;\"\u003e\n\n\u003ctd height=\"23\" class=\"xl67\" style=\"height: 17.0pt;\"\u003ePrise en charge MIG\u003c\/td\u003e\n\n\u003ctd class=\"xl65\" width=\"71\" style=\"width: 53pt;\"\u003eNon\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"23\" style=\"height: 17.0pt;\"\u003e\n\n\u003ctd height=\"23\" class=\"xl67\" style=\"height: 17.0pt;\"\u003ePrise en charge NVLink\u003c\/td\u003e\n\n\u003ctd class=\"xl65\" width=\"71\" style=\"width: 53pt;\"\u003eNon\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\n\n\n\u003c\/table\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":50526637883628,"sku":null,"price":12999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0848\/8537\/3164\/files\/NVIDIATeslaA216G_2.jpg?v=1790326141"},{"product_id":"nvidia-l20-48gb-pcie-gpu-graphics-card-vidoe-cards","title":"Carte graphique NVIDIA L20 48 Go PCIe","description":"\u003cp\u003e\u003cmeta charset=\"utf-8\"\u003e\u003cspan\u003eLa L20 est une carte graphique professionnelle de NVIDIA. Basée sur le processus de fabrication 5 nm et le processeur graphique AD102, dans sa variante AD102, la carte prend en charge DirectX 12 Ultimate. Le processeur graphique AD102 est une puce de grande taille avec une surface de die de 609 mm² et 76 300 millions de transistors. Contrairement au TITAN Ada entièrement débloqué, qui utilise le même GPU mais avec tous les 18432 shaders activés, NVIDIA a désactivé certaines unités de shading sur la L20 pour atteindre le nombre de shaders cible du produit. Elle dispose de 11776 unités de shading, 368 unités de mappage de textures et 128 ROPs. Sont également inclus 368 Tensor Cores qui améliorent la vitesse des applications d'apprentissage automatique avec la prise en charge des formats de données INT4, INT8 et FP8. La carte dispose également de 92 cœurs d'accélération du ray tracing. NVIDIA a associé 48 Go de mémoire GDDR6 à la L20, connectés via une interface mémoire de 384 bits. Le GPU fonctionne à une fréquence de 1440 MHz, qui peut être augmentée jusqu'à 2520 MHz, la mémoire fonctionne à 2250 MHz (18 Gbps effectifs).\u003c\/span\u003e\u003cbr\u003e\u003cspan\u003eÉtant une carte à double emplacement, la NVIDIA L20 tire son alimentation d'un connecteur d'alimentation 16 broches, avec une consommation électrique maximale de 275 W. Les sorties d'affichage incluent : 4x DisplayPort 1.4a. La L20 est connectée au reste du système à l'aide d'une interface PCI-Express 4.0 x16. La carte dispose d'une solution de refroidissement à double emplacement.\u003c\/span\u003e\u003c\/p\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Default Title","offer_id":50526637916396,"sku":null,"price":6999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0848\/8537\/3164\/files\/TeslaL4048G_2.jpg?v=1790326142"},{"product_id":"nvidia-l4-tensor-core-video-cards-graohics-cards-gpu-memory-24gb-gpu-memory-bandwidth-300-gb-s-1","title":"Cartes vidéo NVIDIA L4 Tensor Core Cartes graphiques Mémoire GPU 24 Go Bande passante mémoire GPU 300 Go\/s","description":"\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"292\" style=\"border-collapse: collapse; width: 219pt;\"\u003e\n\n\u003ccolgroup\u003e\n\n\u003ccol width=\"292\" style=\"mso-width-source: userset; mso-width-alt: 9344; width: 219pt;\"\u003e \u003c\/colgroup\u003e\n\n\u003ctbody\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" class=\"xl65\" width=\"292\" style=\"height: 15.0pt; width: 219pt;\"\u003eSpécifications\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" class=\"xl65\" width=\"292\" style=\"height: 15.0pt; width: 219pt;\"\u003eFP32 30,3 téraFLOPS\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" class=\"xl65\" width=\"292\" style=\"height: 15.0pt; width: 219pt;\"\u003eCœur Tensor TF32 120 téraFLOPS*\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" class=\"xl65\" width=\"292\" style=\"height: 15.0pt; width: 219pt;\"\u003eCœur Tensor FP16 242 téraFLOPS*\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" class=\"xl65\" width=\"292\" style=\"height: 15.0pt; width: 219pt;\"\u003eCœur Tensor BFLOAT16 242 téraFLOPS*\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" class=\"xl65\" width=\"292\" style=\"height: 15.0pt; width: 219pt;\"\u003eCœur Tensor FP8 485 téraFLOPS*\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" class=\"xl65\" width=\"292\" style=\"height: 15.0pt; width: 219pt;\"\u003eCœur Tensor INT8 485 TOPS*\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" class=\"xl65\" width=\"292\" style=\"height: 15.0pt; width: 219pt;\"\u003eMémoire GPU 24 Go\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" class=\"xl65\" width=\"292\" style=\"height: 15.0pt; width: 219pt;\"\u003eBande passante mémoire GPU 300 Go\/s\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" class=\"xl65\" width=\"292\" style=\"height: 15.0pt; width: 219pt;\"\u003eNVENC | NVDEC | Décodeurs JPEG 2 | 4 | 4\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" class=\"xl65\" width=\"292\" style=\"height: 15.0pt; width: 219pt;\"\u003ePuissance de conception thermique maximale (TDP) 72 W\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" class=\"xl65\" width=\"292\" style=\"height: 15.0pt; width: 219pt;\"\u003eFacteur de forme PCIe à profil bas 1 slot\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" class=\"xl65\" width=\"292\" style=\"height: 15.0pt; width: 219pt;\"\u003eInterconnexion PCIe Gen4 x16 64 Go\/s\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" class=\"xl65\" width=\"292\" style=\"height: 15.0pt; width: 219pt;\"\u003eOptions de serveur Systèmes partenaires et certifiés NVIDIA avec 1 à 8 GPU\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" class=\"xl65\" width=\"292\" style=\"height: 15.0pt; width: 219pt;\"\u003e*Indiqué avec la parcimonie. Les spécifications sont réduites de moitié sans parcimonie.\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\n\n\n\u003c\/table\u003e\n\u003cp\u003eAccélérer les charges de travail vidéo, d'IA et graphiques Le GPU NVIDIA Ada Lovelace L4 Tensor Core offre une accélération universelle et une efficacité énergétique pour les applications vidéo, d'IA, de bureaux virtualisés et graphiques dans l'entreprise, dans le cloud et en périphérie. Grâce à la plateforme d'IA de NVIDIA et à son approche full-stack, le L4 est optimisé pour l'inférence à grande échelle pour un large éventail d'applications d'IA, y compris les recommandations, les assistants d'avatar d'IA basés sur la voix, l'IA générative, la recherche visuelle et l'automatisation des centres de contact afin d'offrir les meilleures expériences personnalisées. En tant qu'accélérateur NVIDIA le plus efficace pour une utilisation courante, les serveurs équipés du L4 offrent des performances vidéo d'IA jusqu'à 120 fois supérieures à celles des solutions CPU et des performances d'IA générative 2,5 fois supérieures, ainsi que des performances graphiques plus de 4 fois supérieures à celles de la génération précédente de GPU. La polyvalence et le facteur de forme économe en énergie, à un seul slot et à profil bas du NVIDIA L4 en font un outil idéal pour les déploiements mondiaux, y compris les sites périphériques.\u003c\/p\u003e\n\u003cp\u003eSpécifications FP32 30,3 téraFLOPS Cœur Tensor TF32 120 téraFLOPS* Cœur Tensor FP16 242 téraFLOPS* Cœur Tensor BFLOAT16 242 téraFLOPS* Cœur Tensor FP8 485 téraFLOPS* Cœur Tensor INT8 485 TOPS* Mémoire GPU 24 Go Bande passante mémoire GPU 300 Go\/s NVENC | NVDEC | Décodeurs JPEG 2 | 4 | 4 Puissance de conception thermique maximale (TDP) 72 W Facteur de forme PCIe à profil bas 1 slot Interconnexion PCIe Gen4 x16 64 Go\/s Options de serveur Systèmes partenaires et certifiés NVIDIA avec 1 à 8 GPU\u003c\/p\u003e","brand":"Neobitti Inc. 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In the field of artificial intelligence","offers":[{"title":"Gigabyte RTX5060 8G WINDFORCE OC","offer_id":50526651220204,"sku":null,"price":450.0,"currency_code":"USD","in_stock":true},{"title":"Gigabyte RTX5060 8G EAGLE OC","offer_id":50526651252972,"sku":null,"price":454.0,"currency_code":"USD","in_stock":true},{"title":"Gigabyte RTX5060 8G EAGLE ICE OC","offer_id":50526651285740,"sku":null,"price":470.0,"currency_code":"USD","in_stock":true},{"title":"Gigabyte RTX5060 8G GAMING OC","offer_id":50526651318508,"sku":null,"price":487.0,"currency_code":"USD","in_stock":true},{"title":"Gigabyte RTX5060 8G AERO OC","offer_id":50526651351276,"sku":null,"price":504.0,"currency_code":"USD","in_stock":true},{"title":"Gigabyte RTX5060 8G elite","offer_id":50526651384044,"sku":null,"price":529.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0848\/8537\/3164\/files\/GeForceRTX_5060WINDFORCEOC8G.webp?v=1790326183"},{"product_id":"colorful-igame-geforce-graphics-card-rtx-5080-vulcan-oc-16gb-v-neptune-oc-16gb-v-advanced-oc-16gb-v-ultra-oc-16gb-v","title":"Carte Graphique Colorful iGame GeForce RTX 5080 Vulcan OC 16GB-V Neptune OC 16GB-V Advanced OC 16GB-V Ultra OC 16GB-V","description":"\u003cp\u003e\u003cmeta charset=\"utf-8\"\u003eiGame GeForce RTX 5080 Vulcan OC 16GB-V\u003cbr\u003eiGame GeForce RTX 5080 Vulcan W OC 16GB-V\u003cbr\u003eiGame GeForce RTX 5080 Neptune OC 16GB-V\u003cbr\u003eiGame GeForce RTX 5080 Advanced OC 16GB-V\u003cbr\u003eiGame GeForce RTX 5080 Ultra OC 16GB-V\u003cbr\u003eiGame GeForce RTX 5080 Ultra W OC 16GB-V\u003c\/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"284\" style=\"border-collapse: collapse; width: 213pt;\"\u003e\n\n\u003ccolgroup\u003e\n\n\u003ccol width=\"213\" style=\"mso-width-source: userset; mso-width-alt: 6826; width: 160pt;\"\u003e \u003ccol width=\"71\" style=\"width: 53pt;\"\u003e \u003c\/colgroup\u003e\n\n\u003ctbody\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" width=\"213\" style=\"height: 15.0pt; width: 160pt;\"\u003eMarque\/Modèle\/série\/version\u003c\/td\u003e\n\n\u003ctd width=\"71\" style=\"width: 53pt;\"\u003eEXW USD\/pièce\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" style=\"height: 15.0pt;\"\u003eColorful RTX5080 16G ULTRA W OC\u003c\/td\u003e\n\n\u003ctd align=\"right\"\u003e1877\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" style=\"height: 15.0pt;\"\u003eColorful RTX5080 16G AD OC\u003c\/td\u003e\n\n\u003ctd align=\"right\"\u003e2034\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" style=\"height: 15.0pt;\"\u003eColorful RTX5080 16G Vulcan OC\u003c\/td\u003e\n\n\u003ctd align=\"right\"\u003e2284\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" style=\"height: 15.0pt;\"\u003eColorful RTX5080 16G Neptune OC\u003c\/td\u003e\n\n\u003ctd align=\"right\"\u003e2190\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\n\n\n\u003c\/table\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Colorful RTX5080 16G ULTRA W OC","offer_id":50526651416812,"sku":null,"price":1877.0,"currency_code":"USD","in_stock":true},{"title":"Colorful RTX5080 16G AD OC","offer_id":50526651449580,"sku":null,"price":2034.0,"currency_code":"USD","in_stock":true},{"title":"Colorful RTX5080 16G Vulcan OC","offer_id":50526651482348,"sku":null,"price":2284.0,"currency_code":"USD","in_stock":true},{"title":"Colorful RTX5080 16G Neptune OC","offer_id":50526651515116,"sku":null,"price":2190.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0848\/8537\/3164\/files\/iGame_GeForce_RTX_5080_Ultra_W_OC_16GB-V.png?v=1790326184"},{"product_id":"colorful-geforce-rtx-5070-ti-battle-ax-16gb-igame-geforce-rtx-5070-ti-advanced-oc-vulcan-oc-16gb","title":"Colorful GeForce RTX 5070 Ti Battle AX 16Go iGame GeForce RTX 5070 Ti Advanced OC Vulcan OC 16Go","description":"\u003cp\u003e\u003cmeta charset=\"utf-8\"\u003eiGame GeForce RTX 5070 Ti Vulcan OC 16GB-V\u003cbr\u003eiGame GeForce RTX 5070 Ti Advanced OC 16GB-V\u003cbr\u003eiGame GeForce RTX 5070 Ti Ultra W OC SFF 16GB-V\u003cbr\u003eiGame GeForce RTX 5070 Ti Ultra Z BLACK OC 16GB-V\u003cbr\u003eiGame GeForce RTX 5070 Ti Ultra OC SFF 16GB-V\u003cbr\u003eiGame GeForce RTX 5070 Ti Ultra Z OC 16GB-V\u003cbr\u003eiGame GeForce RTX 5070 Ti Ultra W OC 16GB-V\u003cbr\u003eColorful GeForce RTX 5070 Ti Battle AX SFF 16GB-V(Colorful GeForce RTX 5070 Ti NB EX SFF 16GB-V)\u003cbr\u003eColorful GeForce RTX 5070 Ti Battle AX 16GB-V(Colorful GeForce RTX 5070 Ti NB EX 16GB-V)\u003c\/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"299\" style=\"border-collapse: collapse; width: 224pt;\"\u003e\n\n\u003ccolgroup\u003e\n\n\u003ccol width=\"228\" style=\"mso-width-source: userset; mso-width-alt: 7296; width: 171pt;\"\u003e \u003ccol width=\"71\" style=\"width: 53pt;\"\u003e \u003c\/colgroup\u003e\n\n\u003ctbody\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" width=\"228\" style=\"height: 15.0pt; width: 171pt;\"\u003eMarque\/Modèle\/série\/version\u003c\/td\u003e\n\n\u003ctd width=\"71\" style=\"width: 53pt;\"\u003eEXW USD\/pièce\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" style=\"height: 15.0pt;\"\u003eColorful RTX 5070 Ti 16G Battle\u003c\/td\u003e\n\n\u003ctd align=\"right\"\u003e1111\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" style=\"height: 15.0pt;\"\u003eColor RTX 5070TI ULTRA Wite OC\u003c\/td\u003e\n\n\u003ctd align=\"right\"\u003e1189\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" style=\"height: 15.0pt;\"\u003eColor RTX 5070 Ti 16G AD OC\u003c\/td\u003e\n\n\u003ctd align=\"right\"\u003e1221\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" style=\"height: 15.0pt;\"\u003eColor RTX 5070 Ti 16G Vulcan OC\u003c\/td\u003e\n\n\u003ctd align=\"right\"\u003e1455\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\n\n\n\u003c\/table\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Colorful RTX 5070 Ti 16G Battle","offer_id":50526651547884,"sku":null,"price":1111.0,"currency_code":"USD","in_stock":true},{"title":"Colorful RTX 5070TI ULTRA Wite OC","offer_id":50526651580652,"sku":null,"price":1189.0,"currency_code":"USD","in_stock":true},{"title":"Colorful RTX 5070 Ti 16G AD OC","offer_id":50526651613420,"sku":null,"price":1221.0,"currency_code":"USD","in_stock":true},{"title":"Colorful RTX 5070 Ti 16G Vulcan OC","offer_id":50526651646188,"sku":null,"price":1455.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0848\/8537\/3164\/files\/iGameGeForceRTX5070TiAdvancedOC16GB-V.png?v=1790326185"},{"product_id":"colorful-geforce-graphics-cards-rtx-5070-battle-ax-12gb-v2-v-igame-geforce-rtx-5070-ultra-w-oc-12gb-v-rtx-5070-advanced-oc-12gb-v","title":"Cartes graphiques Colorful GeForce RTX 5070 Battle AX 12 Go V2-V iGame GeForce RTX 5070 Ultra W OC 12 Go-V RTX 5070 Advanced OC 12 Go-V","description":"\u003cp\u003e\u003cmeta charset=\"utf-8\"\u003eiGame GeForce RTX 5070 Ultra W OC 12GB-V\u003cbr\u003eColorful GeForce RTX 5070 Gaming 12GB-V\u003cbr\u003eColorful GeForce RTX 5070 Battle AX 12GB V2-V(Colorful GeForce RTX 5070 NB EX 12GB V2-V)\u003cbr\u003eColorful GeForce RTX 5070 Battle AX 12GB-V(Colorful GeForce RTX 5070 NB EX 12GB-V)\u003cbr\u003eiGame GeForce RTX 5070 Ultra Z OC 12GB-V\u003cbr\u003eiGame GeForce RTX 5070 Mini OC 12GB-V\u003cbr\u003eiGame GeForce RTX 5070 Mini W OC 12GB-V\u003cbr\u003eiGame GeForce RTX 5070 Vulcan OC 12GB-V\u003cbr\u003eiGame GeForce RTX 5070 Advanced OC 12GB-V\u003cbr\u003eiGame GeForce RTX 5070 Ultra OC 12GB-V\u003c\/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"299\" style=\"border-collapse: collapse; width: 224pt;\"\u003e\n\n\u003ccolgroup\u003e\n\n\u003ccol width=\"228\" style=\"mso-width-source: userset; mso-width-alt: 7296; width: 171pt;\"\u003e \u003ccol width=\"71\" style=\"width: 53pt;\"\u003e \u003c\/colgroup\u003e\n\n\u003ctbody\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" width=\"228\" style=\"height: 15.0pt; width: 171pt;\"\u003eMarque\/Modèle\/série\/version\u003c\/td\u003e\n\n\u003ctd width=\"71\" style=\"width: 53pt;\"\u003ePrix départ usine USD\/pièce\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" style=\"height: 15.0pt;\"\u003eColorful RTX5070 Battle 12G\u003c\/td\u003e\n\n\u003ctd align=\"right\"\u003e774\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" style=\"height: 15.0pt;\"\u003eColorful RTX5070 Ultra W OC 12G\u003c\/td\u003e\n\n\u003ctd align=\"right\"\u003e817\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" style=\"height: 15.0pt;\"\u003eColorful RTX5070 Advanced OC 12G\u003c\/td\u003e\n\n\u003ctd align=\"right\"\u003e895\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" style=\"height: 15.0pt;\"\u003eColorful RTX5070 Vulcan OC 12G\u003c\/td\u003e\n\n\u003ctd align=\"right\"\u003e999\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\n\n\n\u003c\/table\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Colorful RTX5070 Battle 12 Go","offer_id":50526651678956,"sku":null,"price":774.0,"currency_code":"USD","in_stock":true},{"title":"Colorful RTX5070 Ultra W OC 12G","offer_id":50526651711724,"sku":null,"price":817.0,"currency_code":"USD","in_stock":true},{"title":"Colorful RTX5070 Advanced OC 12G","offer_id":50526651744492,"sku":null,"price":895.0,"currency_code":"USD","in_stock":true},{"title":"Colorful RTX5070 Vulcan OC 12G","offer_id":50526651777260,"sku":null,"price":999.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0848\/8537\/3164\/files\/iGameGeForceRTX5070UltraWOC12GB-V.png?v=1790326187"},{"product_id":"colorful-rtx5060-ti-8gb-16gb-battle-duo-rtx5060-ti-8g-16gb-ultra-w-duo-oc-rtx5060ti-ad-oc-8gb-16gb","title":"Colorful RTX5060 Ti 8 Go 16 Go Battle DUO RTX5060 Ti 8 Go 16 Go ULTRA W DUO OC RTX5060Ti AD OC 8 Go 16 Go","description":"\u003cp\u003eColorful RTX5060 Ti 8GB 16GB Battle DUO RTX5060 Ti 8G 16GB ULTRA W DUO OC RTX5060Ti AD OC 8GB 16GB\u003c\/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"299\" style=\"border-collapse: collapse; width: 354px;\" height=\"350\"\u003e\n\n\u003ccolgroup\u003e\n\n\u003ccol width=\"228\" style=\"width: 254.5px;\"\u003e \u003ccol width=\"71\" style=\"width: 98.5px;\"\u003e \u003c\/colgroup\u003e\n\n\u003ctbody\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" width=\"228\" style=\"height: 15.0pt; width: 171pt;\"\u003eMarque\/Modèle\/série\/version\u003c\/td\u003e\n\n\u003ctd width=\"71\" style=\"width: 53pt;\"\u003eEXW USD\/pièce\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" style=\"height: 15.0pt;\"\u003eColorful RTX5060 Ti 8G Battle DUO\u003c\/td\u003e\n\n\u003ctd align=\"right\"\u003e571\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" style=\"height: 15.0pt;\"\u003eColorful RTX5060 Ti 8G Battle\u003c\/td\u003e\n\n\u003ctd align=\"right\"\u003e588\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" style=\"height: 15.0pt;\"\u003eColorful RTX5060 Ti 8G ULTRA W DUO OC\u003c\/td\u003e\n\n\u003ctd align=\"right\"\u003e588\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" style=\"height: 15.0pt;\"\u003eColorful RTX5060 Ti 8G ULTRA W OC\u003c\/td\u003e\n\n\u003ctd align=\"right\"\u003e596\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" style=\"height: 15.0pt;\"\u003eColorful RTX5060 Ti 8G AD OC\u003c\/td\u003e\n\n\u003ctd align=\"right\"\u003e807\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" style=\"height: 15.0pt;\"\u003eColorful RTX5060Ti Battle DUO 16G\u003c\/td\u003e\n\n\u003ctd align=\"right\"\u003e823\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" style=\"height: 15.0pt;\"\u003eColorful RTX5060Ti Battle 16G\u003c\/td\u003e\n\n\u003ctd align=\"right\"\u003e840\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" style=\"height: 15.0pt;\"\u003eColorful RTX5060Ti Ultra DUO OC 16G\u003c\/td\u003e\n\n\u003ctd align=\"right\"\u003e670\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" style=\"height: 15.0pt;\"\u003eColorful RTX5060Ti Ultra W OC 16G\u003c\/td\u003e\n\n\u003ctd align=\"right\"\u003e874\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" style=\"height: 15.0pt;\"\u003eColorful RTX5060Ti AD OC 16G\u003c\/td\u003e\n\n\u003ctd align=\"right\"\u003e874\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\n\n\n\u003c\/table\u003e","brand":"Neobitti Inc. In the field of artificial intelligence","offers":[{"title":"Colorful RTX5060 Ti 8G Battle DUO","offer_id":50526651810028,"sku":null,"price":571.0,"currency_code":"USD","in_stock":true},{"title":"Colorful RTX5060 Ti 8G Battle","offer_id":50526651842796,"sku":null,"price":588.0,"currency_code":"USD","in_stock":true},{"title":"Colorful RTX5060 Ti 8G ULTRA W DUO OC","offer_id":50526651875564,"sku":null,"price":588.0,"currency_code":"USD","in_stock":true},{"title":"Colorful RTX5060 Ti 8G ULTRA W OC","offer_id":50526651908332,"sku":null,"price":596.0,"currency_code":"USD","in_stock":true},{"title":"Colorful RTX5060 Ti 8G AD OC","offer_id":50526651941100,"sku":null,"price":807.0,"currency_code":"USD","in_stock":true},{"title":"Colorful RTX5060Ti Battle DUO 16 Go","offer_id":50526651973868,"sku":null,"price":823.0,"currency_code":"USD","in_stock":true},{"title":"Colorful RTX5060Ti Battle 16G","offer_id":50526652006636,"sku":null,"price":840.0,"currency_code":"USD","in_stock":true},{"title":"Colorful RTX5060Ti Ultra DUO OC 16G","offer_id":50526652039404,"sku":null,"price":670.0,"currency_code":"USD","in_stock":true},{"title":"Colorful RTX5060Ti Ultra W OC 16G","offer_id":50526652072172,"sku":null,"price":874.0,"currency_code":"USD","in_stock":true},{"title":"Colorful RTX5060Ti AD OC 16G","offer_id":50526652104940,"sku":null,"price":874.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0848\/8537\/3164\/files\/ColorfulGeForceRTX5060TiGamingDUO16GB-V.png?v=1790326188"},{"product_id":"zotac-gaming-geforce-graphics-card-8gb-rtx-5050-low-profile-rtx-5050-solo-rtx-5050-twin-edge-oc","title":"Carte graphique ZOTAC GAMING GeForce 8 Go RTX 5050 Low Profile RTX 5050 SOLO RTX 5050 Twin Edge OC","description":"\u003cp\u003eZOTAC GAMING GeForce RTX 5050 Low Profile 3 ventilateurs\u003cbr\u003eZOTAC GAMING GeForce RTX 5050 SOLO 1 ventilateur\u003cbr\u003eZOTAC GAMING GeForce RTX 5050 Twin Edge 2 ventilateurs\u003cbr\u003eZOTAC GAMING GeForce RTX 5050 Twin Edge OC 2 ventilateurs\u003cbr\u003eZOTAC GAMING GeForce RTX 5050 Twin Edge OC White Edition 2 ventilateurs\u003cbr\u003eZOTAC GAMING GeForce RTX 5050 Xingye OC HA 2 ventilateurs\u003cbr\u003e\u003c\/p\u003e\n\u003cp\u003eCartes graphiques ZOTAC GAMING GeForce, L'état est neuf ou d'occasion\u003c\/p\u003e\n\u003cp\u003eLe prix EXW comme ci-dessous :\u003c\/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"392\" style=\"border-collapse: collapse; width: 294pt;\"\u003e\n\n\u003ccolgroup\u003e\n\n\u003ccol width=\"321\" style=\"mso-width-source: userset; mso-width-alt: 10282; width: 241pt;\"\u003e \u003ccol width=\"71\" style=\"width: 53pt;\"\u003e \u003c\/colgroup\u003e\n\n\u003ctbody\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" width=\"321\" style=\"height: 15.0pt; width: 241pt;\"\u003eMarque\/Modèle\/Série\/Version\u003c\/td\u003e\n\n\u003ctd width=\"71\" style=\"width: 53pt;\"\u003eEXW USD\/pièce\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" style=\"height: 15.0pt;\"\u003eZOTAC GAMING GeForce RTX 5050 Low Profile 3 ventilateurs\u003c\/td\u003e\n\n\u003ctd align=\"right\"\u003e386\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" style=\"height: 15.0pt;\"\u003eZOTAC GAMING GeForce RTX 5050 SOLO 1 ventilateur\u003c\/td\u003e\n\n\u003ctd align=\"right\"\u003e386\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" style=\"height: 15.0pt;\"\u003eZOTAC GAMING GeForce RTX 5050 Twin Edge 2 ventilateurs\u003c\/td\u003e\n\n\u003ctd align=\"right\"\u003e386\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" style=\"height: 15.0pt;\"\u003eZOTAC GAMING GeForce RTX 5050 Twin Edge OC 2 ventilateurs\u003c\/td\u003e\n\n\u003ctd align=\"right\"\u003e386\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" style=\"height: 15.0pt;\"\u003eZOTAC GAMING GeForce RTX 5050 Twin Edge OC White Edition 2 ventilateurs\u003c\/td\u003e\n\n\u003ctd align=\"right\"\u003e404\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\u003ctr height=\"20\" style=\"height: 15.0pt;\"\u003e\n\n\u003ctd height=\"20\" style=\"height: 15.0pt;\"\u003eZOTAC GAMING GeForce RTX 5050 Xingye OC HA 2 ventilateurs\u003c\/td\u003e\n\n\u003ctd align=\"right\"\u003e333\u003c\/td\u003e\n\n\n\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\n\n\n\u003c\/table\u003e","brand":"Neobitti Inc. 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