{"product_id":"modulo-nvidia-jetson-t4000","title":"Módulo NVIDIA Jetson T4000","description":"\u003cdiv id=\"buildify\" class=\"buildify buildify-12539581137236\"\u003e\u003cdiv class=\"bdf-innr\"\u003e \u003cdiv class=\"bdf-s-wrp\"\u003e \u003csection data-id=\"ywqzfdc\" class=\"bdf-e bdf-e-ywqzfdc bdf-s-full_width bdf-s-h-default bdf-s-h-default bdf-s bdf-top-s\" data-el=\"section\"\u003e \u003cdiv class=\"bdf-ctr bdf-c-gap-default\"\u003e \u003cdiv class=\"bdf-r\"\u003e \u003cdiv data-id=\"awuaawd\" class=\"bdf-e bdf-e-awuaawd bdf-c bdf-c-s-100 bdf-top-c\" data-el=\"column\"\u003e \u003cdiv class=\"bdf-c-wrp bdf-e-ppl\"\u003e \u003cdiv class=\"bdf-w-wrp\"\u003e \u003cdiv data-id=\"lfuzqeo\" class=\"bdf-e bdf-e-lfuzqeo bdf-w bdf-w-heading\" data-el=\"heading.default\"\u003e \u003cdiv class=\"bdf-w-ctr\"\u003e \u003ch2 class=\"bdf-h-t bdf-sz-medium\"\u003eModulo NVIDIA Jetson T4000\u003c\/h2\u003e \u003c\/div\u003e \u003c\/div\u003e \u003cdiv data-id=\"zgedg2q\" class=\"bdf-e bdf-e-zgedg2q bdf-w bdf-w-heading\" data-el=\"heading.default\"\u003e \u003cdiv class=\"bdf-w-ctr\"\u003e \u003ch2 class=\"bdf-h-t bdf-sz-small\"\u003e900-13834-0000-000\u003c\/h2\u003e \u003c\/div\u003e \u003c\/div\u003e \u003csection data-id=\"dzrjrze\" class=\"bdf-e bdf-e-dzrjrze bdf-s-full_width bdf-s-h-default bdf-s-h-default bdf-s bdf-inner-s\" data-el=\"section\"\u003e \u003cdiv class=\"bdf-ctr bdf-c-gap-default\"\u003e \u003cdiv class=\"bdf-r\"\u003e \u003cdiv data-id=\"jachcmk\" class=\"bdf-e bdf-e-jachcmk bdf-c bdf-c-s-100 bdf-inner-c\" data-el=\"column\"\u003e \u003cdiv class=\"bdf-c-wrp bdf-e-ppl\"\u003e \u003cdiv class=\"bdf-w-wrp\"\u003e \u003cdiv data-id=\"ulccafn\" class=\"bdf-e bdf-e-ulccafn bdf-w bdf-w-text-editor\" data-el=\"text-editor.default\"\u003e \u003cdiv class=\"bdf-w-ctr\"\u003e \u003cdiv class=\"bdf-txt-edt bdf-clr\"\u003e\u003cul\u003e\n\u003cli\u003e\u003cspan style=\"font-size: 16px; line-height: 26px; display: inline-block;\"\u003eMódulo NVIDIA Jetson T4000\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan style=\"font-size: 16px; line-height: 26px; display: inline-block;\"\u003e1536 núcleos NVIDIA CUDA® e 64 núcleos Tensor\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan style=\"font-size: 16px; line-height: 26px; display: inline-block;\"\u003eCPU Arm® Neoverse®-V3AE de 12 núcleos 64 bits\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan style=\"font-size: 16px; line-height: 26px; display: inline-block;\"\u003e64 GB LPDDR5X de 256 bits 273 GB\/s\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/section\u003e \u003cdiv data-id=\"vegfcxn\" class=\"bdf-e bdf-e-vegfcxn bdf-tbs-v-horizontal bdf-w bdf-w-tabs\" data-el=\"tabs.default\"\u003e \u003cdiv class=\"bdf-w-ctr\"\u003e \u003cdiv class=\"bdf-tbs bdf-rcv-tb-w\" role=\"tablist\"\u003e \u003cdiv class=\"bdf-tbs-wrp\" role=\"tab\"\u003e \u003cdiv class=\"bdf-tb-t bdf-tb-dt-t\" data-tab=\"1\"\u003eVista Rápida\u003c\/div\u003e \u003cdiv class=\"bdf-tb-t bdf-tb-dt-t\" data-tab=\"2\"\u003eCaracterísticas\u003c\/div\u003e \u003cdiv class=\"bdf-tb-t bdf-tb-dt-t\" data-tab=\"3\"\u003eDownloads\u003c\/div\u003e \u003c\/div\u003e \u003cdiv class=\"bdf-tbs-cnt-wrp bdf-rcv-tb-wrp\" role=\"tabpanel\"\u003e \u003cdiv class=\"bdf-tb\"\u003e \u003cdiv class=\"bdf-tb-t bdf-tb-ph-t\" data-tab=\"1\"\u003eVista Rápida\u003c\/div\u003e \u003cdiv class=\"bdf-rcv-tb-cnt bdf-tb-cnt bdf-s-wrp bdf-clr bdf-rptr-itm-kfp0noq\" data-tab=\"1\"\u003e \u003csection data-id=\"kxfflkj\" class=\"bdf-e bdf-e-kxfflkj bdf-s-full_width bdf-s-h-default bdf-s-h-default bdf-s bdf-inner-s\" data-el=\"section\"\u003e \u003cdiv class=\"bdf-ctr bdf-c-gap-default\"\u003e \u003cdiv class=\"bdf-r\"\u003e \u003cdiv data-id=\"qkyonbf\" class=\"bdf-e bdf-e-qkyonbf bdf-c bdf-c-s-100 bdf-top-c\" data-el=\"column\"\u003e \u003cdiv class=\"bdf-c-wrp bdf-e-ppl\"\u003e \u003cdiv class=\"bdf-w-wrp\"\u003e \u003cdiv data-id=\"heduntc\" class=\"bdf-e bdf-e-heduntc bdf-w bdf-w-text-editor\" data-el=\"text-editor.default\"\u003e \u003cdiv class=\"bdf-w-ctr\"\u003e \u003cdiv class=\"bdf-txt-edt bdf-clr\"\u003e\n\u003cp\u003e\u003cspan style=\"font-size: 16px; line-height: 26px; display: inline-block;\"\u003e\u003cstrong\u003eA Plataforma Definitiva para IA Física e Robótica\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan style=\"font-size: 16px; line-height: 26px; display: inline-block;\"\u003eO módulo NVIDIA® Jetson T4000™ oferece um desempenho excepcional para IA física e robótica. Isto inclui até 1200 FP4 TFLOPS de computação de IA e 64 GB de memória com potência configurável entre 40 W e 70 W. Comparado com o AGX Orin™, oferece mais de 4x a computação de IA com uma eficiência energética excepcional e raciocínio em tempo real.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan style=\"font-size: 16px; line-height: 26px; display: inline-block;\"\u003eEste sistema-em-módulo possui uma GPU com arquitetura NVIDIA Blackwell e um motor de transformador com suporte para GPU de Múltiplas Instâncias (MIG) para executar sem esforço os mais recentes modelos generativos de IA. O NVIDIA Jetson Thor acelera aplicações multi-sensor em tempo real com baixa latência, com uma CPU Arm® Neoverse®-V3AE de 12 núcleos, rede de 3x 25 GbE e extensas opções de E\/S para fusão de sensores. Inclui também um conjunto de aceleradores, como um Acelerador de Visão Programável (PVA) de terceira geração, codificadores e decodificadores duplos, um acelerador de fluxo óptico, entre outros.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan style=\"font-size: 16px; line-height: 26px; display: inline-block;\"\u003eO Jetson Thor pertence a uma nova classe de computadores robóticos, arquitetado desde o início para alimentar aplicações de IA física de próxima geração. Com a nova arquitetura de GPU Blackwell, é construído para raciocínio generativo e processamento de sensores multimodais. Também suporta uma ampla gama de modelos generativos de IA, desde modelos de Ação em Linguagem Visual (VLA) como o NVIDIA Isaac™ GR00T para humanoides até todos os populares LLMs e VLMs como o Cosmos Reason. Para fornecer uma experiência contínua do cloud-to-edge, o Jetson Thor executa a pilha de software NVIDIA AI para aplicações de IA físicas, incluindo o NVIDIA Isaac para robótica, o NVIDIA Metropolis para IA visual agencial e o NVIDIA Holoscan para processamento de sensores. Você pode também construir agentes de IA de análises de vídeo na periferia usando os Blueprints da NVIDIA, como Pesquisa e Resumo de Vídeo (VSS) com o NVIDIA Cosmos Reason como VLM.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan style=\"font-size: 16px; line-height: 26px; display: inline-block;\"\u003eO nosso ecossistema de parceiros oferece todas as placas de suporte, serviços de design, câmaras e outros sensores que você precisa, bem como software adicional de IA e de sistemas. Isto permite acelerar o desenvolvimento de soluções em indústrias que vão desde robótica e espaços inteligentes até comércio a retalho, industrial, médico e muito mais.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan style=\"font-size: 16px; line-height: 26px; display: inline-block;\"\u003eO módulo Jetson Thor oferece desempenho pronto para produção, enorme computação de IA e capacidades de sensores para aplicações de IA físicas num formato compacto. Isso torna-o a plataforma ideal para desenvolvedores que procuram desbloquear novas possibilidades para robótica humanoide e outras aplicações de IA física\u003c\/span\u003e.\u003c\/p\u003e\n\u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/section\u003e \u003c\/div\u003e \u003c\/div\u003e \u003cdiv class=\"bdf-tb\"\u003e \u003cdiv class=\"bdf-tb-t bdf-tb-ph-t\" data-tab=\"2\"\u003eCaracterísticas\u003c\/div\u003e \u003cdiv class=\"bdf-rcv-tb-cnt bdf-tb-cnt bdf-s-wrp bdf-clr bdf-rptr-itm-1jpf9wf\" data-tab=\"2\"\u003e \u003csection data-id=\"19sojk8\" class=\"bdf-e bdf-e-19sojk8 bdf-s-full_width bdf-s-h-default bdf-s-h-default bdf-s bdf-inner-s\" data-el=\"section\"\u003e \u003cdiv class=\"bdf-ctr bdf-c-gap-default\"\u003e \u003cdiv class=\"bdf-r\"\u003e \u003cdiv data-id=\"7fzd5zq\" class=\"bdf-e bdf-e-7fzd5zq bdf-c bdf-c-s-100 bdf-top-c\" data-el=\"column\"\u003e \u003cdiv class=\"bdf-c-wrp bdf-e-ppl\"\u003e \u003cdiv class=\"bdf-w-wrp\"\u003e \u003cdiv data-id=\"27otq25\" class=\"bdf-e bdf-e-27otq25 bdf-w bdf-w-text-editor\" data-el=\"text-editor.default\"\u003e \u003cdiv class=\"bdf-w-ctr\"\u003e \u003cdiv class=\"bdf-txt-edt bdf-clr\"\u003e\n\u003cdiv class=\"nv-title text h--smallest aem-GridColumn aem-GridColumn--default--12\"\u003e\u003cdiv id=\"nv-title-0a8e2d8f6f\" class=\"general-container-text \"\u003e\u003cdiv class=\"text-center lap-text-center tab-text-center mob-text-center\"\u003e\u003ch3 class=\"title \"\u003e\u003cspan style=\"font-size: 16px; line-height: 26px; display: inline-block;\"\u003e\u003cstrong\u003eNVIDIA Jetson Thor Series\u003c\/strong\u003e\u003c\/span\u003e\u003c\/h3\u003e\u003c\/div\u003e\u003c\/div\u003e\u003c\/div\u003e\n\u003cdiv class=\"nv-text text aem-GridColumn aem-GridColumn--default--12\"\u003e\u003cdiv id=\"nv-text-1228782b3d\" class=\"general-container-text \"\u003e\u003cdiv class=\"text-left lap-text-left tab-text-left mob-text-left\"\u003e\u003cdiv class=\"description \"\u003e\n\u003cdiv id=\"jetson-prod-module-table-topscroll\"\u003e\u003cdiv class=\"scroll-div1\"\u003e \u003c\/div\u003e\u003c\/div\u003e\n\u003cdiv id=\"jetson-prod-module-table\" class=\"jetson-specs-table scrolling expanded\"\u003e\u003cdiv class=\"scroll-div2\"\u003e\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"center\"\u003e\u003ctbody\u003e\n\u003ctr class=\"head border\"\u003e\n\u003ctd class=\"titlehead tableCLdata1\" width=\"10%\"\u003e \u003c\/td\u003e\n\u003ctd class=\"titlehead tableCMdata1\" width=\"18%\"\u003e\u003cstrong\u003eJetson AGX Thor Developer Kit\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd class=\"titlehead tableCMdata1\" width=\"18%\"\u003e\u003cstrong\u003eJetson T5000\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd class=\"titlehead tableCMdata1\" width=\"18%\"\u003e\u003cstrong\u003eJetson T4000\u003c\/strong\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"tableCLdata1\"\u003eAI Performance\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\" colspan=\"2\"\u003e2070 TFLOPS (FP4—Sparse)\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\"\u003e1200 TFLOPS (FP4—Sparse)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"tableCLdata1\"\u003eGPU\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\" colspan=\"2\"\u003e2560-core NVIDIA Blackwell architecture GPU with fifth-gen Tensor Cores\u003cbr\u003eMulti-Instance GPU (MIG) with 10 TPCs\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\"\u003e1536-core NVIDIA Blackwell architecture GPU with fifth-gen Tensor Cores\u003cbr\u003eMulti-Instance GPU (MIG) with 6 TPCs\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"tableCLdata1\"\u003eGPU Max Frequency\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\" colspan=\"2\"\u003e1.57 GHz\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\"\u003e1.53 GHz\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"tableCLdata1\"\u003eCPU\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\" colspan=\"2\"\u003e14-core Arm® Neoverse®-V3AE 64-bit CPU\u003cbr\u003e1 MB L2 cache per core\u003cbr\u003e16 MB shared system L3 cache\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\"\u003e12-core Arm® Neoverse®-V3AE 64-bit CPU\u003cbr\u003e1 MB L2 cache per core\u003cbr\u003e16 MB shared system L3 cache\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"tableCLdata1\"\u003eCPU Max Frequency\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\" colspan=\"3\"\u003e2.6 GHz\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"tableCLdata1\"\u003e\u003ca href=\"https:\/\/developer.nvidia.com\/embedded\/pva\" target=\"_blank\" rel=\"noopener\"\u003eVision Accelerator\u003c\/a\u003e\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\" colspan=\"3\"\u003e1x PVA v3\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"tableCLdata1\"\u003eMemory\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\" colspan=\"2\"\u003e128 GB 256-bit LPDDR5X\u003cbr\u003e273 GB\/s\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\"\u003e64 GB 256-bit LPDDR5X\u003cbr\u003e273 GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"tableCLdata1\"\u003eStorage\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\"\u003e1 TB NVMe M.2 Key M Slot\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\" colspan=\"2\"\u003eSupports NVMe through PCIe\u003cbr\u003eSupports SSD through USB3.2\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"tableCLdata1\"\u003eVideo Encode\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\" colspan=\"2\"\u003e2X NVENCODE\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\"\u003e1X NVENCODE\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"tableCLdata1\"\u003eVideo Decode\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\" colspan=\"2\"\u003e2X NVENCODE\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\"\u003e1X NVENCODE\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"tableCLdata1\"\u003eCamera\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\"\u003eHSB camera via QSFP slot\u003cbr\u003eUSB camera\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\" colspan=\"2\"\u003eUp to 20 cameras via HSB\u003cbr\u003eUp to 6 cameras through 16x lanes MIPI CSI-2\u003cbr\u003eUp to 32 cameras using Virtual Channels\u003cbr\u003eC-PHY 2.1 (10.25 Gbps)\u003cbr\u003eD-PHY 2.1 (40 Gbps)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"tableCLdata1\"\u003ePCIe*\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\"\u003eM.2 Key M slot with x4 PCIe Gen5\u003cbr\u003eM.2 Key E slot with x1 PCIe Gen5\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\" colspan=\"2\"\u003eUp to Gen5 (x8 lanes)\u003cbr\u003eRoot port only—C1 (x1) and C3 (x2)\u003cbr\u003eRoot Point or Endpoint—C2 (x1), C4 (x8), and C5 (x4)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"tableCLdata1\"\u003eUSB*\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\"\u003e2x USB-A (3.2 Gen2)\u003cbr\u003e2x USB-C (3.1)\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\" colspan=\"2\"\u003exHCI host controller with integrated PHY (up to)\u003cbr\u003e3x USB 3.2\u003cbr\u003e4x USB 2.0\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"tableCLdata1\"\u003eNetworking*\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\"\u003e1x 5GBe RJ45 connector\u003cbr\u003e1x QSFP28 (4x 25 GbE)\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\"\u003e4x 25 GbE\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\"\u003e3x 25 Gbe\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"tableCLdata1\"\u003eDisplay\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\"\u003e1x HDMI 2.0b\u003cbr\u003e1x DisplayPort 1.4a\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\" colspan=\"2\"\u003e4x shared HDMI2.1\u003cbr\u003eVESA DisplayPort 1.4a—HBR3, MST\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"tableCLdata1\"\u003eOther I\/O\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\"\u003eQSFP connector\u003cbr\u003eM.2 Key E expansion slot (WLAN\/BT, x1 PCIe, USB2.0, UART, I2C, I2S)\u003cbr\u003eM.2 Key M connector (NVMe for storage)\u003cbr\u003ePCIe x4 lane, I2C, PCIe x2 lane\u003cbr\u003e2x 13-pin CAN header\u003cbr\u003e2x 6-pin automation header\u003cbr\u003eLED\u003cbr\u003eJTAG connector (2x 5-pin header)\u003cbr\u003e1x fan connector —12V, PWM, and Tach\u003cbr\u003eAudio panel header (2x 5-pin)\u003cbr\u003eMicrofit power jack\u003cbr\u003eRTC backup battery connector 2-pin\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\"\u003e5x I2S\/2x audio hub (AHUB), 2x DMIC, 4x UART, 4x CAN, 3x SPI, 13x I2C, 6x PWM outputs\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\"\u003e5x I2S\/2x audio hub (AHUB), 2x DMIC, 4x UART, 3x SPI, 13x I2C, 6x PWM outputs**\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"tableCLdata1\"\u003ePower\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\"\u003e40 W–130 W\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\"\u003e40 W–130 W\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\"\u003e40 W–70 W\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd class=\"tableCLdata1\"\u003eMechanical\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\"\u003e243.19 mm x 112.40 mm x 56.88 mm\u003cbr\u003eThermal Transfer Plate (TTP) and optional fan or heat sink\u003c\/td\u003e\n\u003ctd class=\"tableCMdata1\" colspan=\"2\"\u003e100 mm x 87 mm\u003cbr\u003e699-pin B2B connector\u003cbr\u003eIntegrated Thermal Transfer Plate (TTP) with heatpipe\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\u003c\/div\u003e\u003c\/div\u003e\n\u003c\/div\u003e\u003c\/div\u003e\u003c\/div\u003e\u003c\/div\u003e\n\u003cdiv class=\"nv-text text aem-GridColumn aem-GridColumn--default--12\"\u003e\u003cdiv id=\"nv-text-ccaf9500f8\" class=\"general-container-text \"\u003e\u003cdiv class=\"text-left lap-text-left tab-text-left mob-text-left\"\u003e\u003cdiv class=\"description \"\u003e\u003cdiv class=\"spec-table-legal\"\u003e\u003cp\u003e\u003cspan class=\"p--small\"\u003e* Refer to the Software Features section of the latest NVIDIA Jetson Linux Developer Guide for a list of supported features.\u003cbr\u003e** Low-speed I\/O specification is subject to change.\u003c\/span\u003e\u003c\/p\u003e\u003c\/div\u003e\u003c\/div\u003e\u003c\/div\u003e\u003c\/div\u003e\u003c\/div\u003e\n\u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/section\u003e \u003c\/div\u003e \u003c\/div\u003e \u003cdiv class=\"bdf-tb\"\u003e \u003cdiv class=\"bdf-tb-t bdf-tb-ph-t\" data-tab=\"3\"\u003eDownloads\u003c\/div\u003e \u003cdiv class=\"bdf-rcv-tb-cnt bdf-tb-cnt 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bdf-btn-icon\"\u003e \u003ci class=\"fa fa-angle-right\"\u003e\u003c\/i\u003e \u003c\/span\u003e \u003cspan class=\"bdf-btn-text\"\u003eDatasheet\u003c\/span\u003e \u003c\/span\u003e \u003c\/a\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003cdiv data-id=\"ff3v58i\" class=\"bdf-e bdf-e-ff3v58i bdf-w bdf-w-button\" data-el=\"button.default\"\u003e \u003cdiv class=\"bdf-w-ctr\"\u003e \u003cdiv class=\"bdf-btn-wrp\"\u003e \u003ca href=\"https:\/\/developer.nvidia.com\/embedded\/downloads\" class=\"bdf-btn-link bdf-btn bdf-sz-md\" target=\"_blank\"\u003e \u003cspan class=\"bdf-btn-cnt-wrp\"\u003e \u003cspan class=\"bdf-aln-icon-right bdf-btn-icon\"\u003e \u003ci class=\"fa fa-angle-right\"\u003e\u003c\/i\u003e \u003c\/span\u003e \u003cspan class=\"bdf-btn-text\"\u003eProduct Download\u003c\/span\u003e \u003c\/span\u003e \u003c\/a\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003cdiv data-id=\"wnxmedk\" class=\"bdf-e bdf-e-wnxmedk bdf-w bdf-w-button\" data-el=\"button.default\"\u003e \u003cdiv class=\"bdf-w-ctr\"\u003e \u003cdiv class=\"bdf-btn-wrp\"\u003e \u003ca href=\"https:\/\/devtalk.nvidia.com\/default\/board\/139\/embedded-systems\/1\" class=\"bdf-btn-link bdf-btn bdf-sz-md\" target=\"_blank\"\u003e \u003cspan class=\"bdf-btn-cnt-wrp\"\u003e \u003cspan class=\"bdf-aln-icon-right bdf-btn-icon\"\u003e \u003ci class=\"fa fa-angle-right\"\u003e\u003c\/i\u003e \u003c\/span\u003e \u003cspan class=\"bdf-btn-text\"\u003eProduct FAQ\u003c\/span\u003e \u003c\/span\u003e \u003c\/a\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003cdiv data-id=\"7j4k7vo\" class=\"bdf-e bdf-e-7j4k7vo bdf-w bdf-w-button\" data-el=\"button.default\"\u003e \u003cdiv class=\"bdf-w-ctr\"\u003e \u003cdiv class=\"bdf-btn-wrp\"\u003e \u003ca href=\"https:\/\/developer.nvidia.com\/embedded\/develop\/tools\" class=\"bdf-btn-link bdf-btn bdf-sz-md\" target=\"_blank\"\u003e \u003cspan class=\"bdf-btn-cnt-wrp\"\u003e \u003cspan class=\"bdf-aln-icon-right bdf-btn-icon\"\u003e \u003ci class=\"fa fa-angle-right\"\u003e\u003c\/i\u003e \u003c\/span\u003e \u003cspan class=\"bdf-btn-text\"\u003eTools\u003c\/span\u003e \u003c\/span\u003e \u003c\/a\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003cdiv data-id=\"pt6hogd\" class=\"bdf-e bdf-e-pt6hogd bdf-w bdf-w-button\" data-el=\"button.default\"\u003e \u003cdiv class=\"bdf-w-ctr\"\u003e \u003cdiv class=\"bdf-btn-wrp\"\u003e \u003ca href=\"https:\/\/developer.nvidia.com\/embedded\/learn\/tutorials\" class=\"bdf-btn-link bdf-btn bdf-sz-md\" target=\"_blank\"\u003e \u003cspan class=\"bdf-btn-cnt-wrp\"\u003e \u003cspan class=\"bdf-aln-icon-right bdf-btn-icon\"\u003e \u003ci class=\"fa fa-angle-right\"\u003e\u003c\/i\u003e \u003c\/span\u003e \u003cspan class=\"bdf-btn-text\"\u003eTutoriais\u003c\/span\u003e \u003c\/span\u003e \u003c\/a\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/section\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/section\u003e \u003c\/div\u003e \u003c\/div\u003e\u003c\/div\u003e","brand":"NVIDIA","offers":[{"title":"Default Title","offer_id":53851240595796,"sku":"900-13834-0000-000","price":2348.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1913\/0443\/files\/900-13834-0000-000.png?v=1772487213","url":"https:\/\/guedeslopes.pt\/en\/products\/modulo-nvidia-jetson-t4000","provider":"AGL","version":"1.0","type":"link"}