{"title":"NVIDIA","description":"\u003cp\u003eExplore high-performance graphics cards and cutting-edge computing solutions from NVIDIA - a global leader in GPU technology and AI innovation. Whether you're a gamer, creator, or professional, NVIDIA delivers industry-leading performance, reliability, and efficiency with products like GeForce, Quadro, and RTX series. Shop now for the best deals on NVIDIA graphics cards, components, and accessories at Network Outlet.\u003c\/p\u003e","products":[{"product_id":"mqm9700-ns2r-nvidia-quantum-2-based-ndr-infiniband-switch-64-ports-ndr-32-osfp-ports-managed-connector-to-power-c2p-airflow-reverse","title":"MQM9700-NS2R NVIDIA Quantum-2 Based NDR InfiniBand Switch, 64-Ports NDR, 32 OSFP Ports, Managed, Connector-to-Power (C2P) Airflow (Reverse)","description":"\u003ch1\u003e\u003cstrong\u003eNVIDIA MQM9700-NS2R 64-Port NDR InfiniBand Switch with C2P Airflow\u003c\/strong\u003e\u003c\/h1\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003cstrong\u003eHigh-Speed 64-Port NDR OSFP InfiniBand Switch with Reverse Airflow for HPC and AI\u003c\/strong\u003e\u003c\/h2\u003e\n\u003cp\u003eThe \u003cstrong\u003eNVIDIA MQM9700-NS2R\u003c\/strong\u003e is a high-performance \u003cstrong\u003eQuantum-2 based NDR InfiniBand switch\u003c\/strong\u003e, designed to deliver industry-leading throughput and ultra-low latency across \u003cstrong\u003e64 NDR (400Gb\/s) ports\u003c\/strong\u003e via \u003cstrong\u003e32 OSFP interfaces\u003c\/strong\u003e. Ideal for \u003cstrong\u003eAI workloads\u003c\/strong\u003e, \u003cstrong\u003ehigh-performance computing (HPC)\u003c\/strong\u003e, and \u003cstrong\u003ecloud infrastructure\u003c\/strong\u003e, this switch provides unmatched scalability and performance in a compact 1U form factor.\u003c\/p\u003e\n\u003cp\u003eThis version features \u003cstrong\u003eConnector-to-Power (C2P)\u003c\/strong\u003e airflow — also known as \u003cstrong\u003ereverse airflow\u003c\/strong\u003e — making it ideal for rack environments that require front-to-back cooling.\u003c\/p\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003cstrong\u003e🔧 Product Specifications \u003ca title=\"MQM9700-NS2R NVIDIA - Data Sheet\" href=\"https:\/\/docs.nvidia.com\/networking\/display\/qm97x0pub\" target=\"_blank\"\u003e(Data Sheet)\u003c\/a\u003e\u003ca title=\"MQM9700-NS2R NVIDIA - Data Sheet\" href=\"https:\/\/docs.nvidia.com\/networking\/display\/qm97x0pub\" target=\"_blank\"\u003e\u003c\/a\u003e\u003c\/strong\u003e\u003c\/h3\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eFeature\u003c\/strong\u003e\u003c\/th\u003e\n\u003cth\u003e\u003cstrong\u003eDetails\u003c\/strong\u003e\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eBrand\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eNVIDIA\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eModel\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eMQM9700-NS2R\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePlatform\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eNVIDIA Quantum-2\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eSwitch Type\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eManaged\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePorts\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e32x OSFP (supports up to 64x NDR 400Gb\/s via splitter cables)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eSpeed\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eNDR – 400Gb\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eSwitching Capacity\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e51.2 Tbps\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eForwarding Rate\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e25.6 Bpps\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eLatency\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eSub-300ns (ultra-low latency)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eAirflow Direction\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eConnector-to-Power (C2P) – Reverse airflow\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eForm Factor\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1U Rack-Mountable\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCooling\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eRedundant, hot-swappable fans (rear-exhaust)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePower Supply\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eRedundant, hot-swappable PSUs\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eManagement Interfaces\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eCLI, SNMP, Web GUI, out-of-band management\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCompatibility\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eOSFP optics, NVIDIA ConnectX NICs, DGX systems\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eUse Cases\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eAI Clusters, HPC, Scientific Research, Hyperscale Data Centers\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003cstrong\u003e❓ Frequently Asked Questions (FAQs)\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003e\u003cstrong\u003eQ1: What airflow does the MQM9700-NS2R support?\u003c\/strong\u003e\u003cbr\u003e\u003cstrong\u003eA:\u003c\/strong\u003e The MQM9700-NS2R features \u003cstrong\u003eConnector-to-Power (C2P)\u003c\/strong\u003e airflow, which directs air from the connector side to the power supply side — perfect for environments with reverse airflow setups.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ2: How many total NDR connections are supported?\u003c\/strong\u003e\u003cbr\u003e\u003cstrong\u003eA:\u003c\/strong\u003e The switch supports up to \u003cstrong\u003e64 NDR (400Gb\/s) connections\u003c\/strong\u003e using \u003cstrong\u003e32 OSFP ports\u003c\/strong\u003e and breakout cables (2x NDR per port).\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ3: Is this model suitable for AI and HPC workloads?\u003c\/strong\u003e\u003cbr\u003e\u003cstrong\u003eA:\u003c\/strong\u003e Yes, it’s specifically built for \u003cstrong\u003eAI training clusters\u003c\/strong\u003e, \u003cstrong\u003escientific computing\u003c\/strong\u003e, and \u003cstrong\u003ecloud-native high-performance environments\u003c\/strong\u003e.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ4: Can I hot-swap components like fans and power supplies?\u003c\/strong\u003e\u003cbr\u003e\u003cstrong\u003eA:\u003c\/strong\u003e Yes. Both \u003cstrong\u003efans and power supplies are hot-swappable\u003c\/strong\u003e, allowing for non-disruptive servicing.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ5: What’s the difference between MQM9700-NS2R and MQM9700-NS2F?\u003c\/strong\u003e\u003cbr\u003e\u003cstrong\u003eA:\u003c\/strong\u003e The main difference is airflow direction. The \u003cstrong\u003eNS2R\u003c\/strong\u003e model uses \u003cstrong\u003eC2P (reverse airflow)\u003c\/strong\u003e, while the \u003cstrong\u003eNS2F\u003c\/strong\u003e model uses \u003cstrong\u003eP2C (forward airflow)\u003c\/strong\u003e.\u003c\/p\u003e\n\u003chr\u003e\n\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e","brand":"NVIDIA","offers":[{"title":"Default Title","offer_id":46743127228645,"sku":"MQM9700-NS2R","price":18800.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0252\/2280\/7632\/files\/MQM9700-NS2R_1u_Mellanox.jpg?v=1754586682"},{"product_id":"nvidia-h200-nvl-tensor-core-gpu-141gb-hbm3e-pcie-gen-5-0-hopper-architecture-ai-accelerator","title":"NVIDIA H200 NVL Tensor Core GPU 141GB HBM3e PCIe Gen 5.0 – Hopper Architecture AI Accelerator","description":"\u003ch1\u003e\u003cstrong\u003eNVIDIA H200 NVL Tensor Core GPU 141GB HBM3e PCIe Gen 5.0\u003c\/strong\u003e\u003c\/h1\u003e\n\u003ch2\u003e\u003cstrong\u003eHigh-Performance AI Accelerator for HPC \u0026amp; LLM Workloads - \u003cspan style=\"color: rgb(255, 42, 0);\"\u003ePNs: 900-21010-0040-000, 699-21010-0230-B00\u003c\/span\u003e\u003c\/strong\u003e\u003c\/h2\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan style=\"color: rgb(255, 42, 0);\"\u003eNote: This product is export restricted and may not be relocated or transferred without compliance with all applicable regulations.\u003c\/span\u003e\u003c\/strong\u003e\u003c\/p\u003e\n\u003chr\u003e\n\u003cp\u003eThe \u003cstrong\u003eNVIDIA H200 NVL Tensor Core GPU\u003c\/strong\u003e sets a new benchmark in data center performance, built on the \u003cstrong\u003eHopper architecture\u003c\/strong\u003e and optimized for \u003cstrong\u003eAI, HPC, and generative workloads\u003c\/strong\u003e. Featuring \u003cstrong\u003e141GB of ultra-fast HBM3e memory\u003c\/strong\u003e and \u003cstrong\u003ePCIe Gen 5.0\u003c\/strong\u003e interface, the H200 delivers exceptional throughput for training and inference of large language models (LLMs), deep learning, and scientific computing.\u003c\/p\u003e\n\u003cp\u003eWith \u003cstrong\u003eTensor Core technology\u003c\/strong\u003e, \u003cstrong\u003eNVLink scalability\u003c\/strong\u003e, and advanced \u003cstrong\u003eMulti-Instance GPU (MIG)\u003c\/strong\u003e support, the H200 NVL achieves unprecedented acceleration across AI pipelines — from model training to real-time inference. Designed for modern AI clusters and hyperscale data centers, it offers \u003cstrong\u003emassive memory bandwidth\u003c\/strong\u003e, \u003cstrong\u003eenergy efficiency\u003c\/strong\u003e, and \u003cstrong\u003ecompatibility with NVIDIA’s full software stack\u003c\/strong\u003e, including \u003cstrong\u003eCUDA\u003c\/strong\u003e, \u003cstrong\u003ecuDNN\u003c\/strong\u003e, and \u003cstrong\u003eTensorRT\u003c\/strong\u003e.\u003c\/p\u003e\n\u003cp\u003eThe H200 NVL PCIe is a drop-in replacement for the A100 and H100 GPUs, ensuring smooth transition and enhanced performance across existing infrastructures.\u003c\/p\u003e\n\u003chr\u003e\n\u003ch3\u003e\n\u003cstrong\u003e⚙️ Product Specifications: NVIDIA H200 NVL PCIe GPU\u003c\/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003ca href=\"https:\/\/resources.nvidia.com\/en-us-data-center-overview-mc\/en-us-data-center-overview\/hpc-datasheet-sc23-h200\" title=\"NVIDIA H200 NVL Tensor Core GPU 141GB HBM3e PCIe Gen 5.0 – Specs Sheet\" rel=\"noopener\" target=\"_blank\"\u003eCLICK HERE FOR SPECIFICATIONS SHEET\u003c\/a\u003e\u003ca href=\"https:\/\/resources.nvidia.com\/en-us-data-center-overview-mc\/en-us-data-center-overview\/hpc-datasheet-sc23-h200\" title=\"NVIDIA H200 NVL Tensor Core GPU 141GB HBM3e PCIe Gen 5.0 – Specs Sheet\" rel=\"noopener\" target=\"_blank\"\u003e\u003c\/a\u003e\u003c\/strong\u003e\n\u003c\/h3\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eCategory\u003c\/strong\u003e\u003c\/th\u003e\n\u003cth\u003e\u003cstrong\u003eDetails\u003c\/strong\u003e\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eModel\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eNVIDIA H200 NVL Tensor Core GPU\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePart Numbers\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e900-21010-0040-000, 699-21010-0230-B00\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eGPU Architecture\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eNVIDIA Hopper\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCUDA Cores\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e14,592\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTensor Cores\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e456 (4th Gen)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMemory\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e141GB HBM3e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMemory Bandwidth\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e4.8 TB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eInterconnect\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003ePCIe Gen 5.0 x16\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eNVLink Support\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eYes – up to 900 GB\/s GPU-GPU bandwidth\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMIG Support\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eUp to 7 GPU instances\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePerformance\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eFP64: 34 TFLOPS \/ FP32: 68 TFLOPS \/ FP8: up to 1,979 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eForm Factor\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003ePCIe Dual Slot\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCooling\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003ePassive (requires chassis airflow)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePower Consumption (TDP)\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e700W\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eSystem Compatibility\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eNVIDIA-certified servers and AI platforms\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eSoftware Stack\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eCUDA, cuDNN, TensorRT, NCCL, NVLink, NVIDIA AI Enterprise\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eUse Cases\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eAI training, LLMs, HPC simulations, inference, cloud computing\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCompliance\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eRoHS, WEEE, CE, FCC, UL certified\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eWarranty\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eStandard manufacturer warranty (varies by reseller)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003cstrong\u003e❓ Frequently Asked Questions (FAQs)\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003e\u003cstrong\u003eQ1: What is the main difference between the NVIDIA H200 and H100 GPUs?\u003c\/strong\u003e\u003cbr\u003eA1: The H200 introduces next-generation \u003cstrong\u003eHBM3e memory (141GB)\u003c\/strong\u003e offering up to \u003cstrong\u003e1.8× higher bandwidth\u003c\/strong\u003e, significantly improving large-scale AI model training and inference performance.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ2: What workloads benefit most from the H200 NVL?\u003c\/strong\u003e\u003cbr\u003eA2: It’s ideal for \u003cstrong\u003eAI training, inference, LLMs (like GPT-4 scale models)\u003c\/strong\u003e, scientific simulations, and \u003cstrong\u003ehigh-performance data analytics\u003c\/strong\u003e.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ3: Can the H200 NVL be used in existing A100 or H100 systems?\u003c\/strong\u003e\u003cbr\u003eA3: Yes - the H200 PCIe maintains \u003cstrong\u003ebackward compatibility\u003c\/strong\u003e, allowing seamless upgrades from A100 or H100 environments.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ4: Does the H200 NVL require special cooling?\u003c\/strong\u003e\u003cbr\u003eA4: Yes, it features \u003cstrong\u003epassive cooling\u003c\/strong\u003e and should be installed in a \u003cstrong\u003eserver with sufficient airflow\u003c\/strong\u003e.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ5: What’s the total memory advantage of the H200 NVL?\u003c\/strong\u003e\u003cbr\u003eA5: With \u003cstrong\u003e141GB of HBM3e\u003c\/strong\u003e, it provides the \u003cstrong\u003elargest memory capacity\u003c\/strong\u003e of any NVIDIA PCIe GPU to date, perfect for training extremely large AI models.\u003c\/p\u003e","brand":"NVIDIA","offers":[{"title":"Default Title","offer_id":46805840298213,"sku":"NVIDIA-H200-NVL-PCIE-141GB","price":37000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0252\/2280\/7632\/files\/NVIDIA-H200-NVL-GPU_141GB-PCIE.jpg?v=1761151317"},{"product_id":"nvidia-tesla-t4-16gb-gddr6-gpu-ai-inference-virtualization-deep-learning-accelerator","title":"NVIDIA Tesla T4 16GB GDDR6 GPU | AI Inference, Virtualization \u0026 Deep Learning Accelerator","description":"\u003ch1\u003e\u003cstrong\u003eNVIDIA Tesla T4 16GB GPU - Optimized for AI Inference, Virtualization \u0026amp; Deep Learning\u003c\/strong\u003e\u003c\/h1\u003e\n\u003ch2\u003e\u003cstrong\u003eCompact, Energy-Efficient GPU for Data Centers and Virtual Workloads — Compatible \u003cbr\u003e\u003cspan style=\"color: rgb(255, 42, 0);\"\u003ePart Numbers: 900-2G183-0000-001 \/ P09571-001 \/ R0W29C \/ 490-BFHN \/ 490-BFLB \/ UCSC-GPU-T4-16\u003c\/span\u003e\u003c\/strong\u003e\u003c\/h2\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan style=\"color: rgb(255, 42, 0);\"\u003eNote: This product is export restricted and may not be relocated or transferred without compliance with all applicable regulations.\u003c\/span\u003e\u003c\/strong\u003e\u003c\/p\u003e\n\u003chr\u003e\n\u003cp\u003eThe \u003cstrong\u003eNVIDIA Tesla T4 16GB GPU\u003c\/strong\u003e delivers exceptional performance for AI inference, deep learning, data analytics, and virtual desktop infrastructure (VDI).\u003cbr\u003eBuilt on the \u003cstrong\u003eTuring architecture\u003c\/strong\u003e, it’s designed for energy-efficient scalability across cloud, enterprise, and edge deployments.\u003c\/p\u003e\n\u003cp\u003eFeaturing \u003cstrong\u003e2,560 CUDA cores\u003c\/strong\u003e, \u003cstrong\u003e320 Tensor Cores\u003c\/strong\u003e, and \u003cstrong\u003e16GB GDDR6 memory\u003c\/strong\u003e, the Tesla T4 brings versatile acceleration for AI workloads — from natural language processing and recommendation systems to video analytics and high-density virtualization.\u003c\/p\u003e\n\u003cp\u003eIts \u003cstrong\u003elow-profile PCIe form factor\u003c\/strong\u003e and \u003cstrong\u003e70W power consumption\u003c\/strong\u003e make it ideal for high-density servers and edge AI environments.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompatible Part Numbers:\u003c\/strong\u003e\u003cbr\u003e900-2G183-0000-001, P09571-001, R0W29C, 490-BFHN, 490-BFLB, UCSC-GPU-T4-16 — all share identical specifications and performance.\u003c\/p\u003e\n\u003chr\u003e\n\u003ch3\u003e\n\u003cstrong\u003e⚙️ Product Specifications For NVIDIA Tesla T4 16GB GDDR6 GPU\u003c\/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003ca href=\"https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/Data-Center\/tesla-t4\/t4-tensor-core-datasheet-951643.pdf\" title=\"NVIDIA Tesla T4 16GB GDDR6 GPU - Specifications Sheet\" rel=\"noopener\" target=\"_blank\"\u003eCLICK HERE FOR SPECIFICATIONS SHEET\u003c\/a\u003e\u003ca href=\"https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/Data-Center\/tesla-t4\/t4-tensor-core-datasheet-951643.pdf\" title=\"NVIDIA Tesla T4 16GB GDDR6 GPU - Specifications Sheet\" rel=\"noopener\" target=\"_blank\"\u003e\u003c\/a\u003e\u003c\/strong\u003e\n\u003c\/h3\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eSpecification\u003c\/strong\u003e\u003c\/th\u003e\n\u003cth\u003e\u003cstrong\u003eDetails\u003c\/strong\u003e\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eGPU Architecture\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eNVIDIA Turing\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCUDA Cores\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e2,560\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTensor Cores\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e320\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eGPU Memory\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e16GB GDDR6\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMemory Interface\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e256-bit\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMemory Bandwidth\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e320 GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eFP32 Performance\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e8.1 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eFP16 Performance\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e65 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eINT8 Performance\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e130 TOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eINT4 Performance\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e260 TOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eForm Factor\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eLow-profile, PCIe Gen3 x16\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePower Consumption\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e70W\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCooling Type\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003ePassive or active (OEM dependent)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eNVENC \/ NVDEC Support\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eYes (Video Encoding\/Decoding)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eVirtualization Support\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eNVIDIA GRID \/ vGPU Ready\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eThermal Solution\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eServer airflow required\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eUse Cases\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eAI Inference, Deep Learning, Virtual Desktops, HPC Edge Computing\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eSupported APIs\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eCUDA, cuDNN, TensorRT, DirectX 12, OpenGL 4.6, Vulkan\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eOperating Temperature\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e0°C – 55°C (typical)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003cstrong\u003e❓ Frequently Asked Questions (FAQs)\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003e\u003cstrong\u003eQ1: What are the main workloads supported by the NVIDIA Tesla T4?\u003c\/strong\u003e\u003cbr\u003eA1: The T4 is optimized for AI inference, deep learning, data analytics, and virtual desktop infrastructure (VDI).\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ2: Can the T4 GPU be used in a standard server chassis?\u003c\/strong\u003e\u003cbr\u003eA2: Yes. Its low-profile PCIe design fits easily into standard and high-density rack servers.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ3: Does the T4 require active cooling?\u003c\/strong\u003e\u003cbr\u003eA3: It’s passively cooled by server airflow, though active-cooled versions exist depending on OEM design.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ4: How does the T4 compare to the A100 or A30 GPUs?\u003c\/strong\u003e\u003cbr\u003eA4: The Tesla T4 is optimized for inference and virtualization - offering excellent efficiency at lower power, while A100\/A30 target high-end training and HPC workloads.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ5: Are all listed part numbers identical in performance?\u003c\/strong\u003e\u003cbr\u003eA5: Yes - all listed part numbers (900-2G183-0000-001, P09571-001, R0W29C, 490-BFHN, 490-BFLB, UCSC-GPU-T4-16) share the same T4 GPU architecture, memory, and capabilities.\u003c\/p\u003e","brand":"NVIDIA","offers":[{"title":"Default Title","offer_id":46805887418597,"sku":"T4-Nvidia-Tesla-16GB","price":950.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0252\/2280\/7632\/files\/T4NvidiaTeslaT416GBGPU.jpg?v=1748638601"},{"product_id":"a10-nvidia-tesla-a10-tensor-core-gpu-24gb-gddr6-pcie-gen-4-0","title":"Nvidia Tesla A10 - Tensor Core GPU – 24GB GDDR6, PCIe Gen 4.0","description":"\u003ch1\u003e\u003cstrong\u003e🖥️ NVIDIA A10 24GB Tensor Core GPU – PCIe Gen 4.0, Data Center \u0026amp; AI Accelerator\u003c\/strong\u003e\u003c\/h1\u003e\n\u003ch2\u003e\u003cstrong\u003e⚙️ High-Performance NVIDIA A10 GPU \u003cspan style=\"color: rgb(255, 42, 0);\"\u003e(PN: 900-2G133-6220-0302)\u003c\/span\u003e – Built for AI, Deep Learning \u0026amp; Virtual Workstations\u003c\/strong\u003e\u003c\/h2\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan style=\"color: rgb(255, 42, 0);\"\u003eNote: This product is export restricted and may not be relocated or transferred without compliance with all applicable regulations.\u003c\/span\u003e\u003c\/strong\u003e\u003c\/p\u003e\n\u003chr\u003e\n\u003cp\u003eThe \u003cstrong\u003eNVIDIA A10 Tensor Core GPU\u003c\/strong\u003e delivers the perfect balance of AI performance, graphics, and virtualization for modern data centers.\u003cbr\u003ePowered by the \u003cstrong\u003eAmpere architecture\u003c\/strong\u003e, it features \u003cstrong\u003e24GB of GDDR6 memory\u003c\/strong\u003e, \u003cstrong\u003eTensor Cores\u003c\/strong\u003e, and \u003cstrong\u003ePCIe Gen 4.0\u003c\/strong\u003e support for unmatched acceleration in AI inference, rendering, and VDI workloads.\u003c\/p\u003e\n\u003cp\u003eDesigned for enterprise environments, the A10 brings high-performance computing to virtualized desktops and AI-driven servers, offering a significant leap in efficiency over previous generations like the T4 and Quadro RTX 6000.\u003c\/p\u003e\n\u003cp\u003eWhether you’re deploying GPU virtualization, scaling AI inference, or running high-end 3D workloads, the NVIDIA A10 is a proven, reliable choice for sustained performance and lower TCO.\u003c\/p\u003e\n\u003chr\u003e\n\u003ch3\u003e\n\u003cstrong\u003e📊 Product Specifications: NVIDIA A10 Tensor Core GPU (900-2G133-6220-0302)\u003c\/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003ca href=\"https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/Data-Center\/a10\/pdf\/a10-datasheet.pdf\" title=\"NVIDIA A10 Tensor Core GPU – 24GB GDDR6 - Specs Sheet\" rel=\"noopener\" target=\"_blank\"\u003eCLICK HERE FOR SPECIFICATIONS SHEET\u003c\/a\u003e\u003ca href=\"https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/Data-Center\/a10\/pdf\/a10-datasheet.pdf\" title=\"NVIDIA A10 Tensor Core GPU – 24GB GDDR6 - Specs Sheet\" rel=\"noopener\" target=\"_blank\"\u003e\u003c\/a\u003e\u003c\/strong\u003e\n\u003c\/h3\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eSpecification\u003c\/strong\u003e\u003c\/th\u003e\n\u003cth\u003e\u003cstrong\u003eDetails\u003c\/strong\u003e\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eGPU Architecture\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eNVIDIA Ampere\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCUDA Cores\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e10,752\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTensor Cores\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e336 (3rd Generation)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eRT Cores\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e84 (2nd Generation)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eGPU Memory\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e24GB GDDR6\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMemory Bandwidth\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e600 GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMemory Interface\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e384-bit\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eInterface\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003ePCI Express 4.0 x16\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eForm Factor\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eFull-height, full-length dual-slot\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCooling\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003ePassive (requires server airflow)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eFP32 Performance\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e~31.2 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eFP16 Tensor Performance\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e~125 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eINT8 Performance\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eUp to 250 TOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eNVENC \/ NVDEC\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eYes (AV1 decode supported)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMax Power Consumption\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e150 W TDP\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eVirtualization Support\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eNVIDIA vGPU \/ vPC \/ vComputeServer\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eSupported APIs\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eCUDA, DirectX 12, OpenGL 4.6, Vulkan 1.2\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eDimensions (L × H)\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e10.5 in × 4.4 in (267 mm × 112 mm)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePart Number\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e900-2G133-6220-0302\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003cstrong\u003e💬 Frequently Asked Questions (FAQs)\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003e\u003cstrong\u003eQ1: What workloads is the NVIDIA A10 ideal for?\u003c\/strong\u003e\u003cbr\u003eA1: It’s optimized for AI inference, virtual desktop infrastructure (VDI), rendering, and simulation workloads in enterprise and data-center environments.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ2: How does the A10 compare to the NVIDIA T4 or RTX A6000?\u003c\/strong\u003e\u003cbr\u003eA2: The A10 offers over \u003cstrong\u003e2.5× more Tensor performance\u003c\/strong\u003e than the T4 and improved efficiency with PCIe 4.0 bandwidth, while consuming moderate power (150 W).\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ3: Is this GPU suitable for virtualization?\u003c\/strong\u003e\u003cbr\u003eA3: Yes - it supports \u003cstrong\u003eNVIDIA vGPU\u003c\/strong\u003e technologies including vPC, vApps, and vComputeServer, ideal for multi-tenant virtualized deployments.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ4: Does it require active cooling?\u003c\/strong\u003e\u003cbr\u003eA4: No, it features \u003cstrong\u003epassive cooling\u003c\/strong\u003e and must be installed in a server chassis with sufficient airflow.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ5: What operating systems are supported?\u003c\/strong\u003e\u003cbr\u003eA5: Compatible with major enterprise platforms including Linux, Windows Server, and VMware vSphere environments.\u003c\/p\u003e","brand":"NVIDIA","offers":[{"title":"Default Title","offer_id":46805900165349,"sku":"Nvidia-Tesla-A10-24GB","price":3597.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0252\/2280\/7632\/files\/A10NvidiaTeslaA10-TensorCore.png?v=1761234201"},{"product_id":"mqm9790-ns2f-nvidia-quantum-2-based-ndr-infiniband-switch-64-ports-ndr-32-osfp-ports-unmanaged-p2c-airflow-forward","title":"MQM9790-NS2F NVIDIA Quantum-2 Based NDR InfiniBand Switch, 64-Ports NDR, 32 OSFP Ports, Unmanaged, P2C Airflow (Forward)","description":"\u003ch1\u003e\u003cstrong\u003eNVIDIA MQM9790-NS2F Quantum-2 64-Port NDR InfiniBand Switch – Unmanaged, Forward Airflow\u003c\/strong\u003e\u003c\/h1\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003cstrong\u003eHigh-Speed 64-Port Unmanaged InfiniBand Switch with 400Gb\/s, 32 OSFP \u0026amp; P2C Cooling\u003c\/strong\u003e\u003c\/h2\u003e\n\u003cp\u003eThe \u003cstrong\u003eNVIDIA MQM9790-NS2F\u003c\/strong\u003e is a high-density, \u003cstrong\u003eunmanaged 64-port NDR (400Gb\/s)\u003c\/strong\u003e InfiniBand switch powered by the \u003cstrong\u003eQuantum‑2 ASIC\u003c\/strong\u003e. Designed for plug-and-play simplicity, it supports \u003cstrong\u003e32 OSFP ports\u003c\/strong\u003e, delivering exceptional \u003cstrong\u003e51.2Tb\/s\u003c\/strong\u003e throughput and ultra-low latency—ideal for data centers, HPC clusters, and AI fabrics. Its \u003cstrong\u003ePower-to-Connector (P2C)\u003c\/strong\u003e airflow aligns with standard \u003cstrong\u003efront-to-back rack cooling\u003c\/strong\u003e.\u003c\/p\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003cstrong\u003e🔧 Product Specifications: MQM9790-NS2F NVIDIA\u003c\/strong\u003e\u003c\/h3\u003e\n\u003ch3\u003eMore Information: \u003ca title=\"MQM9790-NS2F NVIDIA User Manual\" href=\"https:\/\/docs.nvidia.com\/networking\/display\/qm97x0pub\" target=\"_blank\"\u003eUser Manual\u003c\/a\u003e\n\u003c\/h3\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eFeature\u003c\/strong\u003e\u003c\/th\u003e\n\u003cth\u003e\u003cstrong\u003eDetails\u003c\/strong\u003e\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eModel Number\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eMQM9790‑NS2F\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eSwitch Type\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eUnmanaged InfiniBand\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePorts\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e32× OSFP (64× 400Gb\/s NDR)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eSwitching Capacity\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e51.2 Tb\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePacket Rate\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e66.5 Bpps (Billion Packets\/sec)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eLatency\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e~130 ns\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eAirflow Direction\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eP2C (Power-to-Connector) – Front-to‑Back cooling\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eForm Factor\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1U Rack-mountable\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePower Supplies\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eDual redundant, hot-swappable AC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eFans\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e6+1 hot-swappable fan units\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eManagement Interfaces\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eNone (unmanaged configuration)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eEnvironmental Range\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e0–35 °C operating, up to 3050 m altitude\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eDimensions (W×H×D)\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e17″ × 1.7″ × 26″ \/ 433 mm × 43.6 mm × 660 mm\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eWeight\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e~31.97 lbs (14.5 kg)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTypical Power Consumption\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e640 W (up to 1.61 kW active)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCertifications\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eRoHS, ENERGY STAR, 80 Plus Gold, CE, FCC, VCCI, ICES, RCM\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003cstrong\u003e❓ Frequently Asked Questions (FAQs)\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003e\u003cstrong\u003eQ1: What does ‘unmanaged’ mean for this switch?\u003c\/strong\u003e\u003cbr\u003eA: The MQM9790‑NS2F is plug-and-play with no CLI or SNMP interface; ideal for fabrics managed externally (e.g., via NVIDIA UFM).\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ2: What cooling direction does it use, and why does it matter?\u003c\/strong\u003e\u003cbr\u003eA: Uses \u003cstrong\u003eP2C forward airflow\u003c\/strong\u003e, pushing air from rear to front—compatible with front-to-back rack cooling setups.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ3: How much bandwidth does each port support?\u003c\/strong\u003e\u003cbr\u003eA: Each OSFP port provides 400 Gb\/s; 32 ports yield up to \u003cstrong\u003e51.2 Tb\/s total throughput\u003c\/strong\u003e.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ4: Can it operate in multi-node AI and HPC clusters?\u003c\/strong\u003e\u003cbr\u003eA: Yes. Supports RDMA, adaptive routing, SHARP, and topologies like Fat‑Tree and DragonFly+. \u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ5: How heavy is the switch and how much power does it draw?\u003c\/strong\u003e\u003cbr\u003eA: Weighs about \u003cstrong\u003e31.97 lbs (14.5 kg)\u003c\/strong\u003e and draws ~640 W under typical loads, with peak up to \u003cstrong\u003e1.61 kW\u003c\/strong\u003e. \u003c\/p\u003e\n\u003chr\u003e\n\u003cp\u003e \u003c\/p\u003e","brand":"NVIDIA","offers":[{"title":"Default Title","offer_id":46871587487973,"sku":"MQM9790-NS2F","price":15800.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0252\/2280\/7632\/files\/MQM9790-NS2F2.jpg?v=1750955918"},{"product_id":"nvidia-tesla-a100-80gb-sxm4-gpu-ampere-architecture-80gb-hbm2e-nvlink-mig-support","title":"NVIDIA Tesla A100 80GB SXM4 GPU (Ampere Architecture, 80GB HBM2e, NVLink, MIG Support)","description":"\u003ch1\u003e\u003cstrong\u003eNVIDIA Tesla A100 80 GB SXM4 GPU - Ampere Architecture for AI \u0026amp; HPC | \u003cspan style=\"color: rgb(255, 42, 0);\"\u003ePNs: 699-2G506-0210-300, [future compatible part numbers]\u003c\/span\u003e\u003c\/strong\u003e\u003c\/h1\u003e\n\u003ch2\u003e\u003cstrong\u003eHigh-Performance NVIDIA A100 SXM4 GPU for Data Centers, AI, and High-Performance Computing\u003c\/strong\u003e\u003c\/h2\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan style=\"color: rgb(255, 42, 0);\"\u003eNote: This product is export restricted and may not be relocated or transferred without compliance with all applicable regulations.\u003c\/span\u003e\u003c\/strong\u003e\u003c\/p\u003e\n\u003chr\u003e\n\u003cp\u003eThe \u003cstrong\u003eNVIDIA Tesla A100 80 GB SXM4 GPU\u003c\/strong\u003e (Part Numbers: \u003cstrong\u003e699-2G506-0210-300\u003c\/strong\u003e, and equivalent PNs in the A100 80GB SXM4 family) delivers breakthrough acceleration for AI, data analytics, and HPC workloads.\u003c\/p\u003e\n\u003cp\u003eBuilt on the \u003cstrong\u003eAmpere architecture (GA100 GPU)\u003c\/strong\u003e, it features \u003cstrong\u003e80 GB of HBM2e memory\u003c\/strong\u003e, \u003cstrong\u003e2 TB\/s bandwidth\u003c\/strong\u003e, and \u003cstrong\u003e7,000+ CUDA cores\u003c\/strong\u003e, setting the standard for data center performance.\u003c\/p\u003e\n\u003cp\u003eEngineered for \u003cstrong\u003emulti-GPU NVLink scaling\u003c\/strong\u003e, this A100 SXM4 GPU provides up to \u003cstrong\u003e600 GB\/s interconnect bandwidth\u003c\/strong\u003e, enabling massive AI model training and scientific simulations with unmatched efficiency.\u003c\/p\u003e\n\u003cp\u003eWhether you’re powering \u003cstrong\u003eAI research\u003c\/strong\u003e, \u003cstrong\u003edeep learning clusters\u003c\/strong\u003e, or \u003cstrong\u003eHPC environments\u003c\/strong\u003e, the NVIDIA A100 SXM4 delivers consistent performance, scalability, and energy efficiency trusted by leading enterprises and research institutions.\u003c\/p\u003e\n\u003cp\u003e✔️ \u003cstrong\u003eIn Stock \u0026amp; Ready to Ship – Tested \u0026amp; Certified\u003c\/strong\u003e\u003c\/p\u003e\n\u003chr\u003e\n\u003ch3\u003e\n\u003cstrong\u003e⚙️ Product Specifications: NVIDIA Tesla A100 80 GB SXM4 GPU\u003c\/strong\u003e\u003cbr\u003e\u003ca href=\"https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/Data-Center\/a100\/pdf\/nvidia-a100-datasheet-us-nvidia-1758950-r4-web.pdf\" title=\"NVIDIA Tesla A100 80GB SXM4 GPU | Specs Sheet\" rel=\"noopener\" target=\"_blank\"\u003e\u003cstrong\u003eCLICK HERE FOR SPECIFICATIONS SHEET\u003c\/strong\u003e\u003c\/a\u003e\n\u003c\/h3\u003e\n\u003ctable style=\"width: 100.036%; height: 470.4px;\"\u003e\n\u003cthead\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003cth style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eSpecification\u003c\/strong\u003e\u003c\/th\u003e\n\u003cth style=\"width: 68.4848%; height: 19.6px;\"\u003e\u003cstrong\u003eDetails\u003c\/strong\u003e\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 39.2px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 39.2px;\"\u003e\u003cstrong\u003eModel \/ Part Numbers\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 39.2px;\"\u003e699-2G506-0210-300 (and equivalent A100 SXM4 80GB variants)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eArchitecture\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003eNVIDIA Ampere (GA100 GPU)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eCUDA Cores\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003e6,912\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eTensor Cores\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003e432\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eGPU Memory\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003e80 GB HBM2e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eMemory Bandwidth\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003e2,039 GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eFP64 Performance\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003e9.7 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eFP32 Performance\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003e19.5 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eTF32 (Tensor Float 32)\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003e156 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 39.2px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 39.2px;\"\u003e\u003cstrong\u003eFP16 \/ BF16 Performance\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 39.2px;\"\u003e312 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eINT8 Tensor Core\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003e1,248 TOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eForm Factor\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003eSXM4 module (for GPU-accelerated servers)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eNVLink Bandwidth\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003eUp to 600 GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eMIG Support\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003eUp to 7 instances per GPU\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eTDP\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003e400 W\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eCooling\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003ePassive (server chassis airflow required)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eDie Size \/ Transistors\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003e826 mm², ~54 B transistors\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eCertifications\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003eNVLink, MIG, DGX\/AEP compatible\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eWeight\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003eApprox. 2 lb (heatsink), 11 lb full module\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eCondition\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003eUsed \/ Tested\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 29.0553%; height: 19.6px;\"\u003e\u003cstrong\u003eAvailability\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 68.4848%; height: 19.6px;\"\u003eIn Stock – Ready to Ship\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003cstrong\u003e❓ Frequently Asked Questions (FAQs)\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003e\u003cstrong\u003eQ1: Which workloads benefit most from the A100 SXM4 GPU?\u003c\/strong\u003e\u003cbr\u003eA: Deep learning model training, AI inference, HPC simulations, and large-scale analytics all leverage the A100’s Ampere Tensor Core performance.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ2: Are there differences between the various A100 SXM4 part numbers?\u003c\/strong\u003e\u003cbr\u003eA: No - all equivalent PNs (e.g., 699-2G506-0210-300 and future variants) share identical specifications and performance. They mainly differ by regional, batch, or OEM labeling.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ3: What’s the cooling requirement?\u003c\/strong\u003e\u003cbr\u003eA: The SXM4 module uses passive cooling and requires robust server chassis airflow for proper thermal performance.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ4: Does it support Multi-Instance GPU (MIG)?\u003c\/strong\u003e\u003cbr\u003eA: Yes, up to seven isolated GPU instances per device for flexible workload partitioning.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ5: Is this compatible with NVIDIA DGX systems?\u003c\/strong\u003e\u003cbr\u003eA: Yes - it’s designed for DGX, AEP, and other enterprise GPU platforms using SXM4 sockets.\u003c\/p\u003e","brand":"NVIDIA","offers":[{"title":"Default Title","offer_id":47309196591333,"sku":"A100-SXM4-80GB","price":9900.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0252\/2280\/7632\/files\/A100_80GB_SXM4_GPU.png?v=1752777260"},{"product_id":"nvidia-hgx-h100-sxm5-8-gpu-board-935-24287-0301-000-board-new","title":"NVIDIA HGX H100 SXM5 8‑GPU Board (935‑24287‑0301‑000) Board - New","description":"\u003ch1\u003e\u003cstrong\u003eNVIDIA HGX H100 SXM5 8‑GPU Board (935‑24287‑0301‑000)\u003c\/strong\u003e\u003c\/h1\u003e\n\u003ch2\u003e\u003cstrong\u003eEnterprise-Grade 8‑Way H100 SXM5 AI Accelerator Platform\u003c\/strong\u003e\u003c\/h2\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan style=\"color: rgb(255, 42, 0);\"\u003eNote: This product is export restricted and may not be relocated or transferred without compliance with all applicable regulations.\u003c\/span\u003e\u003c\/strong\u003e\u003c\/p\u003e\n\u003chr\u003e\n\u003cp\u003eThe \u003cstrong\u003eNVIDIA HGX H100 SXM5 8‑GPU Board\u003c\/strong\u003e (part number \u003cstrong\u003e935‑24287‑0301‑000\u003c\/strong\u003e) integrates eight \u003cstrong\u003eNVIDIA Hopper‑based H100 SXM5 GPUs\u003c\/strong\u003e, each with \u003cstrong\u003e80 GB HBM3 memory\u003c\/strong\u003e, into a single high-density accelerator board. Built for \u003cstrong\u003eAI training\u003c\/strong\u003e, \u003cstrong\u003einference\u003c\/strong\u003e, and exascale HPC, it supports direct liquid cooling. With \u003cstrong\u003eNVLink\u003c\/strong\u003e at \u003cstrong\u003e900 GB\/s\u003c\/strong\u003e interconnect and 3.35 TB\/s memory bandwidth, this platform delivers unmatched scalability and performance.\u003c\/p\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003cstrong\u003e🔧 Product Specifications for \u003ca href=\"https:\/\/docs.nvidia.com\/dgx\/dgxh100-user-guide\/introduction-to-dgxh100.html\" title=\"NVIDIA HGX H100 SXM5 8‑GPU Board (935‑24287‑0301‑000) - Data Sheet\" rel=\"noopener\" target=\"_blank\"\u003eNVIDIA HGX H100 SXM5 8‑GPU Board (935‑24287‑0301‑000)\u003c\/a\u003e\u003c\/strong\u003e\u003c\/h3\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eFeature\u003c\/strong\u003e\u003c\/th\u003e\n\u003cth\u003e\u003cstrong\u003eDetails\u003c\/strong\u003e\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePart Name \/ Number\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eNVIDIA HGX H100 SXM5 8‑GPU Board – 935‑24287‑0301‑000\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eForm Factor\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eSXM5 (8 GPUs on single board, direct liquid-cooled)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eGPUs\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e8 × NVIDIA H100 SXM5, each 80 GB HBM3\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTotal GPU Memory\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e640 GB HBM3\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMemory Bandwidth\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e3.35 TB\/s per GPU via HBM3, \u003cem\u003eNVLink at 900 GB\/s per NVLink\u003c\/em\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCUDA Cores (Total)\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e8 × 16,896 = 135,168 cores\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTensor Cores (Total)\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e8 × 528 = 4,224 cores\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eGPU Interconnect\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eNVLink SXM5 — 900 GB\/s GPU ↔ GPU\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eForm Thermal Design Power\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eConfigurable up to 700 W per GPU\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCooling\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eDirect liquid cooling (equipment not included)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePower Interface\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eBoard-level power connectors (server-integrated power delivery)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePCIe Host Interface\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003ePCIe Gen 5 x16 interface (board to host)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMulti‑Instance GPU (MIG)\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eEach H100 supports up to 7 MIG instances\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eUse Cases\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eDistributed AI training, supercomputing, HPC clusters\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCertifications\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eHopper microarchitecture, NVLink-enabled, NVIDIA HGX\/DGX compliant\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003cstrong\u003e❓ Frequently Asked Questions (FAQs)\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003e\u003cstrong\u003eQ1: What is the total GPU memory on this board?\u003c\/strong\u003e\u003cbr\u003eA: The board provides \u003cstrong\u003e640 GB total HBM3 memory\u003c\/strong\u003e—80 GB across 8 SXM5 GPUs.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ2: How do the GPUs communicate within the board?\u003c\/strong\u003e\u003cbr\u003eA: Via \u003cstrong\u003eSXM5 NVLink\u003c\/strong\u003e, offering up to \u003cstrong\u003e900 GB\/s per connection\u003c\/strong\u003e, enabling high-speed GPU-to-GPU communication.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ3: What cooling solution does this board require?\u003c\/strong\u003e\u003cbr\u003eA: It requires \u003cstrong\u003edirect liquid cooling\u003c\/strong\u003e, typically implemented in purpose-built HX\/L48 chassis. (Board is liquid-cooled ready.)\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ4: Is this board plug-and-play with standard servers?\u003c\/strong\u003e\u003cbr\u003eA: No. It's designed for \u003cstrong\u003eHGX-compatible host systems\u003c\/strong\u003e with integrated power, liquid cooling, and server infrastructure.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ5: Can I run multi-instance workloads?\u003c\/strong\u003e\u003cbr\u003eA: Yes—each H100 supports up to \u003cstrong\u003e7 MIG instances\u003c\/strong\u003e, suitable for concurrent multi-tenant or mixed workloads.\u003c\/p\u003e","brand":"NVIDIA","offers":[{"title":"Default Title","offer_id":47309248495845,"sku":"NVIDIA-HGX-H100-SXM5","price":179000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0252\/2280\/7632\/files\/NVIDIA-HGX-H100-SXM5-8-GPU-Board.jpg?v=1752778219"},{"product_id":"nvidia-rtx-6000-ada-48gb-gddr6-professional-gpu-ai-rendering-amp-simulation","title":"NVIDIA RTX 6000 Ada 48GB GDDR6 Professional GPU – AI, Rendering \u0026amp; Simulation","description":"\u003ch2\u003e\u003cstrong\u003ePart Numbers Supported (Same Specs \u0026amp; Performance):\u003c\/strong\u003e\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003ch2\u003e\u003cstrong\u003e900-5G133-0150-001\u003c\/strong\u003e\u003c\/h2\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003ch2\u003e\u003cstrong\u003e8KCT1 (Dell OEM)\u003c\/strong\u003e\u003c\/h2\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan style=\"color: rgb(255, 42, 0);\"\u003eNote: This product is export restricted and may not be relocated or transferred without compliance with all applicable regulations.\u003c\/span\u003e\u003c\/strong\u003e\u003c\/p\u003e\n\u003cblockquote\u003e\n\u003cp\u003e\u003cspan style=\"color: rgb(255, 42, 0);\"\u003e\u003cstrong\u003e\u003cem\u003eAll above PNs refer to the same NVIDIA RTX 6000 Ada 48GB GPU with identical specifications and capabilities.\u003c\/em\u003e\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/blockquote\u003e\n\u003chr\u003e\n\u003cp\u003eThe \u003cstrong\u003eNVIDIA RTX 6000 Ada 48GB GPU\u003c\/strong\u003e is a high-performance professional graphics solution built on the Ada Lovelace architecture, engineered for AI development, 3D rendering, simulation, and data-intensive workflows. With 48GB of GDDR6 ECC memory, 18,176 CUDA Cores, and advanced ray-tracing and Tensor Core capabilities, it delivers unmatched performance for enterprise workstations, data science teams, and GPU-accelerated environments.\u003c\/p\u003e\n\u003chr\u003e\n\u003ch2\u003e⭐ \u003cstrong\u003eKey Features\u003c\/strong\u003e\n\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eNext-gen \u003cstrong\u003eAda Lovelace architecture\u003c\/strong\u003e for extreme performance gains\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e48GB GDDR6 ECC Memory\u003c\/strong\u003e for large datasets, AI models \u0026amp; complex scenes\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e18,176 CUDA Cores\u003c\/strong\u003e, 568 Tensor Cores \u0026amp; 142 RT Cores\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003ePCIe Gen 4.0 x16\u003c\/strong\u003e interface for high-bandwidth workloads\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eSupports \u003cstrong\u003eNVIDIA Omniverse, CUDA, RTX, AI \u0026amp; ML frameworks\u003c\/strong\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eQuad DisplayPort 1.4a\u003c\/strong\u003e outputs with HDR and 8K support\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eBuilt for \u003cstrong\u003eworkstations, media studios, AI labs, and enterprise R\u0026amp;D\u003c\/strong\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003chr\u003e\n\u003ch2\u003e📊 \u003cstrong\u003eTechnical Specifications \u003cbr\u003e\u003ca href=\"https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/design-visualization\/quadro-product-literature\/proviz-print-nvidia-rtx-a6000-datasheet-us-nvidia-1454980-r9-web%20(1).pdf\" title=\"NVIDIA RTX 6000 Ada 48GB GDDR6 Professional GPU – Specs Sheet\" rel=\"noopener\" target=\"_blank\"\u003eCLICK HERE FOR SPECS SHEET\u003c\/a\u003e\u003c\/strong\u003e\n\u003c\/h2\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eSpecification\u003c\/th\u003e\n\u003cth\u003eDetails\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eArchitecture\u003c\/td\u003e\n\u003ctd\u003eNVIDIA Ada Lovelace\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCUDA Cores\u003c\/td\u003e\n\u003ctd\u003e18,176\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTensor Cores\u003c\/td\u003e\n\u003ctd\u003e568\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRT Cores\u003c\/td\u003e\n\u003ctd\u003e142\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU Memory\u003c\/td\u003e\n\u003ctd\u003e48GB GDDR6 ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory Interface\u003c\/td\u003e\n\u003ctd\u003e384-bit\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory Bandwidth\u003c\/td\u003e\n\u003ctd\u003e960 GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eFP32 Performance\u003c\/td\u003e\n\u003ctd\u003e~91 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRT Performance\u003c\/td\u003e\n\u003ctd\u003e~210 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTensor Performance\u003c\/td\u003e\n\u003ctd\u003e~1,466 TFLOPS (with sparsity)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMax Power Consumption\u003c\/td\u003e\n\u003ctd\u003e300W\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSystem Interface\u003c\/td\u003e\n\u003ctd\u003ePCI Express 4.0 x16\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDisplay Outputs\u003c\/td\u003e\n\u003ctd\u003e4 × DisplayPort 1.4a\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eForm Factor\u003c\/td\u003e\n\u003ctd\u003eDual-slot, 10.5\"\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCooling\u003c\/td\u003e\n\u003ctd\u003eActive Air Cooling\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eVR Ready\u003c\/td\u003e\n\u003ctd\u003eYes\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSupported APIs\u003c\/td\u003e\n\u003ctd\u003eDirectX 12, OpenGL 4.6, Vulkan, CUDA, OpenCL\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003chr\u003e\n\u003ch2\u003e🧩 \u003cstrong\u003eIdeal Use Cases\u003c\/strong\u003e\n\u003c\/h2\u003e\n\u003cp\u003eThis GPU is designed for mission-critical, high-compute workflows such as:\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eAI training \u0026amp; inference\u003c\/strong\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eRendering \u0026amp; VFX Production (Cinema 4D, Maya, Blender, Unreal Engine)\u003c\/strong\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eDigital Twins \u0026amp; NVIDIA Omniverse\u003c\/strong\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eData Science, Simulation \u0026amp; Engineering Workloads\u003c\/strong\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eMedical Imaging, CAD, CAM \u0026amp; Scientific Modeling\u003c\/strong\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eEnterprise Workstations \u0026amp; GPU Virtualization\u003c\/strong\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003chr\u003e\n\u003ch2\u003e🆚 \u003cstrong\u003eRTX 6000 Ada vs RTX A6000 vs RTX A5000 - Comparison\u003c\/strong\u003e\n\u003c\/h2\u003e\n\u003cp\u003eChoosing the right NVIDIA workstation GPU depends on your AI, 3D, and rendering workload requirements. Here’s a quick comparison to help you decide:\u003c\/p\u003e\n\u003cdiv class=\"_tableContainer_1rjym_1\"\u003e\n\u003cdiv class=\"group _tableWrapper_1rjym_13 flex w-fit flex-col-reverse\"\u003e\n\u003ctable class=\"w-fit min-w-(--thread-content-width)\"\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eFeature \/ Model\u003c\/th\u003e\n\u003cth\u003e\u003cstrong\u003eRTX 6000 Ada (48GB)\u003c\/strong\u003e\u003c\/th\u003e\n\u003cth\u003eRTX A6000 (48GB)\u003c\/th\u003e\n\u003cth\u003eRTX A5000 (24GB)\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eArchitecture\u003c\/td\u003e\n\u003ctd\u003e\u003cstrong\u003eAda Lovelace (Latest)\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eAmpere\u003c\/td\u003e\n\u003ctd\u003eAmpere\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCUDA Cores\u003c\/td\u003e\n\u003ctd\u003e\u003cstrong\u003e18,176\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e10,752\u003c\/td\u003e\n\u003ctd\u003e8,192\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTensor Cores\u003c\/td\u003e\n\u003ctd\u003e\u003cstrong\u003e568 (4th Gen)\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e336 (3rd Gen)\u003c\/td\u003e\n\u003ctd\u003e256 (3rd Gen)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRT Cores\u003c\/td\u003e\n\u003ctd\u003e\u003cstrong\u003e142 (3rd Gen)\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e84 (2nd Gen)\u003c\/td\u003e\n\u003ctd\u003e64 (2nd Gen)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU Memory\u003c\/td\u003e\n\u003ctd\u003e\u003cstrong\u003e48GB GDDR6 ECC\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e48GB GDDR6 ECC\u003c\/td\u003e\n\u003ctd\u003e24GB GDDR6 ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory Bandwidth\u003c\/td\u003e\n\u003ctd\u003e\u003cstrong\u003e~960 GB\/s\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e768 GB\/s\u003c\/td\u003e\n\u003ctd\u003e600 GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMax FP32 Compute\u003c\/td\u003e\n\u003ctd\u003e\u003cstrong\u003e~91 TFLOPS\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e38.7 TFLOPS\u003c\/td\u003e\n\u003ctd\u003e27.8 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePower Consumption\u003c\/td\u003e\n\u003ctd\u003e300W\u003c\/td\u003e\n\u003ctd\u003e300W\u003c\/td\u003e\n\u003ctd\u003e230W\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVLink Support\u003c\/td\u003e\n\u003ctd\u003e❌ No\u003c\/td\u003e\n\u003ctd\u003e✅ Yes\u003c\/td\u003e\n\u003ctd\u003e✅ Yes\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eBest For\u003c\/td\u003e\n\u003ctd\u003e\u003cstrong\u003eAI, Simulation, 3D, LLMs, VFX, Omniverse\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eHigh-end 3D \u0026amp; Rendering\u003c\/td\u003e\n\u003ctd\u003eMid-range Rendering \u0026amp; CAD\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003ch3\u003e🔍 Quick Summary\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eRTX 6000 Ada\u003c\/strong\u003e → Best choice if you need maximum performance for \u003cstrong\u003eAI, 3D rendering, digital twins, Omniverse, and simulation workloads\u003c\/strong\u003e.\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eRTX A6000\u003c\/strong\u003e → Still a strong option for \u003cstrong\u003erendering, visualization, and multi-GPU NVLink setups\u003c\/strong\u003e.\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eRTX A5000\u003c\/strong\u003e → Ideal for \u003cstrong\u003emid-range VFX, CAD, BIM, engineering design\u003c\/strong\u003e, and budget-conscious workflows.\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003cstrong\u003e❓ Frequently Asked Questions\u003c\/strong\u003e\u003c\/h2\u003e\n\u003cp\u003e\u003cstrong\u003eQ1: Is the Dell part number (8KCT1) different from the standard NVIDIA retail version?\u003c\/strong\u003e\u003cbr\u003eA: No - both part numbers represent the same RTX 6000 Ada GPU with identical specs and performance. OEM versions may include different packaging or accessories but performance remains unchanged.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ2: Can the RTX 6000 Ada be used for AI model training?\u003c\/strong\u003e\u003cbr\u003eA: Yes, its 48GB ECC memory and Ada Tensor Cores make it highly suitable for LLMs, diffusion models, NLP, and computer vision workloads.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ3: Does it support NVLink?\u003c\/strong\u003e\u003cbr\u003eA: No - NVIDIA removed NVLink support in Ada-based workstation GPUs.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ4: Is this GPU compatible with servers and workstations?\u003c\/strong\u003e\u003cbr\u003eA: It is primarily designed for \u003cstrong\u003eprofessional workstations\u003c\/strong\u003e, though some servers support it if PCIe power and airflow requirements are met.\u003c\/p\u003e","brand":"NVIDIA","offers":[{"title":"Default Title","offer_id":47521679278309,"sku":"NVIDIA-RTX6000ADA-48GB","price":6500.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0252\/2280\/7632\/files\/NVIDIA-RTX6000ADA-48GB.jpg?v=1754317891"},{"product_id":"nvidia-rtx-6000-24gb-gddr6-workstation-gpu-turing-architecture-nvlink-ecc-memory","title":"NVIDIA RTX 6000 24GB GDDR6 Workstation GPU | Turing Architecture, NVLink, ECC Memory","description":"\u003ch1\u003e\u003cstrong\u003eNVIDIA RTX 6000 24 GB GPU – Turing Architecture for Professional Visualisation \u0026amp; AI\u003c\/strong\u003e\u003c\/h1\u003e\n\u003ch2\u003e\u003cstrong\u003eProfessional-Grade NVIDIA RTX 6000 24 GB GPU \u003cspan style=\"color: rgb(255, 42, 0);\"\u003e(PNs: 900-2G150-6240-030, R0Z45C, 0M1T16, M1T16) \u003c\/span\u003e– Ideal for Rendering, Simulation \u0026amp; AI\u003c\/strong\u003e\u003c\/h2\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan style=\"color: rgb(255, 42, 0);\"\u003eNote: \u003cspan\u003eThis product is export restricted and may not be relocated or transferred without compliance with all applicable regulations.\u003c\/span\u003e\u003c\/span\u003e\u003c\/strong\u003e\u003c\/p\u003e\n\u003chr\u003e\n\u003cp\u003eThe NVIDIA RTX 6000 24 GB GPU is a high-performance workstation graphics solution built on the Turing architecture. Featuring \u003cstrong\u003e24 GB GDDR6 ECC memory\u003c\/strong\u003e, \u003cstrong\u003e4,608 CUDA cores\u003c\/strong\u003e, \u003cstrong\u003e576 Tensor cores\u003c\/strong\u003e, and \u003cstrong\u003e72 RT cores\u003c\/strong\u003e, it enables professionals to handle large models, complex simulations, and real-time ray tracing with confidence.\u003c\/p\u003e\n\u003cp\u003eWhether you’re accelerating 3D rendering, scientific visualisation, or AI tasks, this GPU offers robust performance and reliability. The part numbers \u003cstrong\u003e900-2G150-6240-030\u003c\/strong\u003e, \u003cstrong\u003eR0Z45C\u003c\/strong\u003e, \u003cstrong\u003e0M1T16\u003c\/strong\u003e, and \u003cstrong\u003eM1T16\u003c\/strong\u003e all correspond to this configuration, ensuring consistent specifications and compatibility across variants.\u003c\/p\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003cstrong\u003e⚙ Product Specifications: NVIDIA RTX 6000 24 GB GPU\u003cbr\u003e\u003ca href=\"https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/design-visualization\/quadro-product-literature\/quadro-rtx-6000-us-nvidia-704093-r4-web.pdf\" title=\"NVIDIA RTX 6000 24GB GDDR6 - Specs Sheet\" rel=\"noopener\" target=\"_blank\"\u003eCLICK HERE FOR SPECIFICATIONS SHEET\u003c\/a\u003e\u003c\/strong\u003e\u003c\/h3\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003eSpecification\u003c\/th\u003e\n\u003cth\u003eDetails\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eModel \/ Part Numbers\u003c\/td\u003e\n\u003ctd\u003eRTX 6000 (900-2G150-6240-030, R0Z45C, 0M1T16, M1T16)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eArchitecture\u003c\/td\u003e\n\u003ctd\u003eNVIDIA Turing™ \u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCUDA Cores\u003c\/td\u003e\n\u003ctd\u003e4,608\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTensor Cores\u003c\/td\u003e\n\u003ctd\u003e576\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRT Cores\u003c\/td\u003e\n\u003ctd\u003e72\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU Memory\u003c\/td\u003e\n\u003ctd\u003e24 GB GDDR6 ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory Interface\u003c\/td\u003e\n\u003ctd\u003e384-bit\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory Bandwidth\u003c\/td\u003e\n\u003ctd\u003eUp to 672 GB\/s \u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSingle-Precision (FP32) Perf.\u003c\/td\u003e\n\u003ctd\u003e~16.3 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVLink Support\u003c\/td\u003e\n\u003ctd\u003eYes (for multi-GPU memory scaling)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSystem Interface\u003c\/td\u003e\n\u003ctd\u003ePCI Express 3.0 x16\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eDisplay Outputs\u003c\/td\u003e\n\u003ctd\u003e4 × DisplayPort 1.4 + 1 × VirtualLink (varies by board)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003ePower Consumption (TDP)\u003c\/td\u003e\n\u003ctd\u003e295 W board power\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eForm Factor\u003c\/td\u003e\n\u003ctd\u003eDual-slot, full height (4.4” H × 10.5” L)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUse Cases\u003c\/td\u003e\n\u003ctd\u003eProfessional rendering, VR\/AR simulation, AI visualization\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003chr\u003e\n\u003ch3\u003e💬 Frequently Asked Questions (FAQs)\u003c\/h3\u003e\n\u003cp\u003e\u003cstrong\u003eQ1: Are all listed part numbers identical in spec?\u003c\/strong\u003e\u003cbr\u003eYes - the PN variants \u003cstrong\u003e900-2G150-6240-030\u003c\/strong\u003e, \u003cstrong\u003eR0Z45C\u003c\/strong\u003e, \u003cstrong\u003e0M1T16\u003c\/strong\u003e, and \u003cstrong\u003eM1T16\u003c\/strong\u003e represent the same hardware configuration of the NVIDIA RTX 6000 24 GB GPU.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ2: Is the 24 GB memory size sufficient for professional workloads?\u003c\/strong\u003e\u003cbr\u003eAbsolutely. This GPU’s 24 GB GDDR6 ECC memory supports large 3D scenes, VR applications, and AI inference tasks. For even larger datasets or models, dual-GPU NVLink configurations are supported.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ3: Can this GPU be used in servers or only in workstations?\u003c\/strong\u003e\u003cbr\u003eWhile it’s designed for professional workstations, many servers configured for visualization, simulation, or AI workloads can integrate this card - provided adequate cooling and slot support.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ4: What distinguishes this GPU from the 48 GB variants?\u003c\/strong\u003e\u003cbr\u003eThe primary difference is memory capacity. The 48 GB variants provide more headroom for massive datasets, but the 24 GB variant still delivers the same core architecture and performance features.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ5: What certification or driver support is available?\u003c\/strong\u003e\u003cbr\u003eThis GPU is ISV-certified for major professional software (e.g., Autodesk, SolidWorks, Adobe) and uses NVIDIA’s Quadro\/RTX driver stack for workstation reliability.\u003c\/p\u003e","brand":"NVIDIA","offers":[{"title":"Default Title","offer_id":47521688223973,"sku":"RTX6000-24GB","price":1500.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0252\/2280\/7632\/files\/NVIDIA-RTX6000-24GB.png?v=1761140429"},{"product_id":"p100-900-2h400-6200-030-nvidia-tesla-p100-16gb-pcie-3-0-x-16-gpu","title":"P100: 900-2H400-6200-030 NVIDIA Tesla P100 16GB PCIe 3.0 x 16 GPU","description":"\u003ch1\u003e\u003cstrong\u003eNVIDIA Tesla P100 16GB PCIe GPU – 900-2H400-6200-030\u003c\/strong\u003e\u003c\/h1\u003e\n\u003chr\u003e\n\u003ch2\u003e\u003cstrong\u003eHigh-Performance Tesla P100 for AI, HPC \u0026amp; Deep Learning Workloads\u003c\/strong\u003e\u003c\/h2\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan style=\"color: rgb(255, 42, 0);\"\u003eNote: This product is export restricted and may not be relocated or transferred without compliance with all applicable regulations.\u003c\/span\u003e\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eThe \u003cstrong\u003eNVIDIA Tesla P100 16GB PCIe GPU (900-2H400-6200-030)\u003c\/strong\u003e delivers exceptional performance for data science, AI training, and HPC workloads. Built on the Pascal architecture, the P100 is engineered with 16GB of high-bandwidth HBM2 memory and supports 732 GB\/s memory bandwidth. With 3584 CUDA cores and PCIe Gen 3.0 x16 support, it accelerates complex computational workloads across data centers, workstations, and AI servers.\u003c\/p\u003e\n\u003cp\u003eDesigned for power-efficient performance, the P100 enables faster model training, simulation, and data analytics, making it ideal for academic, enterprise, and research applications.\u003c\/p\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003cstrong\u003e✅ Product Description for \u003ca href=\"https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/Data-Center\/tesla-product-literature\/NV-tesla-p100-pcie-PB-08248-001-v01.pdf\" title=\"NVIDIA Tesla P100 16GB PCIe GPU Specifications\" rel=\"noopener\" target=\"_blank\"\u003eNVIDIA Tesla P100 16GB PCIe GPU\u003c\/a\u003e\u003c\/strong\u003e\u003c\/h3\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eCategory\u003c\/strong\u003e\u003c\/th\u003e\n\u003cth\u003e\u003cstrong\u003eDetails\u003c\/strong\u003e\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePart Number\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e900-2H400-6200-030\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eGPU Architecture\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003ePascal\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCUDA Cores\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e3584\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMemory\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e16 GB HBM2\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMemory Bandwidth\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e732 GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eInterface\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003ePCI Express 3.0 x16\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eForm Factor\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eDual-slot, full-height\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMax Power Consumption\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e250W\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCompute Performance\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eUp to 10.6 TFLOPS (single-precision)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eECC Support\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eYes\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCooling\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003ePassive (requires system airflow)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTarget Applications\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eAI\/ML, HPC, Data Analytics, Simulation\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eSupported Platforms\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eLinux, Windows, CUDA 8.0+\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eDimensions\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e10.5\" x 4.4\" (267mm x 112mm)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eWeight\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e~1.76 lbs (0.8 kg)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003cstrong\u003e❓ Frequently Asked Questions (FAQs)\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003e\u003cstrong\u003e1. Is the Tesla P100 suitable for gaming?\u003c\/strong\u003e\u003cbr\u003eNo, the Tesla P100 is built for professional compute workloads such as AI, deep learning, and simulation, not gaming or consumer graphics.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Does it require additional cooling?\u003c\/strong\u003e\u003cbr\u003eYes. The P100 uses a passive heatsink design and requires adequate system airflow for cooling.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Can it be used in workstations?\u003c\/strong\u003e\u003cbr\u003eYes, as long as your workstation supports PCIe 3.0 x16 and has adequate power and airflow.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Does this card support ECC memory?\u003c\/strong\u003e\u003cbr\u003eYes, ECC (Error-Correcting Code) is supported for high data integrity in compute environments.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003e5. What's the difference between SXM and PCIe versions?\u003c\/strong\u003e\u003cbr\u003eThe PCIe version (this model) offers broader system compatibility, while the SXM2 form factor offers higher interconnect bandwidth in specialized systems.\u003ccode\u003e\u003c\/code\u003e\u003c\/p\u003e","brand":"NVIDIA","offers":[{"title":"Default Title","offer_id":47521722335461,"sku":"NVD-TESLA-P100-16GB-6200-030","price":495.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0252\/2280\/7632\/files\/P100900-2H400-6200-030nVidiaTeslaP10016GBPCIe3.0x16GPU_49c55b6d-2c24-43f5-a930-591821b63319.jpg?v=1754320642"},{"product_id":"nvidia-quadro-rtx-8000-48gb-gpu-gddr6-graphics-card-pcie-passive-cooling","title":"NVIDIA Quadro RTX 8000 48GB GPU, GDDR6 Graphics Card, PCIe Passive Cooling","description":"\u003ch1\u003e\u003cstrong\u003eNVIDIA Quadro RTX 8000 48GB GDDR6 Passive Cooling Graphics Card \u003cspan style=\"color: rgb(255, 42, 0);\"\u003e(PN's: 900-2G150-0150-030 \/ 490-BFSG \/ 8VJMK)\u003c\/span\u003e\u003c\/strong\u003e\u003c\/h1\u003e\n\u003ch2\u003e\u003cstrong\u003eNVIDIA Quadro RTX 8000 48GB Passive GPU - Designed for Data Centers and Professional Workloads\u003c\/strong\u003e\u003c\/h2\u003e\n\u003cp\u003e\u003cspan style=\"color: rgb(255, 42, 0);\"\u003e\u003cstrong\u003eNote: \u003cspan\u003eThis product is export restricted and may not be relocated or transferred without compliance with all applicable regulations.\u003c\/span\u003e\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003chr\u003e\n\u003cp\u003eThe \u003cstrong\u003eNVIDIA Quadro RTX 8000 48GB Passive Graphics Card\u003c\/strong\u003e — available under part numbers \u003cstrong\u003e900-2G150-0150-030\u003c\/strong\u003e, \u003cstrong\u003e490-BFSG\u003c\/strong\u003e, and \u003cstrong\u003e8VJMK\u003c\/strong\u003e — delivers enterprise-grade performance for \u003cstrong\u003eAI computing, professional rendering, simulation, and visualization\u003c\/strong\u003e.\u003c\/p\u003e\n\u003cp\u003ePowered by the \u003cstrong\u003eTuring architecture\u003c\/strong\u003e, it combines \u003cstrong\u003e48GB of GDDR6 ECC memory\u003c\/strong\u003e, \u003cstrong\u003e4,608 CUDA cores\u003c\/strong\u003e, and \u003cstrong\u003e576 Tensor cores\u003c\/strong\u003e to accelerate demanding workflows in \u003cstrong\u003edata centers and high-performance workstations\u003c\/strong\u003e.\u003c\/p\u003e\n\u003cp\u003eThis \u003cstrong\u003epassively cooled\u003c\/strong\u003e GPU is purpose-built for \u003cstrong\u003erackmount servers and multi-GPU environments\u003c\/strong\u003e, where thermal efficiency and continuous uptime are essential. Each part number variant represents identical performance and specifications, ensuring full compatibility and consistent results across deployments.\u003c\/p\u003e\n\u003chr\u003e\n\u003ch3\u003e\n\u003cstrong\u003e⚙️ Product Specifications: NVIDIA Quadro RTX 8000 48GB Passive Cooling\u003c\/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003ca href=\"https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/design-visualization\/quadro-product-literature\/NVIDIA-Quadro-RTX-8000-PCIe-Server-Card-PB-FINAL-1219.pdf\" title=\"NVIDIA Quadro RTX 8000 48GB Passive Cooling Data Sheet\" rel=\"noopener\" target=\"_blank\"\u003eCLICK HERE FOR DATA SHEET\u003c\/a\u003e\u003c\/strong\u003e\n\u003c\/h3\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eSpecification\u003c\/strong\u003e\u003c\/th\u003e\n\u003cth\u003e\u003cstrong\u003eDetails\u003c\/strong\u003e\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eGPU Architecture\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eNVIDIA Turing\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCUDA Cores\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e4608\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTensor Cores\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e576\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eRT Cores\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e72\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eGPU Memory\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e48GB GDDR6 ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMemory Interface\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e384-bit\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMemory Bandwidth\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e672 GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePeak FP32 Performance\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e16.3 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePeak FP16 Performance\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e33 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eDeep Learning Performance\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e130+ Tensor TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMax Power Consumption\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e250W\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eSystem Interface\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003ePCI Express 3.0 x16\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCooling\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003ePassive (requires chassis airflow)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eForm Factor\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eDual slot, 4.4\" x 10.5\"\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eNVLink Support\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eYes, up to 96GB combined memory\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eDisplay Outputs\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eNone (server-grade GPU)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eAPIs Supported\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eDirectX 12, OpenGL 4.5, Vulkan, CUDA\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eSupported Part Numbers\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e900-2G150-0150-030, 490-BFSG, 8VJMK\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003cstrong\u003e🔍 Why Choose the NVIDIA Quadro RTX 8000 48GB (All PNs)\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eIdentical specifications across all listed part numbers\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eEnterprise reliability for AI training, rendering, and simulation\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003ePassive cooling ideal for high-density servers\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eNVLink scalability up to 96GB total GPU memory\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eNVIDIA-certified for professional visualization workloads\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003cstrong\u003e🧠 Frequently Asked Questions\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003e\u003cstrong\u003eQ1: Are all part numbers 900-2G150-0150-030, 490-BFSG, and 8VJMK identical?\u003c\/strong\u003e\u003cbr\u003e✅ Yes — all three represent the \u003cstrong\u003esame Quadro RTX 8000 48GB GPU\u003c\/strong\u003e with identical performance, architecture, and features. They differ only by \u003cstrong\u003edistribution channel or OEM packaging\u003c\/strong\u003e.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ2: Does this GPU include active cooling?\u003c\/strong\u003e\u003cbr\u003eNo. It features \u003cstrong\u003epassive cooling\u003c\/strong\u003e and requires adequate server airflow for optimal operation.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ3: Can this be installed in a desktop PC?\u003c\/strong\u003e\u003cbr\u003eThis card is optimized for \u003cstrong\u003edata center and server environments\u003c\/strong\u003e. Desktop use is not recommended without specialized cooling infrastructure.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ4: What workloads is it best suited for?\u003c\/strong\u003e\u003cbr\u003eAI research, deep learning, simulation, CAD, 3D rendering, and virtual workstation deployments.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ5: What is the warranty coverage?\u003c\/strong\u003e\u003cbr\u003eTypically includes a \u003cstrong\u003e3-year manufacturer warranty\u003c\/strong\u003e or equivalent, depending on reseller terms.\u003c\/p\u003e","brand":"NVIDIA","offers":[{"title":"Default Title","offer_id":47622635356389,"sku":"NVIDIA-Quadro-RTX-8000","price":2400.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0252\/2280\/7632\/files\/NVIDIA_Quadro_RTX_8000_48GB_GPU_48_GB.jpg?v=1760626692"},{"product_id":"mma4z00-ns-nvidia-mellanox-infiniband-ndr-1x400gb-osfp-multi-mode-50m-hca-side-transceiver","title":"MMA4Z00-NS Nvidia Mellanox InfiniBand NDR 1x400Gb OSFP Multi-Mode 50m HCA-Side Transceiver","description":"\u003ch1 data-start=\"146\" data-end=\"212\"\u003e\u003cstrong\u003eMMA4Z00-NS NVIDIA Mellanox InfiniBand NDR 400Gb OSFP Transceiver\u003c\/strong\u003e\u003c\/h1\u003e\n\u003ch2 data-start=\"236\" data-end=\"308\"\u003e\u003cstrong\u003eHigh-Speed 400Gb InfiniBand OSFP Transceiver – 50m Multi-Mode HCA-Side\u003c\/strong\u003e\u003c\/h2\u003e\n\u003chr data-start=\"310\" data-end=\"313\"\u003e\n\u003cp data-start=\"339\" data-end=\"661\"\u003eThe \u003cstrong data-start=\"343\" data-end=\"357\"\u003eMMA4Z00-NS\u003c\/strong\u003e is a high-performance NVIDIA Mellanox InfiniBand NDR transceiver, designed for next-generation HPC and AI networking environments. With \u003cstrong data-start=\"494\" data-end=\"515\"\u003e400Gb\/s bandwidth\u003c\/strong\u003e, OSFP form factor, and multi-mode fiber support up to \u003cstrong data-start=\"570\" data-end=\"583\"\u003e50 meters\u003c\/strong\u003e, this transceiver ensures ultra-low latency and reliable data transmission.\u003c\/p\u003e\n\u003cp data-start=\"663\" data-end=\"1039\"\u003eBuilt for \u003cstrong data-start=\"673\" data-end=\"708\"\u003eInfiniBand NDR (Next Data Rate)\u003c\/strong\u003e networks, the MMA4Z00-NS supports \u003cstrong data-start=\"743\" data-end=\"768\"\u003eHCA-side connectivity\u003c\/strong\u003e, making it a crucial component for data centers, supercomputing, and AI clusters requiring extreme throughput. Its energy-efficient design and NVIDIA’s trusted Mellanox technology ensure long-lasting performance and seamless integration into demanding infrastructures.\u003c\/p\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003cstrong\u003e📊Product Specifications – MMA4Z00-NS NVIDIA Mellanox\u003cbr\u003e\u003ca href=\"https:\/\/docs.nvidia.com\/mma4z00-ns400-400gb-s-single-port-osfp-400gb-s-multimode-sr4-50m-transceiver.pdf\" target=\"_blank\" title=\"MMA4Z00-NS NVIDIA Mellanox Specifications\" rel=\"noopener\"\u003eCLICK HERE FOR MORE SPECIFICATIONS\u003c\/a\u003e\u003c\/strong\u003e\u003c\/h3\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eCategory\u003c\/strong\u003e\u003c\/th\u003e\n\u003cth\u003e\u003cstrong\u003eDetails\u003c\/strong\u003e\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePart Number\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eMMA4Z00-NS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eProduct Type\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eInfiniBand NDR Optical Transceiver\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eForm Factor\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eOSFP (Octal Small Form Factor Pluggable)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eData Rate\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e400Gb\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eLine Rate\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e2 × 200Gb\/s channels (NDR)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eConnector Type\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eOSFP\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCable Type\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eMulti-Mode Fiber (MMF)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMaximum Reach\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eUp to 50m\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eWavelength\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e850nm\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eApplication Side\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eHCA-side (Host Channel Adapter)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eEncoding\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003ePAM4 (Pulse Amplitude Modulation)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eProtocol Support\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eInfiniBand NDR\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eLatency\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eUltra-low (optimized for HPC\/AI workloads)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePower Consumption\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e~15–18W (typical, varies by deployment)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCooling Type\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003ePassive (no active fan)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eOperating Temperature\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e0°C to 70°C (32°F to 158°F)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eStorage Temperature\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e-40°C to 85°C (-40°F to 185°F)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCompliance\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eIEEE 802.3, RoHS, CE, FCC Class A\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCompatibility\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eNVIDIA Quantum-2 InfiniBand Switches \u0026amp; HCA Adapters\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePrimary Use Cases\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eHPC clusters, AI\/ML training, hyperscale data centers, supercomputing\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003chr\u003e\n\u003ch3 data-start=\"1759\" data-end=\"1787\"\u003e\u003cstrong\u003e❓ FAQs – MMA4Z00-NS\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"1789\" data-end=\"1913\"\u003e\u003cstrong data-start=\"1789\" data-end=\"1853\"\u003e1. What is the maximum supported distance of the MMA4Z00-NS?\u003c\/strong\u003e\u003cbr data-start=\"1853\" data-end=\"1856\"\u003eIt supports up to \u003cstrong data-start=\"1874\" data-end=\"1887\"\u003e50 meters\u003c\/strong\u003e using multi-mode fiber.\u003c\/p\u003e\n\u003cp data-start=\"1915\" data-end=\"2036\"\u003e\u003cstrong data-start=\"1915\" data-end=\"1967\"\u003e2. Is this transceiver compatible with Ethernet?\u003c\/strong\u003e\u003cbr data-start=\"1967\" data-end=\"1970\"\u003eNo, it is specifically designed for \u003cstrong data-start=\"2006\" data-end=\"2033\"\u003eInfiniBand NDR networks\u003c\/strong\u003e.\u003c\/p\u003e\n\u003cp data-start=\"2038\" data-end=\"2144\"\u003e\u003cstrong data-start=\"2038\" data-end=\"2086\"\u003e3. What form factor does the MMA4Z00-NS use?\u003c\/strong\u003e\u003cbr data-start=\"2086\" data-end=\"2089\"\u003eIt uses \u003cstrong data-start=\"2097\" data-end=\"2141\"\u003eOSFP (Octal Small Form Factor Pluggable)\u003c\/strong\u003e.\u003c\/p\u003e\n\u003cp data-start=\"2146\" data-end=\"2303\"\u003e\u003cstrong data-start=\"2146\" data-end=\"2193\"\u003e4. Can this be used in AI and HPC clusters?\u003c\/strong\u003e\u003cbr data-start=\"2193\" data-end=\"2196\"\u003eYes, it is built for \u003cstrong data-start=\"2217\" data-end=\"2267\"\u003ehigh-performance computing and AI data centers\u003c\/strong\u003e requiring \u003cstrong data-start=\"2278\" data-end=\"2300\"\u003e400Gb\/s throughput\u003c\/strong\u003e.\u003c\/p\u003e\n\u003cp data-start=\"2305\" data-end=\"2438\"\u003e\u003cstrong data-start=\"2305\" data-end=\"2353\"\u003e5. Is cooling required for this transceiver?\u003c\/strong\u003e\u003cbr data-start=\"2353\" data-end=\"2356\"\u003eIt is a \u003cstrong data-start=\"2364\" data-end=\"2390\"\u003epassive cooling design\u003c\/strong\u003e, so no additional cooling module is required.\u003c\/p\u003e","brand":"NVIDIA","offers":[{"title":"Default Title","offer_id":47779393339621,"sku":"MMA4Z00-NS","price":490.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0252\/2280\/7632\/files\/MMA4Z00-NS.jpg?v=1758807925"},{"product_id":"mcp7y00-n001-nvidia-mellanox-infiniband-ndr-osfp-to-2xosfp-1m-splitter-direct-attached-copper-cable","title":"MCP7Y00-N001 Nvidia Mellanox InfiniBand NDR OSFP to 2xOSFP 1m Splitter Direct Attached Copper Cable","description":"\u003ch1 data-start=\"102\" data-end=\"184\"\u003e\u003cstrong\u003eNVIDIA Mellanox MCP7Y00-N001 InfiniBand NDR OSFP to 2xOSFP 1m Splitter DAC Cable\u003c\/strong\u003e\u003c\/h1\u003e\n\u003ch2 data-start=\"226\" data-end=\"308\"\u003e\u003cstrong\u003eHigh-Performance NVIDIA Mellanox MCP7Y00-N001 400Gbps NDR 1m OSFP Splitter Cable\u003c\/strong\u003e\u003c\/h2\u003e\n\u003chr data-start=\"310\" data-end=\"313\"\u003e\n\u003cp data-start=\"345\" data-end=\"707\"\u003eThe \u003cstrong data-start=\"349\" data-end=\"381\"\u003eNVIDIA Mellanox MCP7Y00-N001\u003c\/strong\u003e is a high-performance \u003cstrong data-start=\"404\" data-end=\"454\"\u003eInfiniBand NDR 400Gbps OSFP splitter DAC cable\u003c\/strong\u003e designed to deliver ultra-low latency and reliable connectivity in modern data centers. This 1-meter passive direct-attach copper (DAC) cable splits one OSFP port into two OSFP ports, ensuring flexibility, scalability, and efficient port utilization.\u003c\/p\u003e\n\u003cp data-start=\"709\" data-end=\"948\"\u003eEngineered for NVIDIA Quantum-2 InfiniBand platforms, the MCP7Y00-N001 supports demanding workloads such as \u003cstrong data-start=\"817\" data-end=\"873\"\u003eHPC, AI, cloud computing, and hyperscale deployments\u003c\/strong\u003e, while maintaining excellent signal integrity and low power consumption.\u003c\/p\u003e\n\u003cp data-start=\"950\" data-end=\"1126\"\u003eIdeal for short-range connections, this OSFP splitter cable ensures \u003cstrong data-start=\"1018\" data-end=\"1080\"\u003ecost-effective, energy-efficient, and reliable performance\u003c\/strong\u003e for high-bandwidth networking environments.\u003c\/p\u003e\n\u003chr\u003e\n\u003ch3\u003e\n\u003cstrong\u003eProduct Specifications: NVIDIA Mellanox MCP7Y00-N001\u003c\/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003ca href=\"https:\/\/docs.nvidia.com\/mcp7y00-nxxx-800gb-s-twin-port-2x400g-osfp-to-2x400g-osfp-passive-dac-splitter-product-specifications.pdf\" target=\"_blank\" title=\"NVIDIA Mellanox MCP7Y00-N001\" rel=\"noopener\"\u003eCLICK HERE FOR MORE SPECIFICATIONS\u003c\/a\u003e\u003ca href=\"https:\/\/docs.nvidia.com\/mcp7y00-nxxx-800gb-s-twin-port-2x400g-osfp-to-2x400g-osfp-passive-dac-splitter-product-specifications.pdf\" target=\"_blank\" title=\"NVIDIA Mellanox MCP7Y00-N001\" rel=\"noopener\"\u003e\u003c\/a\u003e\u003c\/strong\u003e\n\u003c\/h3\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eCategory\u003c\/strong\u003e\u003c\/th\u003e\n\u003cth\u003e\u003cstrong\u003eDetails\u003c\/strong\u003e\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePart Number\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eMCP7Y00-N001\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eProduct Type\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eInfiniBand NDR Splitter DAC Cable\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eData Rate\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e400Gbps per OSFP port (aggregate throughput: up to 800Gbps across 2x OSFP)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eConnector Type\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1 × OSFP (male) to 2 × OSFP (male)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCable Technology\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003ePassive Direct Attach Copper (DAC)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCable Length\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1 meter\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eForm Factor\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eOSFP (Octal Small Form Factor Pluggable)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCompliance Standards\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eIEEE 802.3, InfiniBand NDR (Next Data Rate), SFF-8679, SFF-9402\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eSignal Integrity\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eEnhanced shielding for reduced EMI and improved performance\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eLatency\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eUltra-low latency (ideal for HPC \u0026amp; AI workloads)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePower Consumption\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003ePassive (nearly zero watts)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCompatibility\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eNVIDIA Quantum-2 InfiniBand Switches \u0026amp; Adapters\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eApplication\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eHPC clusters, AI\/ML workloads, Enterprise Data Centers, Cloud Computing\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eEnvironment\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eShort-reach, in-rack connectivity\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eOperating Temperature\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e0°C to 70°C\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eStorage Temperature\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e-40°C to 85°C\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eHumidity\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e10% to 85% non-condensing\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eBend Radius\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eMinimum 30mm (recommended for reliability)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eDurability\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e200 mating cycles minimum\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eEMC Certification\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eCE, FCC, RoHS-6 compliant\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eDimensions (Approx.)\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eCable diameter 6–8mm, OSFP connector standard size\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eWeight\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eApprox. 1.1 lbs (0.5 kg)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003chr\u003e\n\u003ch3 data-start=\"2088\" data-end=\"2134\"\u003e\u003cstrong\u003eFAQs for NVIDIA Mellanox MCP7Y00-N001\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp data-start=\"2136\" data-end=\"2237\"\u003e\u003cstrong data-start=\"2136\" data-end=\"2189\"\u003eQ1: What is the length of the MCP7Y00-N001 cable?\u003c\/strong\u003e\u003cbr data-start=\"2189\" data-end=\"2192\"\u003eIt is a 1-meter passive DAC splitter cable.\u003c\/p\u003e\n\u003cp data-start=\"2239\" data-end=\"2405\"\u003e\u003cstrong data-start=\"2239\" data-end=\"2291\"\u003eQ2: What devices are compatible with this cable?\u003c\/strong\u003e\u003cbr data-start=\"2291\" data-end=\"2294\"\u003eIt is designed for use with \u003cstrong data-start=\"2322\" data-end=\"2375\"\u003eNVIDIA Quantum-2 InfiniBand switches and adapters\u003c\/strong\u003e supporting OSFP interfaces.\u003c\/p\u003e\n\u003cp data-start=\"2407\" data-end=\"2534\"\u003e\u003cstrong data-start=\"2407\" data-end=\"2456\"\u003eQ3: Does this cable require additional power?\u003c\/strong\u003e\u003cbr data-start=\"2456\" data-end=\"2459\"\u003eNo, being a passive DAC cable, it consumes virtually no additional power.\u003c\/p\u003e\n\u003cp data-start=\"2536\" data-end=\"2727\"\u003e\u003cstrong data-start=\"2536\" data-end=\"2601\"\u003eQ4: What is the primary benefit of using this splitter cable?\u003c\/strong\u003e\u003cbr data-start=\"2601\" data-end=\"2604\"\u003eIt allows a single 400Gbps OSFP port to split into \u003cstrong data-start=\"2655\" data-end=\"2679\"\u003etwo OSFP connections\u003c\/strong\u003e, optimizing port density and cost-efficiency.\u003c\/p\u003e\n\u003cp data-start=\"2729\" data-end=\"2872\"\u003e\u003cstrong data-start=\"2729\" data-end=\"2791\"\u003eQ5: Is this cable suitable for long-distance connectivity?\u003c\/strong\u003e\u003cbr data-start=\"2791\" data-end=\"2794\"\u003eNo, this 1m DAC cable is optimized for \u003cstrong data-start=\"2833\" data-end=\"2869\"\u003eshort-reach, in-rack deployments\u003c\/strong\u003e.\u003c\/p\u003e","brand":"NVIDIA","offers":[{"title":"Default Title","offer_id":47779414409445,"sku":"MCP7Y00-N001","price":1100.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0252\/2280\/7632\/files\/MCP7Y00-N001.webp?v=1758809378"},{"product_id":"nvidia-a100-40gb-pcie-gpu-ampere-architecture-with-nvlink-mig","title":"NVIDIA A100 40GB PCIe GPU | Ampere Architecture with NVLink \u0026 MIG","description":"\u003ch1\u003e\u003cstrong\u003e🟩 NVIDIA A100 40GB PCIe GPU (Ampere Architecture, 40GB HBM2, NVLink, MIG Support) | \u003cspan style=\"color: rgb(255, 42, 0);\"\u003ePNs: 900-21001-0000-000, RH1X7\u003c\/span\u003e\u003c\/strong\u003e\u003c\/h1\u003e\n\u003ch2\u003e\u003cstrong\u003e🟨 High-Performance NVIDIA A100 40GB PCIe GPU for AI, Deep Learning \u0026amp; HPC Workloads\u003c\/strong\u003e\u003c\/h2\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan style=\"color: rgb(255, 42, 0);\"\u003eNote: This product is export restricted and may not be relocated or transferred without compliance with all applicable regulations.\u003c\/span\u003e\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eThe \u003cstrong\u003eNVIDIA A100 40GB PCIe GPU\u003c\/strong\u003e (Part Numbers: \u003cstrong\u003e900-21001-0000-000\u003c\/strong\u003e, \u003cstrong\u003eRH1X7\u003c\/strong\u003e) delivers enterprise-grade acceleration for AI training, inference, and high-performance computing. Built on NVIDIA’s \u003cstrong\u003eAmpere architecture\u003c\/strong\u003e, it provides breakthrough performance with \u003cstrong\u003e40GB HBM2 memory\u003c\/strong\u003e, \u003cstrong\u003emulti-instance GPU (MIG)\u003c\/strong\u003e capability, and \u003cstrong\u003ethird-generation Tensor Cores\u003c\/strong\u003e.\u003c\/p\u003e\n\u003cp\u003eThe PCIe form factor makes it ideal for flexible deployment in \u003cstrong\u003edata centers\u003c\/strong\u003e, \u003cstrong\u003eAI clusters\u003c\/strong\u003e, and \u003cstrong\u003eGPU servers\u003c\/strong\u003e that demand scalable performance and reliability without the thermal limits of SXM modules.\u003c\/p\u003e\n\u003chr\u003e\n\u003ch3\u003e\n\u003cstrong\u003e⚙️ Product Specifications: NVIDIA A100 40GB PCIe GPU\u003c\/strong\u003e\u003cbr\u003e\u003ca rel=\"noopener\" title=\"NVIDIA A100 40GB PCIe GPU - Specs Sheet\" href=\"https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/Data-Center\/a100\/pdf\/A100-PCIE-Prduct-Brief.pdf\" target=\"_blank\"\u003e\u003cstrong\u003eCLICK HERE FOR SPECIFICATIONS SHEET\u003c\/strong\u003e\u003c\/a\u003e\n\u003c\/h3\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eSpecification\u003c\/strong\u003e\u003c\/th\u003e\n\u003cth\u003e\u003cstrong\u003eDetails\u003c\/strong\u003e\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eModel \/ Part Numbers\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eNVIDIA A100 40GB PCIe (900-21001-0000-000, RH1X7)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eGPU Architecture\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eNVIDIA Ampere (GA100 GPU)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCUDA Cores\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e6,912\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTensor Cores\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e432 (Third Generation)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eGPU Memory\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e40GB HBM2\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMemory Bandwidth\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1,555 GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eFP64 Performance\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e9.7 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eFP32 Performance\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e19.5 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTF32 (Tensor Float32)\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e78 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eFP16 \/ BF16 Performance\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e156 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eINT8 Tensor Core Performance\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e624 TOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eForm Factor\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003ePCI Express Gen 4.0 x16\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCooling\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003ePassive (requires server airflow)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMIG Support\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eYes (up to 7 instances)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eNVLink Support\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eYes (up to 600 GB\/s via NVLink Bridge)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTDP\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e250W\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eDimensions\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eDual-slot, 267mm length\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCertifications\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eData center–qualified, DGX\/Certified servers\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eWeight\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e~3 lbs (approximate module weight)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003cstrong\u003e❓ Frequently Asked Questions (FAQs)\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003e\u003cstrong\u003eQ1: What is the NVIDIA A100 40GB PCIe GPU best used for?\u003c\/strong\u003e\u003cbr\u003eA1: It’s optimized for \u003cstrong\u003eAI training, inference, HPC, data analytics\u003c\/strong\u003e, and \u003cstrong\u003elarge-scale machine learning workloads\u003c\/strong\u003e requiring maximum performance in PCIe-based systems.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ2: How does it differ from the A100 80GB SXM4 version?\u003c\/strong\u003e\u003cbr\u003eA2: The PCIe version operates at \u003cstrong\u003e250W\u003c\/strong\u003e vs \u003cstrong\u003e400W\u003c\/strong\u003e for SXM4 and supports \u003cstrong\u003estandard PCIe servers\u003c\/strong\u003e, offering easier integration but slightly lower bandwidth.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ3: Does it support Multi-Instance GPU (MIG)?\u003c\/strong\u003e\u003cbr\u003eA3: Yes, you can divide one A100 GPU into \u003cstrong\u003eup to 7 independent GPU instances\u003c\/strong\u003e for optimized resource sharing.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ4: Can I connect two A100 PCIe GPUs using NVLink?\u003c\/strong\u003e\u003cbr\u003eA4: Yes, the A100 PCIe supports \u003cstrong\u003eNVLink Bridge\u003c\/strong\u003e connections (sold separately) for up to \u003cstrong\u003e600 GB\/s GPU-to-GPU bandwidth\u003c\/strong\u003e.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ5: What cooling requirements does it have?\u003c\/strong\u003e\u003cbr\u003eA5: The A100 PCIe uses \u003cstrong\u003epassive cooling\u003c\/strong\u003e, so ensure adequate \u003cstrong\u003echassis airflow\u003c\/strong\u003e in your server or workstation for optimal performance.\u003c\/p\u003e","brand":"NVIDIA","offers":[{"title":"Default Title","offer_id":47925683388645,"sku":"A100-40GB-PCIE-GPU","price":7800.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0252\/2280\/7632\/files\/NVIDIA-A100-40GB-PCIE-GPU.png?v=1760711342"},{"product_id":"nvidia-a100-80gb-pcie-gpu-ampere-architecture-with-nvlink-mig-support","title":"NVIDIA A100 80GB PCIe GPU | Ampere Architecture with NVLink \u0026 MIG Support","description":"\u003ch1\u003e\u003cstrong\u003eNVIDIA A100 80GB PCIe GPU – Ampere Architecture for AI, HPC \u0026amp; Data Analytics\u003c\/strong\u003e\u003c\/h1\u003e\n\u003ch2\u003e\u003cstrong\u003eHigh-Performance NVIDIA A100 80GB PCIe GPU for Deep Learning, HPC \u0026amp; Data Center Acceleration \u003cspan style=\"color: rgb(255, 42, 0);\"\u003e(PN: 900-21001-0020-000)\u003c\/span\u003e\u003c\/strong\u003e\u003c\/h2\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan style=\"color: rgb(255, 42, 0);\"\u003eNote: \u003cspan\u003eThis product is export restricted and may not be relocated or transferred without compliance with all applicable regulations.\u003c\/span\u003e\u003c\/span\u003e\u003c\/strong\u003e\u003c\/p\u003e\n\u003chr\u003e\n\u003cp\u003eThe \u003cstrong\u003eNVIDIA A100 80GB PCIe GPU (Part Number: 900-21001-0020-000)\u003c\/strong\u003e delivers next-generation AI acceleration, unmatched performance, and exceptional scalability for modern data centers. Built on the \u003cstrong\u003eNVIDIA Ampere architecture\u003c\/strong\u003e, it provides up to \u003cstrong\u003e80GB of high-bandwidth HBM2e memory\u003c\/strong\u003e and supports \u003cstrong\u003eMulti-Instance GPU (MIG)\u003c\/strong\u003e technology, allowing multiple workloads to run simultaneously with optimal efficiency.\u003c\/p\u003e\n\u003cp\u003eWith \u003cstrong\u003eNVLink\u003c\/strong\u003e, \u003cstrong\u003ePCIe Gen4\u003c\/strong\u003e, and \u003cstrong\u003e2,039 GB\/s of memory bandwidth\u003c\/strong\u003e, the A100 PCIe 80GB GPU empowers demanding AI training, HPC simulations, and data analytics tasks. Designed for data center servers and cloud environments, it offers flexible deployment for both inference and training at scale.\u003c\/p\u003e\n\u003cp\u003eWhether you're powering machine learning models, running deep neural networks, or accelerating scientific computing, the A100 PCIe 80GB GPU is a cornerstone for high-performance, energy-efficient GPU computing.\u003c\/p\u003e\n\u003chr\u003e\n\u003ch3\u003e\n\u003cstrong\u003e📊 Product Specifications: NVIDIA A100 80GB PCIe GPU\u003c\/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003ca rel=\"noopener\" title=\"NVIDIA A100 80GB PCIe GPU - Specs Sheet\" href=\"https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/Data-Center\/a100\/pdf\/PB-10577-001_v02.pdf\" target=\"_blank\"\u003eCLICK HERE FOR SPECIFICATIONS SHEET\u003c\/a\u003e\u003ca rel=\"noopener\" title=\"NVIDIA A100 80GB PCIe GPU - Specs Sheet\" href=\"https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/Data-Center\/a100\/pdf\/PB-10577-001_v02.pdf\" target=\"_blank\"\u003e\u003c\/a\u003e\u003c\/strong\u003e\n\u003c\/h3\u003e\n\u003ctable style=\"width: 100.036%; height: 460.8px;\"\u003e\n\u003cthead\u003e\n\u003ctr style=\"height: 10px;\"\u003e\n\u003cth style=\"width: 28.4292%; height: 10px;\"\u003e\u003cstrong\u003eSpecification\u003c\/strong\u003e\u003c\/th\u003e\n\u003cth style=\"width: 69.1109%; height: 10px;\"\u003e\u003cstrong\u003eDetails\u003c\/strong\u003e\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 28.4292%; height: 19.6px;\"\u003e\u003cstrong\u003eModel \/ Part Number\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 69.1109%; height: 19.6px;\"\u003eNVIDIA A100 80GB PCIe GPU (900-21001-0020-000)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 28.4292%; height: 19.6px;\"\u003e\u003cstrong\u003eArchitecture\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 69.1109%; height: 19.6px;\"\u003eNVIDIA Ampere (GA100 GPU)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 28.4292%; height: 19.6px;\"\u003e\u003cstrong\u003eCUDA Cores\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 69.1109%; height: 19.6px;\"\u003e6,912\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 28.4292%; height: 19.6px;\"\u003e\u003cstrong\u003eTensor Cores\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 69.1109%; height: 19.6px;\"\u003e432 (3rd Gen)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 28.4292%; height: 19.6px;\"\u003e\u003cstrong\u003eGPU Memory\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 69.1109%; height: 19.6px;\"\u003e80GB HBM2e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 28.4292%; height: 19.6px;\"\u003e\u003cstrong\u003eMemory Bandwidth\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 69.1109%; height: 19.6px;\"\u003e2,039 GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 28.4292%; height: 19.6px;\"\u003e\u003cstrong\u003eNVLink Support\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 69.1109%; height: 19.6px;\"\u003eYes, via NVLink Bridge (PCIe variant supports limited NVLink)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 39.2px;\"\u003e\n\u003ctd style=\"width: 28.4292%; height: 39.2px;\"\u003e\u003cstrong\u003eMIG (Multi-Instance GPU)\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 69.1109%; height: 39.2px;\"\u003eUp to 7 GPU instances\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 28.4292%; height: 19.6px;\"\u003e\u003cstrong\u003eFP64 Performance\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 69.1109%; height: 19.6px;\"\u003e9.7 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 28.4292%; height: 19.6px;\"\u003e\u003cstrong\u003eFP32 Performance\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 69.1109%; height: 19.6px;\"\u003e19.5 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 28.4292%; height: 19.6px;\"\u003e\u003cstrong\u003eTF32 (Tensor Float 32)\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 69.1109%; height: 19.6px;\"\u003e156 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 28.4292%; height: 19.6px;\"\u003e\u003cstrong\u003eFP16 \/ BF16 Tensor\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 69.1109%; height: 19.6px;\"\u003e312 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 28.4292%; height: 19.6px;\"\u003e\u003cstrong\u003eINT8 Tensor\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 69.1109%; height: 19.6px;\"\u003e1,248 TOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 28.4292%; height: 19.6px;\"\u003e\u003cstrong\u003eForm Factor\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 69.1109%; height: 19.6px;\"\u003ePCI Express Gen4 x16\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 28.4292%; height: 19.6px;\"\u003e\u003cstrong\u003eCooling\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 69.1109%; height: 19.6px;\"\u003ePassive (requires server airflow)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 28.4292%; height: 19.6px;\"\u003e\u003cstrong\u003eTDP\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 69.1109%; height: 19.6px;\"\u003e300 W\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 28.4292%; height: 19.6px;\"\u003e\u003cstrong\u003eDimensions\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 69.1109%; height: 19.6px;\"\u003eFull-height, full-length dual-slot card\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 28.4292%; height: 19.6px;\"\u003e\u003cstrong\u003eWeight\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 69.1109%; height: 19.6px;\"\u003e~4.9 lbs (2.2 kg)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 28.4292%; height: 19.6px;\"\u003e\u003cstrong\u003eSupported Frameworks\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 69.1109%; height: 19.6px;\"\u003eTensorFlow, PyTorch, Caffe, MXNet, CUDA, and more\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 28.4292%; height: 19.6px;\"\u003e\u003cstrong\u003eCertifications\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 69.1109%; height: 19.6px;\"\u003eNVLink Ready, MIG Enabled, DGX Certified\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 39.2px;\"\u003e\n\u003ctd style=\"width: 28.4292%; height: 39.2px;\"\u003e\u003cstrong\u003eUse Case\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 69.1109%; height: 39.2px;\"\u003eAI training\/inference, HPC workloads, data analytics, cloud computing\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003cstrong\u003e💬 Frequently Asked Questions (FAQs)\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003e\u003cstrong\u003eQ1: What is the difference between the A100 80GB PCIe and SXM4 models?\u003c\/strong\u003e\u003cbr\u003eA1: The PCIe model offers lower power (300 W vs. 400 W), easier server integration, and broader compatibility, while SXM4 provides higher bandwidth and tighter NVLink scaling.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ2: Does the A100 80GB PCIe support NVLink?\u003c\/strong\u003e\u003cbr\u003eA2: Yes, but with limited bandwidth compared to the SXM4 variant — ideal for multi-GPU setups in PCIe server configurations.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ3: What makes the A100 80GB ideal for AI workloads?\u003c\/strong\u003e\u003cbr\u003eA3: With Tensor Core acceleration and MIG partitioning, it handles training, inference, and analytics with exceptional efficiency.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ4: Can I use this GPU in a standard workstation?\u003c\/strong\u003e\u003cbr\u003eA4: It’s primarily designed for data center servers with sufficient airflow, not consumer workstations.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ5: What warranty coverage is available?\u003c\/strong\u003e\u003cbr\u003eA5: Most authorized resellers offer a 3-year limited manufacturer or refurbished warranty depending on condition.\u003c\/p\u003e","brand":"NVIDIA","offers":[{"title":"Default Title","offer_id":47925705081061,"sku":"NVIDIA-A100-80GB-PCIE-GPU","price":18900.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0252\/2280\/7632\/files\/NVIDIA-A100-80GB-PCIE-GPU.jpg?v=1760712634"},{"product_id":"nvidia-rtx-a5000-24gb-gddr6-graphics-card-professional-gpu-for-ai-rendering-design","title":"NVIDIA RTX A5000 24GB GDDR6 Graphics Card | Professional GPU for AI, Rendering \u0026 Design","description":"\u003ch1\u003e\u003cstrong\u003eNVIDIA RTX A5000 GPU – 24GB GDDR6, Ampere Architecture for Professional Workloads\u003c\/strong\u003e\u003c\/h1\u003e\n\u003ch2\u003e\u003cstrong\u003eHigh-Performance NVIDIA RTX A5000 24GB GPU for AI, Deep Learning, and Professional Visualization \u003cspan style=\"color: rgb(255, 42, 0);\"\u003e(PNs: 900-5G132-1700-000, 5V10Y65009)\u003c\/span\u003e\u003c\/strong\u003e\u003c\/h2\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan style=\"color: rgb(255, 42, 0);\"\u003eNote: \u003cspan\u003eThis product is export restricted and may not be relocated or transferred without compliance with all applicable regulations.\u003c\/span\u003e\u003c\/span\u003e\u003c\/strong\u003e\u003c\/p\u003e\n\u003chr\u003e\n\u003cp\u003eThe \u003cstrong\u003eNVIDIA RTX A5000 24GB GPU\u003c\/strong\u003e delivers the perfect balance of power, performance, and reliability for professionals working across AI, visualization, rendering, and data science. Powered by the \u003cstrong\u003eNVIDIA Ampere architecture\u003c\/strong\u003e, it features \u003cstrong\u003e8192 CUDA cores\u003c\/strong\u003e, \u003cstrong\u003e256 Tensor Cores\u003c\/strong\u003e, and \u003cstrong\u003e24GB of GDDR6 ECC memory\u003c\/strong\u003e, enabling real-time ray tracing, accelerated AI inferencing, and complex simulation workflows.\u003c\/p\u003e\n\u003cp\u003eWhether you’re designing 3D models, training neural networks, or visualizing complex datasets, the RTX A5000 is built to handle it all with workstation-class stability.\u003c\/p\u003e\n\u003cp\u003eEquipped with \u003cstrong\u003ePCIe 4.0\u003c\/strong\u003e, \u003cstrong\u003eQuad DisplayPort 1.4a\u003c\/strong\u003e, and \u003cstrong\u003eNVLink scalability\u003c\/strong\u003e, the RTX A5000 ensures seamless integration for large-scale projects and multi-GPU configurations - ideal for creative studios, enterprise AI labs, and high-end engineering environments.\u003c\/p\u003e\n\u003chr\u003e\n\u003ch3\u003e\n\u003cstrong\u003e📊 Product Specifications: NVIDIA RTX A5000 24GB GPU\u003c\/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003ca href=\"https:\/\/resources.nvidia.com\/en-us-briefcase-for-datasheets\/nvidia-rtx-a5000-dat-1\" title=\"NVIDIA RTX A5000 24GB GPU - Specs Sheet\" rel=\"noopener\" target=\"_blank\"\u003eCLICK HERE FOR SPECIFICATIONS SHEET\u003c\/a\u003e\u003ca href=\"https:\/\/resources.nvidia.com\/en-us-briefcase-for-datasheets\/nvidia-rtx-a5000-dat-1\" title=\"NVIDIA RTX A5000 24GB GPU - Specs Sheet\" rel=\"noopener\" target=\"_blank\"\u003e\u003c\/a\u003e\u003c\/strong\u003e\n\u003c\/h3\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eSpecification\u003c\/strong\u003e\u003c\/th\u003e\n\u003cth\u003e\u003cstrong\u003eDetails\u003c\/strong\u003e\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eModel \/ Part Numbers\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eNVIDIA RTX A5000 (900-5G132-1700-000, 5V10Y65009)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eArchitecture\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eNVIDIA Ampere\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCUDA Cores\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e8,192\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTensor Cores\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e256 (3rd Generation)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eRT Cores\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e64 (2nd Generation)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eGPU Memory\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e24GB GDDR6 ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMemory Interface Width\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e384-bit\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMemory Bandwidth\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e768 GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eSingle-Precision Performance (FP32)\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eUp to 27.8 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eRT Core Performance\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eUp to 54.2 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTensor Performance (FP16)\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eUp to 222.2 TFLOPS\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eNVLink Support\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eYes (2-way NVLink with NVIDIA Bridge)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eDisplay Connectors\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e4 × DisplayPort 1.4a\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMaximum Digital Resolution\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e7680 × 4320 (8K @ 60 Hz)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePCI Express Interface\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003ePCIe 4.0 x16\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eForm Factor\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eFull-height, full-length dual-slot\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCooling\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eActive cooling (blower-style)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePower Consumption (TDP)\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e230 W\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePower Connector\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1 × 8-pin PCIe\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eSupported APIs\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eDirectX 12 Ultimate, OpenGL 4.6, Vulkan 1.3, CUDA, OpenCL\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eVR Ready\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eYes\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eUse Cases\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eAI development, ML training, rendering, CAD, simulation, design visualization\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003cstrong\u003e💬 Frequently Asked Questions (FAQs)\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003e\u003cstrong\u003eQ1: What’s the difference between RTX A5000 and RTX A6000?\u003c\/strong\u003e\u003cbr\u003eA1: The A6000 offers 48GB of VRAM and slightly higher core counts, but the A5000 delivers similar professional performance at a more cost-effective price point.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ2: Is the RTX A5000 suitable for AI training?\u003c\/strong\u003e\u003cbr\u003eA2: Yes, it supports Tensor Cores for AI and ML acceleration, making it an excellent option for model training, inference, and data science workflows.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ3: Does this card support NVLink?\u003c\/strong\u003e\u003cbr\u003eA3: Yes, you can pair two RTX A5000 GPUs using an NVLink Bridge for expanded memory and higher performance.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ4: Can I use the A5000 in a workstation or server?\u003c\/strong\u003e\u003cbr\u003eA4: Absolutely - it’s compatible with both professional workstations and rack-mount systems supporting PCIe 4.0 x16 slots.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ5: What warranty or condition options are available?\u003c\/strong\u003e\u003cbr\u003eA5: Warranty coverage varies depending on condition (new or refurbished). Please check your specific listing on Network Outlet for details.\u003c\/p\u003e","brand":"NVIDIA","offers":[{"title":"Default Title","offer_id":47925767700709,"sku":"RTX-A5000-24GB-GPU","price":2500.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0252\/2280\/7632\/files\/NVIDIA-RTX-A5000-24GB-GPU.jpg?v=1760715646"},{"product_id":"nvidia-h100-nvl-94-gb-pcie-gpu-hopper-architecture-for-ai-large-model-training","title":"NVIDIA H100 NVL 94 GB PCIe GPU – Hopper Architecture for AI \u0026 Large-Model Training","description":"\u003ch1\u003e\u003cstrong\u003eNVIDIA H100 NVL 94 GB PCIe GPU \u003cspan style=\"color: rgb(255, 42, 0);\"\u003e(PN: 699-21010-0210-700)\u003c\/span\u003e\u003c\/strong\u003e\u003c\/h1\u003e\n\u003ch2\u003e\u003cstrong\u003eEnterprise-Grade NVIDIA H100 NVL 94 GB PCIe GPU – Designed for LLMs, AI Training \u0026amp; HPC Workloads\u003c\/strong\u003e\u003c\/h2\u003e\n\u003cp\u003e\u003cspan style=\"color: rgb(255, 42, 0);\"\u003e\u003cstrong\u003eNote: This product is export restricted and may not be relocated or transferred without compliance with all applicable regulations.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003chr\u003e\n\u003cp\u003eThe NVIDIA H100 NVL 94 GB PCIe GPU (Part Number: 699-21010-0210-700) is built on the cutting-edge Hopper architecture and engineered for the most demanding AI, large language model (LLM), and HPC workloads. With 94 GB of high-bandwidth HBM3 (or high-capacity HBM2e variant) memory and NVLink-enabled scaling, this PCIe accelerator delivers unmatched performance in data-center servers.\u003c\/p\u003e\n\u003cp\u003eOptimized for multi-GPU clusters, the H100 NVL supports enormous model sizes and massive throughput, enabling enterprises to push the boundary of AI training and inference. Whether you’re deploying large-scale transformer models, generative AI, or scientific simulations, the H100 NVL 94 GB delivers the scale and performance needed for next-gen workloads.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eKey benefits:\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eMassive 94 GB memory for large model parameters and datasets\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003ePCIe form-factor (x16) simplifies integration into standard GPU servers\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eNVLink bridge support for high inter‐GPU bandwidth\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003ePassive\/optimized cooling (server airflow required) for dense rack deployments\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eBuilt for enterprise workflows including AI training, inference, HPC and data analytics\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003chr\u003e\n\u003ch3\u003e\n\u003cstrong\u003e⚙ Product Specifications: NVIDIA H100 NVL 94 GB PCIe GPU\u003c\/strong\u003e\u003cbr\u003e\u003ca href=\"https:\/\/www.nvidia.com\/content\/dam\/en-zz\/Solutions\/Data-Center\/h100\/PB-11773-001_v01.pdf\" title=\"NVIDIA H100 NVL 94 GB PCIe GPU – Specs Sheet\" rel=\"noopener\" target=\"_blank\"\u003e\u003cstrong\u003eCLICK HERE FOR SPECIFICATIONS SHEET\u003c\/strong\u003e\u003c\/a\u003e\n\u003c\/h3\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eSpecification\u003c\/strong\u003e\u003c\/th\u003e\n\u003cth\u003e\u003cstrong\u003eDetails\u003c\/strong\u003e\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eModel \/ Part Number\u003c\/td\u003e\n\u003ctd\u003eNVIDIA H100 NVL 94 GB PCIe (PN: 699-21010-0210-700)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eArchitecture\u003c\/td\u003e\n\u003ctd\u003eNVIDIA Hopper™ (GH100 GPU)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU Memory\u003c\/td\u003e\n\u003ctd\u003e94 GB HBM3 \/ high-capacity memory\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory Bandwidth\u003c\/td\u003e\n\u003ctd\u003e~3.9 TB\/s (for 94 GB variant)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eForm Factor\u003c\/td\u003e\n\u003ctd\u003ePCIe x16 dual-slot passive cooler\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVLink \u0026amp; Interconnect\u003c\/td\u003e\n\u003ctd\u003eNVLink support (600 GB\/s inter-GPU)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMulti-Instance GPU (MIG)\u003c\/td\u003e\n\u003ctd\u003eUp to 7 GPU instances supported\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTDP \/ Cooling Requirements\u003c\/td\u003e\n\u003ctd\u003e~400 W (depending on variant); passive cooling requires server airflow\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eUse Case\u003c\/td\u003e\n\u003ctd\u003eLarge-model AI training, inference, HPC simulations, data analytics\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCompatibility\u003c\/td\u003e\n\u003ctd\u003eEnterprise servers, data-center racks, NVLink-enabled clusters\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003cstrong\u003e💬 FAQs (Frequently Asked Questions)\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003e\u003cstrong\u003eQ1: What types of workloads is the H100 NVL 94 GB ideal for?\u003c\/strong\u003e\u003cbr\u003eA1: It excels at large language model (LLM) training\/inference, deep learning, HPC simulations, and data-analytics at scale.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ2: What is the difference between the 80 GB and 94 GB variants of H100?\u003c\/strong\u003e\u003cbr\u003eA2: The 94 GB variant offers increased memory capacity (~14% higher) and often higher memory bandwidth. The architecture remains Hopper, but the memory stack and bandwidth differ.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ3: Does this card work in a standard workstation?\u003c\/strong\u003e\u003cbr\u003eA3: Although PCIe form-factor, the H100 NVL is designed for enterprise servers with strong cooling and power delivery. Standard workstations may not suffice without proper infrastructure.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ4: Is NVLink supported for multi-GPU setups?\u003c\/strong\u003e\u003cbr\u003eA4: Yes — NVLink bridges allow high bandwidth between GPUs in multi-GPU servers.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ5: What is required for cooling this GPU?\u003c\/strong\u003e\u003cbr\u003eA5: The card uses passive cooling (or very high quality airflow). A suitable server chassis with adequate airflow and cooling is required to maintain reliability and performance.\u003c\/p\u003e","brand":"NVIDIA","offers":[{"title":"Default Title","offer_id":47939902800101,"sku":"H100-NVL-94GB-PCIE","price":29900.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0252\/2280\/7632\/files\/NVIDIA-H100-NVL-94GB-PCIe.jpg?v=1761043950"},{"product_id":"nvidia-l40s-48gb-gpu-ada-lovelace-for-ai-llms-rendering-virtualization","title":"NVIDIA L40S 48GB GPU – Ada Lovelace for AI, LLMs, Rendering \u0026 Virtualization","description":"\u003ch1\u003e\u003cstrong\u003eNVIDIA L40S 48GB GPU – AI, LLM Training, Rendering \u0026amp; Virtualization Performance\u003c\/strong\u003e\u003c\/h1\u003e\n\u003ch2\u003e\u003cstrong\u003eHigh-Performance NVIDIA L40S 48GB GPU for AI, LLMs, Rendering \u0026amp; VDI \u003cspan style=\"color: rgb(255, 42, 0);\"\u003e(PNs: 900-2G133-0080-000, 7WK28, 900-2G133-0180-030)\u003c\/span\u003e\u003c\/strong\u003e\u003c\/h2\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan style=\"color: rgb(255, 42, 0);\"\u003eNote: This product is export restricted and may not be relocated or transferred without compliance with all applicable regulations.\u003c\/span\u003e\u003c\/strong\u003e\u003c\/p\u003e\n\u003chr\u003e\n\u003cp\u003eThe \u003cstrong\u003eNVIDIA L40S 48GB GPU\u003c\/strong\u003e is a next-generation accelerated computing solution designed for high-performance AI training, large language models (LLMs), graphics rendering, simulation, and virtual desktop infrastructure (VDI). Built on the advanced \u003cstrong\u003eAda Lovelace architecture\u003c\/strong\u003e, the L40S delivers up to \u003cstrong\u003e1.2X faster AI training\u003c\/strong\u003e and \u003cstrong\u003e2X faster inference performance\u003c\/strong\u003e than the previous NVIDIA L40. With \u003cstrong\u003e48GB GDDR6 memory\u003c\/strong\u003e, 18,176 CUDA cores, 568 Tensor cores, and 142 RT cores, it is engineered for the most demanding enterprise, data center, and cloud workloads.\u003c\/p\u003e\n\u003cp\u003eOptimized for \u003cstrong\u003eAI compute, visualization, Omniverse, Virtual Workstations\u003c\/strong\u003e, and \u003cstrong\u003eGenerative AI\u003c\/strong\u003e, the L40S accelerates end-to-end workflows—from model training to real-time rendering and deployment at scale. This GPU supports \u003cstrong\u003ePCIe Gen 4.0\u003c\/strong\u003e, delivers exceptional energy efficiency per watt, and integrates seamlessly into enterprise servers and GPU-powered AI infrastructure.\u003c\/p\u003e\n\u003chr\u003e\n\u003ch3\u003e\n\u003cstrong\u003e🔧 Product Specifications – NVIDIA L40S 48GB GPU\u003c\/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003ca href=\"https:\/\/resources.nvidia.com\/en-us-l40s\/l40s-datasheet-28413?ncid=no-ncid\" title=\"NVIDIA L40S 48GB GPU - Specs Sheet\" rel=\"noopener\" target=\"_blank\"\u003eCLICK HERE FOR SPECIFICATIONS SHEET\u003c\/a\u003e\u003c\/strong\u003e\n\u003c\/h3\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eSpecification\u003c\/strong\u003e\u003c\/th\u003e\n\u003cth\u003e\u003cstrong\u003eDetails\u003c\/strong\u003e\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003eModel\u003c\/td\u003e\n\u003ctd\u003eNVIDIA L40S 48GB\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eArchitecture\u003c\/td\u003e\n\u003ctd\u003eAda Lovelace\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCUDA Cores\u003c\/td\u003e\n\u003ctd\u003e18,176\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTensor Cores\u003c\/td\u003e\n\u003ctd\u003e568 (4th Gen)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eRT Cores\u003c\/td\u003e\n\u003ctd\u003e142 (3rd Gen)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eGPU Memory\u003c\/td\u003e\n\u003ctd\u003e48GB GDDR6 ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMemory Bandwidth\u003c\/td\u003e\n\u003ctd\u003e864 GB\/s\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eInterface\u003c\/td\u003e\n\u003ctd\u003ePCIe 4.0 x16\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMax Power (TDP)\u003c\/td\u003e\n\u003ctd\u003e350W\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eNVENC \/ NVDEC\u003c\/td\u003e\n\u003ctd\u003e4x NVENC + AV1 Encode \/ Decode\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eMulti-Instance GPU (MIG) Support\u003c\/td\u003e\n\u003ctd\u003eYes\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eVirtualization\u003c\/td\u003e\n\u003ctd\u003eSupports NVIDIA vGPU, VDI \u0026amp; Omniverse\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eForm Factor\u003c\/td\u003e\n\u003ctd\u003eDual-slot GPU (Passive cooling for server use)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eCooling\u003c\/td\u003e\n\u003ctd\u003ePassive – requires server airflow\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eTarget Workloads\u003c\/td\u003e\n\u003ctd\u003eAI, LLM Training \u0026amp; Inference, Rendering, VDI, Omniverse\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003eSupported Frameworks\u003c\/td\u003e\n\u003ctd\u003eTensorRT, CUDA, cuDNN, VMware, RedHat, Windows, Linux\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003chr\u003e\n\u003ch3\u003e🧾 \u003cstrong\u003eCompatible Part Numbers (Same Specs)\u003c\/strong\u003e\n\u003c\/h3\u003e\n\u003cblockquote\u003e\n\u003cp\u003eThese part numbers represent the \u003cstrong\u003esame NVIDIA L40S 48GB GPU\u003c\/strong\u003e with identical specifications.\u003c\/p\u003e\n\u003c\/blockquote\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e900-2G133-0080-000\u003c\/strong\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e7WK28\u003c\/strong\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e900-2G133-0180-030\u003c\/strong\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cem\u003eAll part numbers listed above refer to the same NVIDIA L40S 48GB model with identical performance and specifications. Minor differences may relate to OEM packaging or system compatibility labeling.\u003c\/em\u003e\u003c\/p\u003e\n\u003chr\u003e\n\u003ch3\u003e❓ \u003cstrong\u003eFrequently Asked Questions (FAQs)\u003c\/strong\u003e\n\u003c\/h3\u003e\n\u003cp\u003e\u003cstrong\u003eQ1: What is the NVIDIA L40S mainly used for?\u003c\/strong\u003e\u003cbr\u003eA: The L40S is ideal for AI training and inference (including LLMs), real-time rendering, Omniverse, visual computing, simulation, and VDI workloads.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ2: How is the L40S different from the original NVIDIA L40?\u003c\/strong\u003e\u003cbr\u003eA: The L40S delivers significantly higher performance, including nearly \u003cstrong\u003e2× AI inference throughput\u003c\/strong\u003e and improved training speeds, making it more suitable for Generative AI and LLM workloads.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ3: Can the L40S be used for multi-user virtualization or VDI?\u003c\/strong\u003e\u003cbr\u003eA: Yes - it supports \u003cstrong\u003eNVIDIA vGPU\u003c\/strong\u003e, making it suitable for virtual workstations and enterprise VDI environments.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ4: Does the L40S support AV1 encoding?\u003c\/strong\u003e\u003cbr\u003eA: Yes - it includes next-gen AV1 encoding\/decoding capabilities, ideal for streaming, media workloads, and virtual content creation.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003eQ5: Is external cooling required?\u003c\/strong\u003e\u003cbr\u003eA: No external cooling is required; however, this GPU requires proper \u003cstrong\u003eserver airflow\u003c\/strong\u003e because it uses passive cooling.\u003c\/p\u003e","brand":"NVIDIA","offers":[{"title":"Default Title","offer_id":47962492403941,"sku":"NVIDIA-L40S-48GB-GPU","price":8900.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0252\/2280\/7632\/files\/NVIDIA-L40S-48GB-GPU.jpg?v=1761230283"},{"product_id":"dell-poweredge-xe9680-ai-gpu-server-8-nvidia-hgx-h100-80gb-sxm","title":"Dell PowerEdge XE9680 AI GPU Server – 8× NVIDIA HGX H100 80GB SXM","description":"\u003ch1\u003e\u003cstrong\u003eDell PowerEdge XE9680 AI GPU Server with NVIDIA HGX H100\u003c\/strong\u003e\u003c\/h1\u003e\n\u003ch2\u003e\u003cstrong\u003eDell PowerEdge XE9680 AI GPU Server – 8× NVIDIA HGX H100 80GB SXM | Dual Intel Xeon Platinum | High-Performance AI \u0026amp; HPC Platform\u003c\/strong\u003e\u003c\/h2\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan style=\"color: rgb(255, 42, 0);\"\u003eNote: This product is export restricted and may not be relocated or transferred without compliance with all applicable regulations.\u003c\/span\u003e\u003c\/strong\u003e\u003c\/p\u003e\n\u003chr\u003e\n\u003cp\u003eThe \u003cstrong\u003eDell PowerEdge XE9680\u003c\/strong\u003e is a flagship \u003cstrong\u003eAI-optimized GPU server\u003c\/strong\u003e engineered for extreme performance, scalability, and reliability. Designed to power \u003cstrong\u003elarge-scale AI training, deep learning, high-performance computing (HPC), and generative AI workloads\u003c\/strong\u003e, the XE9680 delivers unmatched compute density with \u003cstrong\u003e8× NVIDIA HGX H100 80GB SXM GPUs\u003c\/strong\u003e in a single chassis.\u003c\/p\u003e\n\u003cp\u003ePowered by \u003cstrong\u003edual Intel Xeon Platinum 8462Y+ processors\u003c\/strong\u003e and equipped with \u003cstrong\u003e2TB DDR5 ECC memory\u003c\/strong\u003e, the XE9680 enables rapid model training, inference, and data-intensive workloads. With advanced networking, enterprise-grade management via \u003cstrong\u003eiDRAC9 Enterprise\u003c\/strong\u003e, and massive redundant power capacity, the Dell XE9680 is the ideal platform for AI labs, hyperscale data centers, research institutions, and enterprise AI deployments.\u003c\/p\u003e\n\u003chr\u003e\n\u003ch3\u003e\n\u003cstrong\u003e🔹 Product Specifications for Dell PowerEdge XE9680 AI GPU Server\u003c\/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003ca href=\"https:\/\/www.delltechnologies.com\/asset\/en-in\/products\/servers\/technical-support\/poweredge-xe9680-spec-sheet.pdf\" title=\"Dell XE9680 – AI GPU Server - Data Sheet\" target=\"_blank\"\u003eCLICK HERE FOR DATA SHEET\u003c\/a\u003e\u003c\/strong\u003e\n\u003c\/h3\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eSpecification\u003c\/strong\u003e\u003c\/th\u003e\n\u003cth\u003e\u003cstrong\u003eDetails\u003c\/strong\u003e\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eBrand\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eDell\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eProduct Line\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003ePowerEdge\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eModel\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eXE9680\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eServer Type\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eAI GPU Server \/ HPC Server\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eForm Factor\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eRack-mount (High-density chassis)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eProcessor\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e2× Intel Xeon Platinum 8462Y+\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCPU Cores\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eHigh-core count architecture (Sapphire Rapids)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMemory Installed\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e2048GB (32×64GB) DDR5 ECC Registered\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMemory Type\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003ePC5-4800 RDIMM\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eGPU Configuration\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e8× NVIDIA HGX H100 80GB SXM\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eGPU Interconnect\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eNVIDIA NVLink\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTotal GPU Memory\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e640GB HBM3\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eNetworking (Add-on)\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1× Mellanox MT2910 ConnectX-7\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eOnboard Networking\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e2× Broadcom 5720 Dual-Port 1GbE\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eManagement\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eiDRAC 9 Enterprise\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eStorage Support\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eNVMe \/ SAS \/ SATA (configurable)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eRAID Support\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eSoftware \u0026amp; hardware RAID options\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePCIe Support\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003ePCIe Gen5\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePower Supply\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e6× 2800W Hot-swappable PSUs\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eRedundancy\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003ePower, cooling, and management redundancy\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCooling\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eHigh-performance air-cooled system\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eOperating Systems Supported\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eLinux, VMware, NVIDIA AI Enterprise\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eVirtualization\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eVMware, KVM, container platforms\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eSecurity Features\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eSecure Boot, TPM 2.0, iDRAC Security\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eUse Case Optimization\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eAI Training, AI Inference, HPC, LLMs\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003c\/td\u003e\n\u003ctd\u003e\u003cbr\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003cstrong\u003e🔹 Key Features\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e8× NVIDIA HGX H100 80GB SXM GPUs\u003c\/strong\u003e for AI and deep learning\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eDual Intel Xeon Platinum 8462Y+ CPUs\u003c\/strong\u003e for maximum compute throughput\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e2TB DDR5 ECC Registered Memory (PC5-4800)\u003c\/strong\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eOptimized for \u003cstrong\u003eGenerative AI, LLMs, HPC, and scientific computing\u003c\/strong\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eHigh-bandwidth \u003cstrong\u003eMellanox ConnectX-7\u003c\/strong\u003e networking\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eEnterprise management with \u003cstrong\u003eiDRAC 9 Enterprise\u003c\/strong\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eMassive \u003cstrong\u003e6× 2800W redundant power supplies\u003c\/strong\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eDesigned for \u003cstrong\u003econtinuous 24×7 mission-critical workloads\u003c\/strong\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003cstrong\u003e🔹 AI, ML \u0026amp; HPC Use Cases\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003e\u003cstrong\u003eIdeal For:\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eGenerative AI \u0026amp; Large Language Models (LLMs)\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eDeep learning training \u0026amp; inference\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eScientific simulations \u0026amp; research\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eHigh-performance computing (HPC)\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eFinancial modeling \u0026amp; risk analysis\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eAutonomous systems \u0026amp; computer vision\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eEnterprise AI \u0026amp; data science platforms\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003cstrong\u003e🔹 FAQs (Frequently Asked Questions)\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003e\u003cstrong\u003e1. What makes the Dell XE9680 ideal for AI workloads?\u003cbr\u003e\u003c\/strong\u003eThe XE9680 supports \u003cstrong\u003e8× NVIDIA H100 SXM GPUs\u003c\/strong\u003e, offering massive parallel compute, NVLink interconnects, and ultra-high memory bandwidth for AI training.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Does the XE9680 support NVIDIA AI Enterprise?\u003cbr\u003e\u003c\/strong\u003eYes, it is fully compatible with \u003cstrong\u003eNVIDIA AI Enterprise\u003c\/strong\u003e and GPU-accelerated frameworks.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Can the memory be expanded further?\u003cbr\u003e\u003c\/strong\u003eThe system already supports \u003cstrong\u003e2TB DDR5\u003c\/strong\u003e, with additional configurations available depending on requirements.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. What networking options are supported?\u003cbr\u003e\u003c\/strong\u003eIt includes \u003cstrong\u003eMellanox ConnectX-7\u003c\/strong\u003e for high-speed data transfer and dual onboard 1GbE ports.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003e5. Is this server suitable for 24×7 data center use?\u003cbr\u003e\u003c\/strong\u003eAbsolutely. It includes \u003cstrong\u003eredundant power, cooling, and enterprise-grade management\u003c\/strong\u003e.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003e6. Can this server be customized?\u003cbr\u003e\u003c\/strong\u003eYes, storage, networking, OS, and software stack can be customized.\u003c\/p\u003e","brand":"Dell","offers":[{"title":"Default Title","offer_id":48181887172837,"sku":"Dell-XE9680","price":249000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0252\/2280\/7632\/files\/DellXE9680.png?v=1766344504"},{"product_id":"supermicro-sys-821ge-tnhr-ai-gpu-server-nvidia-h100","title":"Supermicro SYS-821GE-TNHR AI GPU Server – NVIDIA H100","description":"\u003ch1\u003e\u003cstrong\u003eSupermicro SYS-821GE-TNHR AI GPU Server\u003c\/strong\u003e\u003c\/h1\u003e\n\u003ch2\u003e\u003cstrong\u003eSupermicro SYS-821GE-TNHR AI GPU Server – NVIDIA H100, Dual Intel Xeon Platinum, High-Density AI \u0026amp; HPC Platform\u003c\/strong\u003e\u003c\/h2\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan style=\"color: rgb(255, 42, 0);\"\u003eNote: This product is export restricted and may not be relocated or transferred without compliance with all applicable regulations.\u003c\/span\u003e\u003c\/strong\u003e\u003c\/p\u003e\n\u003chr\u003e\n\u003cp\u003eThe \u003cstrong\u003eSupermicro SYS-821GE-TNHR\u003c\/strong\u003e is a high-performance \u003cstrong\u003eAI GPU server\u003c\/strong\u003e purpose-built for demanding workloads such as \u003cstrong\u003eartificial intelligence training, deep learning, large language models (LLMs), and high-performance computing (HPC)\u003c\/strong\u003e. Designed with Supermicro’s proven GPU-optimized architecture, this platform delivers exceptional compute density, memory bandwidth, and networking performance.\u003c\/p\u003e\n\u003cp\u003ePowered by \u003cstrong\u003edual Intel Xeon Platinum 8480+ processors\u003c\/strong\u003e, \u003cstrong\u003e2TB DDR5 memory\u003c\/strong\u003e, and \u003cstrong\u003eNVIDIA H100 GPU acceleration\u003c\/strong\u003e, the SYS-821GE-TNHR provides enterprise-grade reliability and scalability. With ultra-fast \u003cstrong\u003eNVMe storage\u003c\/strong\u003e and \u003cstrong\u003e400Gb networking via NVIDIA\/Mellanox ConnectX-7\u003c\/strong\u003e, this server is ideal for AI research labs, hyperscale data centers, and enterprise AI deployments.\u003c\/p\u003e\n\u003chr\u003e\n\u003ch3\u003e\n\u003cstrong\u003e🔹 Product Specifications for Supermicro SYS-821GE-TNHR AI GPU Server – NVIDIA H100\u003c\/strong\u003e\u003cbr\u003e\u003cstrong\u003e\u003ca href=\"https:\/\/www.supermicro.com\/en\/products\/system\/datasheet\/SYS-821GE-TNHR\" title=\"Supermicro SYS-821GE-TNHR AI GPU Server – Data Sheet\" rel=\"noopener\" target=\"_blank\"\u003eCLICK HERE FOR DATA SHEET\u003c\/a\u003e\u003c\/strong\u003e\n\u003c\/h3\u003e\n\u003ctable\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth\u003e\u003cstrong\u003eSpecification\u003c\/strong\u003e\u003c\/th\u003e\n\u003cth\u003e\u003cstrong\u003eDetails\u003c\/strong\u003e\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eBrand\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eSupermicro\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eModel\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eSYS-821GE-TNHR\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eServer Type\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eAI GPU Server \/ HPC Server\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eForm Factor\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eRack-mount, GPU-optimized chassis\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eBuild Type\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eCTO (Configure-To-Order)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eProcessor\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e2× Intel Xeon Platinum 8480+\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCPU Architecture\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eIntel Sapphire Rapids\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMemory Installed\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e32× 64GB DDR5-4400\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTotal Memory\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e2 TB DDR5 ECC Registered\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eMemory Type\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eDDR5-4400 RDIMM\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eGPU Configuration\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1× NVIDIA H100 GPU\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eGPU Architecture\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eNVIDIA Hopper\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eGPU Memory\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e80GB HBM3\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eStorage Configuration\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e4× 7.68TB NVMe SSD\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eTotal Storage Capacity\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e30.72TB NVMe\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eRAID Support\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eSoftware RAID \/ NVMe RAID options\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eNetworking (Primary)\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e8× NVIDIA Mellanox ConnectX-7 400Gb\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eNetworking (Secondary)\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1× NVIDIA Mellanox ConnectX-6 200Gb\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePCIe Support\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003ePCIe Gen5\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eExpansion Slots\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eGPU \u0026amp; high-speed NIC optimized\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eManagement\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eSupermicro IPMI \/ Redfish\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eSecurity Features\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eSecure Boot, TPM 2.0\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003ePower Supply\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eRedundant high-efficiency PSUs\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eCooling\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eHigh-performance air cooling\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eOperating Systems Supported\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eLinux, AI\/ML frameworks, virtualization\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eUse Case Optimization\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eAI Training, AI Inference, HPC, LLMs\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd\u003e\u003cstrong\u003eDeployment\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eData center, AI labs, cloud environments\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003chr\u003e\n\u003ch2\u003e🔹 \u003cstrong\u003eKey Features\u003c\/strong\u003e\n\u003c\/h2\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eNVIDIA H100 GPU acceleration\u003c\/strong\u003e for AI \u0026amp; deep learning\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eDual Intel Xeon Platinum 8480+ CPUs\u003c\/strong\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eMassive \u003cstrong\u003e2TB DDR5 ECC memory\u003c\/strong\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eHigh-speed \u003cstrong\u003eNVMe storage\u003c\/strong\u003e for AI datasets\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003e400GbE \u0026amp; 200GbE networking\u003c\/strong\u003e for low-latency data transfer\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eOptimized for \u003cstrong\u003eLLMs, generative AI, and HPC\u003c\/strong\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eEnterprise-grade redundancy and cooling\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eDesigned for 24×7 mission-critical workloads\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003chr\u003e\n\u003ch2\u003e🔹 \u003cstrong\u003eAI, ML \u0026amp; HPC Use Cases\u003c\/strong\u003e\n\u003c\/h2\u003e\n\u003ch3\u003eIdeal For:\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eGenerative AI \u0026amp; Large Language Models (LLMs)\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eDeep learning model training\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eAI inference pipelines\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eScientific simulations\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eFinancial modeling \u0026amp; analytics\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eAutonomous systems \u0026amp; computer vision\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eEnterprise AI platforms\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003cstrong\u003e🔹 FAQs (Frequently Asked Questions)\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003e\u003cstrong\u003e1. Is the SYS-821GE-TNHR suitable for AI training?\u003cbr\u003e\u003c\/strong\u003eYes, it is optimized for AI and deep learning workloads using \u003cstrong\u003eNVIDIA H100 GPU acceleration\u003c\/strong\u003e.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Is this a CTO (Configure-To-Order) server?\u003cbr\u003e\u003c\/strong\u003eYes. The SYS-821GE-TNHR is a \u003cstrong\u003eCTO chassis\u003c\/strong\u003e, allowing customization of CPU, memory, GPU, storage, and networking.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. What type of memory does it support?\u003cbr\u003e\u003c\/strong\u003eIt supports \u003cstrong\u003eDDR5-4400 ECC Registered memory\u003c\/strong\u003e, with configurations up to \u003cstrong\u003e2TB\u003c\/strong\u003e.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. What networking options are available?\u003cbr\u003e\u003c\/strong\u003eThis configuration includes \u003cstrong\u003e8× 400Gb ConnectX-7\u003c\/strong\u003e and \u003cstrong\u003e1× 200Gb ConnectX-6\u003c\/strong\u003e, ideal for AI clusters.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003e5. Can it support virtualization and container platforms?\u003cbr\u003e\u003c\/strong\u003eYes, it supports Linux, containerized AI workloads, and virtualization environments.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003e6. Is this server suitable for 24×7 operation?\u003cbr\u003e\u003c\/strong\u003eAbsolutely. It is designed with \u003cstrong\u003eenterprise-grade cooling, redundancy, and reliability\u003c\/strong\u003e.\u003c\/p\u003e","brand":"Super Micro","offers":[{"title":"Default Title","offer_id":48181896151269,"sku":"SYS-821GE-TNHR","price":247000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0252\/2280\/7632\/files\/Supermicro-SYS-821GE-TNHR.jpg?v=1766345965"},{"product_id":"dell-poweredge-xe9680-ai-gpu-server-8-nvidia-hgx-h100-80gb-sxm-400gb-networking","title":"Dell PowerEdge XE9680 AI GPU Server – 8× NVIDIA HGX H100 80GB SXM – 400GB Networking","description":"\u003ch1\u003e\u003cstrong\u003eDell PowerEdge XE9680 AI GPU Server with 400GB Networking\u003c\/strong\u003e\u003c\/h1\u003e\n\u003ch2\u003e\u003cstrong\u003e8× NVIDIA HGX H100 80GB SXM Platform for Large-Scale AI \u0026amp; HPC\u003cbr\u003e\u003c\/strong\u003e\u003c\/h2\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan style=\"color: rgb(255, 42, 0);\"\u003eNote: This product is export restricted and may not be relocated or transferred without compliance with all applicable regulations.\u003c\/span\u003e\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp\u003eThe \u003cstrong\u003eDell PowerEdge XE9680 AI GPU Server\u003c\/strong\u003e is Dell’s flagship AI platform, purpose-built to support the most demanding GPU-accelerated workloads. This configuration features \u003cstrong\u003e8× NVIDIA HGX H100 80GB SXM GPUs\u003c\/strong\u003e, delivering exceptional performance for large language models (LLMs), generative AI, scientific computing, and high-performance computing environments.\u003c\/p\u003e\n\u003cp\u003eThis listing represents a \u003cstrong\u003edistinct variant\u003c\/strong\u003e of the XE9680 that includes an upgraded \u003cstrong\u003e400GB networking add-on\u003c\/strong\u003e, replacing the standard networking configuration with \u003cstrong\u003e8× 400GB ConnectX-7 adapters\u003c\/strong\u003e. This enhancement is designed for customers building \u003cstrong\u003emulti-node AI clusters\u003c\/strong\u003e, where ultra-low latency and extreme bandwidth are critical for scaling performance across nodes.\u003c\/p\u003e\n\u003cp\u003eWith enterprise-class reliability, redundant power, advanced cooling architecture, and Dell PowerEdge build quality, the XE9680 with 400GB networking is ideal for data centers, research institutions, and cloud providers running mission-critical AI workloads.\u003c\/p\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003cstrong\u003eProduct Specification For Dell PowerEdge XE9680 AI GPU Server –  8 NVIDIA HGX H100 80GB SXM\u003cbr\u003e\u003ca rel=\"noopener\" title=\"Dell PowerEdge XE9680 AI GPU Server – 8 NVIDIA HGX H100 80GB - Data Sheet\" href=\"https:\/\/www.delltechnologies.com\/asset\/en-in\/products\/servers\/technical-support\/poweredge-xe9680-spec-sheet.pdf\" target=\"_blank\"\u003eCLICK HERE FOR DATA SHEET\u003c\/a\u003e\u003cbr\u003e\u003c\/strong\u003e\u003c\/h3\u003e\n\u003ctable style=\"width: 100.036%; height: 441.2px;\"\u003e\n\u003cthead\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003cth style=\"width: 32.0989%; height: 19.6px;\"\u003e\u003cstrong\u003eSpecification\u003c\/strong\u003e\u003c\/th\u003e\n\u003cth style=\"width: 65.4412%; height: 19.6px;\"\u003e\u003cstrong\u003eDetails\u003c\/strong\u003e\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 32.0989%; height: 19.6px;\"\u003eBrand\u003c\/td\u003e\n\u003ctd style=\"width: 65.4412%; height: 19.6px;\"\u003eDell\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 32.0989%; height: 19.6px;\"\u003eProduct Line\u003c\/td\u003e\n\u003ctd style=\"width: 65.4412%; height: 19.6px;\"\u003ePowerEdge\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 32.0989%; height: 19.6px;\"\u003eModel\u003c\/td\u003e\n\u003ctd style=\"width: 65.4412%; height: 19.6px;\"\u003eXE9680\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 32.0989%; height: 19.6px;\"\u003eServer Category\u003c\/td\u003e\n\u003ctd style=\"width: 65.4412%; height: 19.6px;\"\u003eAI \/ GPU Server\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 32.0989%; height: 19.6px;\"\u003eForm Factor\u003c\/td\u003e\n\u003ctd style=\"width: 65.4412%; height: 19.6px;\"\u003eRackmount\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 32.0989%; height: 19.6px;\"\u003eGPU Configuration\u003c\/td\u003e\n\u003ctd style=\"width: 65.4412%; height: 19.6px;\"\u003e8 × NVIDIA HGX H100 SXM\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 32.0989%; height: 19.6px;\"\u003eGPU Memory\u003c\/td\u003e\n\u003ctd style=\"width: 65.4412%; height: 19.6px;\"\u003e80GB HBM3 per GPU\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 32.0989%; height: 19.6px;\"\u003eTotal GPU Memory\u003c\/td\u003e\n\u003ctd style=\"width: 65.4412%; height: 19.6px;\"\u003e640GB\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 32.0989%; height: 19.6px;\"\u003eGPU Architecture\u003c\/td\u003e\n\u003ctd style=\"width: 65.4412%; height: 19.6px;\"\u003eNVIDIA Hopper\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 32.0989%; height: 19.6px;\"\u003eGPU Interconnect\u003c\/td\u003e\n\u003ctd style=\"width: 65.4412%; height: 19.6px;\"\u003eNVLink \u0026amp; NVSwitch\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 32.0989%; height: 19.6px;\"\u003eNetworking (Add-on)\u003c\/td\u003e\n\u003ctd style=\"width: 65.4412%; height: 19.6px;\"\u003e8x 400GB ConnectX-7\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 10px;\"\u003e\n\u003ctd style=\"width: 32.0989%; height: 10px;\"\u003eNetworking Interface\u003c\/td\u003e\n\u003ctd style=\"width: 65.4412%; height: 10px;\"\u003e\u003cspan\u003e2× Broadcom 5720 Dual-Port 1GbE\u003c\/span\u003e\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 32.0989%; height: 19.6px;\"\u003eNetworking Bandwidth\u003c\/td\u003e\n\u003ctd style=\"width: 65.4412%; height: 19.6px;\"\u003e400GB\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 32.0989%; height: 19.6px;\"\u003eCPU Support\u003c\/td\u003e\n\u003ctd style=\"width: 65.4412%; height: 19.6px;\"\u003eDual-socket processors (platform configurable)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 32.0989%; height: 19.6px;\"\u003eSystem Memory\u003c\/td\u003e\n\u003ctd style=\"width: 65.4412%; height: 19.6px;\"\u003eDDR5 memory (capacity configurable)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 32.0989%; height: 19.6px;\"\u003eStorage Support\u003c\/td\u003e\n\u003ctd style=\"width: 65.4412%; height: 19.6px;\"\u003eNVMe \/ SSD storage (configurable)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 32.0989%; height: 19.6px;\"\u003eExpansion Slots\u003c\/td\u003e\n\u003ctd style=\"width: 65.4412%; height: 19.6px;\"\u003ePCIe expansion (platform dependent)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 32.0989%; height: 19.6px;\"\u003ePower Supply\u003c\/td\u003e\n\u003ctd style=\"width: 65.4412%; height: 19.6px;\"\u003eRedundant power supplies\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 32.0989%; height: 19.6px;\"\u003eCooling\u003c\/td\u003e\n\u003ctd style=\"width: 65.4412%; height: 19.6px;\"\u003eHigh-performance enterprise cooling\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 32.0989%; height: 19.6px;\"\u003eManagement\u003c\/td\u003e\n\u003ctd style=\"width: 65.4412%; height: 19.6px;\"\u003eEnterprise out-of-band management\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 32.0989%; height: 19.6px;\"\u003eDeployment\u003c\/td\u003e\n\u003ctd style=\"width: 65.4412%; height: 19.6px;\"\u003eData center, cloud, AI cluster environments\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 32.0989%; height: 19.6px;\"\u003ePrimary Workloads\u003c\/td\u003e\n\u003ctd style=\"width: 65.4412%; height: 19.6px;\"\u003eAI, ML, LLM training, HPC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003chr\u003e\n\u003ch3\u003e\u003cstrong\u003eAI, ML \u0026amp; HPC Use Cases\u003c\/strong\u003e\u003c\/h3\u003e\n\u003cp\u003e\u003cstrong\u003eIdeal For:\u003c\/strong\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eLarge Language Model (LLM) Training\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eGenerative AI \u0026amp; Multimodal AI\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eDistributed AI \u0026amp; GPU Clusters\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eDeep Learning \u0026amp; Machine Learning\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eHigh-Performance Computing (HPC)\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eAI Inference at Scale\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eData Analytics \u0026amp; Big Data Processing\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eResearch \u0026amp; Academic Computing\u003c\/p\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003eEnterprise AI \u0026amp; Cloud Service Providers\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003chr\u003e\n\u003ch3\u003e\n\u003cstrong\u003eFAQs \u003c\/strong\u003e\u003cstrong\u003e(Frequently Asked Questions)\u003c\/strong\u003e\n\u003c\/h3\u003e\n\u003cp\u003e\u003cstrong\u003e1: What workloads is this server designed for?\u003c\/strong\u003e\u003cbr\u003eThis server is designed for AI training, machine learning, large language models (LLMs), high-performance computing (HPC), and GPU cluster deployments.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003e2: What does 400GB networking mean in this configuration?\u003c\/strong\u003e\u003cbr\u003e400GB networking refers to high-bandwidth network connectivity that enables fast, low-latency data transfer for distributed and multi-node workloads.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003e3: Is this server suitable for multi-node AI clusters?\u003c\/strong\u003e\u003cbr\u003eYes. The high-bandwidth networking is well suited for large-scale, distributed AI and HPC environments.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003e4: Can CPU, memory, and storage configurations be customized?\u003c\/strong\u003e\u003cbr\u003eYes. CPU, system memory, and storage options are configurable based on deployment requirements and availability.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003e5: Does the networking support InfiniBand?\u003c\/strong\u003e\u003cbr\u003eYes. The networking hardware is InfiniBand-ready, depending on fabric design and transceiver configuration.\u003cstrong\u003e\u003c\/strong\u003e\u003c\/p\u003e","brand":"Dell","offers":[{"title":"Default Title","offer_id":48357892489445,"sku":"Dell-XE9680-400GB","price":269000.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0252\/2280\/7632\/files\/DellXE9680_Front.png?v=1770047401"},{"product_id":"dell-nvidia-rtx-pro-6000-blackwell-96gb-gddr7-server-edition-gpu-usa-only","title":"DELL NVIDIA RTX PRO 6000 Blackwell 96GB GDDR7 Server Edition GPU - USA ONLY","description":"\u003ch1\u003e\u003cstrong\u003eDell NVIDIA RTX PRO 6000 Blackwell Server Edition 96GB GDDR7 AI GPU\u003c\/strong\u003e\u003c\/h1\u003e\n\u003ch2\u003e\u003cstrong\u003eEnterprise AI Performance Powered by the NVIDIA Blackwell Architecture\u003c\/strong\u003e\u003c\/h2\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan style=\"color: rgb(255, 42, 0);\"\u003eNote: This product is export restricted and may not be relocated or transferred without compliance with all applicable regulations.\u003c\/span\u003e\u003c\/strong\u003e\u003c\/p\u003e\n\u003cp class=\"PDq2pG_selectionAnchorContainer\"\u003eThe \u003cstrong\u003eDell NVIDIA RTX PRO 6000 Blackwell Server Edition 96GB GDDR7\u003c\/strong\u003e is an enterprise-class PCIe GPU built to accelerate today's most demanding AI, machine learning, high-performance computing (HPC), virtualization, and professional visualization workloads. Powered by the latest \u003cstrong\u003eNVIDIA Blackwell architecture\u003c\/strong\u003e, this server GPU delivers exceptional performance, scalability, and reliability for modern data centers.\u003cspan class=\"PDq2pG_selectionAnchor\"\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003eFeaturing \u003cstrong\u003e96GB of high-speed GDDR7 ECC memory\u003c\/strong\u003e, the RTX PRO 6000 Server Edition is engineered to process massive datasets, train large language models (LLMs), accelerate AI inference, and support complex scientific simulations. Its passive cooling design makes it ideal for deployment in Dell PowerEdge servers and enterprise rack environments with optimized chassis airflow.\u003c\/p\u003e\n\u003cp\u003eDesigned for organizations building AI infrastructure, cloud platforms, engineering applications, and virtual desktop environments, the Dell RTX PRO 6000 combines enterprise-grade hardware with NVIDIA Enterprise software support for dependable, long-term performance.\u003c\/p\u003e\n\u003cp\u003eWhether you're expanding AI capabilities, accelerating GPU-intensive workloads, or modernizing your data center, the Dell NVIDIA RTX PRO 6000 Blackwell Server Edition provides the compute power and memory capacity required for next-generation enterprise computing.\u003c\/p\u003e\n\u003ch3\u003e\u003cstrong\u003eProduct Specifications – Dell NVIDIA RTX PRO 6000 Blackwell Server Edition 96GB GDDR7\u003cbr\u003eCLICK HERE FOR DATA SHEET - \u003ca href=\"https:\/\/resources.nvidia.com\/en-us-rtx-pro-6000\" title=\"NVIDIA RTX PRO 6000 Blackwell Server Edition - Data Sheet\" rel=\"noopener\" target=\"_blank\"\u003eNVIDIA \u003c\/a\u003e| \u003ca href=\"https:\/\/www.delltechnologies.com\/asset\/en-us\/products\/storage\/technical-support\/dell-ai-factory-with-nvidia-rtx-pro-6000-on-dell-poweredge-servers-datasheet.pdf\" title=\"DELL NVIDIA RTX PRO 6000 Blackwell Server Edition - Data Sheet\" rel=\"noopener\" target=\"_blank\"\u003eDELL\u003c\/a\u003e\u003cbr\u003e\u003c\/strong\u003e\u003c\/h3\u003e\n\u003ctable style=\"width: 100.036%; height: 764.4px;\"\u003e\n\u003cthead\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003cth style=\"width: 25.3544%; height: 19.6px;\"\u003eSpecification\u003c\/th\u003e\n\u003cth style=\"width: 72.1857%; height: 19.6px;\"\u003eDetails\u003c\/th\u003e\n\u003c\/tr\u003e\n\u003c\/thead\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 25.3544%; height: 19.6px;\"\u003e\u003cstrong\u003eProduct Name\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 72.1857%; height: 19.6px;\"\u003eDell NVIDIA RTX PRO 6000 Blackwell Server Edition 96GB GDDR7\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 25.3544%; height: 19.6px;\"\u003e\u003cstrong\u003eManufacturer\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 72.1857%; height: 19.6px;\"\u003eDell Technologies (Powered by NVIDIA)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 25.3544%; height: 19.6px;\"\u003e\u003cstrong\u003eGPU Manufacturer\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 72.1857%; height: 19.6px;\"\u003eNVIDIA\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 25.3544%; height: 19.6px;\"\u003e\u003cstrong\u003eGPU Architecture\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 72.1857%; height: 19.6px;\"\u003eNVIDIA Blackwell\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 25.3544%; height: 19.6px;\"\u003e\u003cstrong\u003eProduct Series\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 72.1857%; height: 19.6px;\"\u003eNVIDIA RTX PRO\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 25.3544%; height: 19.6px;\"\u003e\u003cstrong\u003eModel\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 72.1857%; height: 19.6px;\"\u003ePG153A\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 39.2px;\"\u003e\n\u003ctd style=\"width: 25.3544%; height: 39.2px;\"\u003e\u003cstrong\u003eDell Part Number (P\/N)\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 72.1857%; height: 39.2px;\"\u003eTHH68\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 39.2px;\"\u003e\n\u003ctd style=\"width: 25.3544%; height: 39.2px;\"\u003e\u003cstrong\u003eManufacturer Part Number (MPN)\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 72.1857%; height: 39.2px;\"\u003e699-2G153-0210-301\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 25.3544%; height: 19.6px;\"\u003e\u003cstrong\u003eForm Factor\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 72.1857%; height: 19.6px;\"\u003ePassive, Server Edition PCIe GPU\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 25.3544%; height: 19.6px;\"\u003e\u003cstrong\u003eMemory Capacity\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 72.1857%; height: 19.6px;\"\u003e96 GB\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 25.3544%; height: 19.6px;\"\u003e\u003cstrong\u003eMemory Type\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 72.1857%; height: 19.6px;\"\u003eGDDR7 ECC\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 25.3544%; height: 19.6px;\"\u003e\u003cstrong\u003eInterface\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 72.1857%; height: 19.6px;\"\u003ePCI Express x16 (Gen 5)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 39.2px;\"\u003e\n\u003ctd style=\"width: 25.3544%; height: 39.2px;\"\u003e\u003cstrong\u003eCooling\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 72.1857%; height: 39.2px;\"\u003ePassive cooling (Designed for enterprise servers with chassis airflow)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 25.3544%; height: 19.6px;\"\u003e\u003cstrong\u003eDisplay Outputs\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 72.1857%; height: 19.6px;\"\u003eNone (Server Edition)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 25.3544%; height: 19.6px;\"\u003e\u003cstrong\u003eECC Memory\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 72.1857%; height: 19.6px;\"\u003eSupported\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 58.8px;\"\u003e\n\u003ctd style=\"width: 25.3544%; height: 58.8px;\"\u003e\u003cstrong\u003eTarget Workloads\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 72.1857%; height: 58.8px;\"\u003eArtificial Intelligence (AI), Machine Learning (ML), Large Language Models (LLMs), HPC, Scientific Computing, Data Analytics, Virtualization, Rendering\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 39.2px;\"\u003e\n\u003ctd style=\"width: 25.3544%; height: 39.2px;\"\u003e\u003cstrong\u003eVirtualization Support\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 72.1857%; height: 39.2px;\"\u003eNVIDIA vGPU (License Required)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 25.3544%; height: 19.6px;\"\u003e\u003cstrong\u003eCUDA Support\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 72.1857%; height: 19.6px;\"\u003eYes\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 25.3544%; height: 19.6px;\"\u003e\u003cstrong\u003eTensor Cores\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 72.1857%; height: 19.6px;\"\u003e5th Generation Tensor Cores\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 25.3544%; height: 19.6px;\"\u003e\u003cstrong\u003eRT Cores\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 72.1857%; height: 19.6px;\"\u003e4th Generation RT Cores\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 39.2px;\"\u003e\n\u003ctd style=\"width: 25.3544%; height: 39.2px;\"\u003e\u003cstrong\u003eMulti-Instance GPU (MIG)\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 72.1857%; height: 39.2px;\"\u003eSupported\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 25.3544%; height: 19.6px;\"\u003e\u003cstrong\u003eNVLink\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 72.1857%; height: 19.6px;\"\u003eNot Supported (Server Edition uses PCIe architecture)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 39.2px;\"\u003e\n\u003ctd style=\"width: 25.3544%; height: 39.2px;\"\u003e\u003cstrong\u003eEnterprise Driver Support\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 72.1857%; height: 39.2px;\"\u003eNVIDIA Enterprise Drivers\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 39.2px;\"\u003e\n\u003ctd style=\"width: 25.3544%; height: 39.2px;\"\u003e\u003cstrong\u003eServer Compatibility\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 72.1857%; height: 39.2px;\"\u003eDell PowerEdge Servers (Model compatibility depends on server configuration)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 39.2px;\"\u003e\n\u003ctd style=\"width: 25.3544%; height: 39.2px;\"\u003e\u003cstrong\u003eOperating Systems\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 72.1857%; height: 39.2px;\"\u003eWindows Server, Linux Enterprise Distributions (Driver Dependent)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 39.2px;\"\u003e\n\u003ctd style=\"width: 25.3544%; height: 39.2px;\"\u003e\u003cstrong\u003eTypical Deployment\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 72.1857%; height: 39.2px;\"\u003eAI Infrastructure, Data Centers, Enterprise GPU Servers, Research Labs, Cloud Infrastructure\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr style=\"height: 19.6px;\"\u003e\n\u003ctd style=\"width: 25.3544%; height: 19.6px;\"\u003e\u003cstrong\u003eWarranty\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"width: 72.1857%; height: 19.6px;\"\u003eDell Limited Hardware Warranty (Region Dependent)\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\n\u003c\/table\u003e\n\u003cp\u003e\u003cspan style=\"color: rgb(255, 42, 0);\"\u003e\u003cstrong\u003e\u003cbr\u003eNote : USA Shipping and Usage Only – Not for Export\u003c\/strong\u003e\u003c\/span\u003e\u003cbr\u003e\u003cspan style=\"color: rgb(255, 42, 0);\"\u003e\u003cstrong\u003eGPU\/Graphics Card Export Compliance Notice: By purchasing this GPU\/graphics card, you acknowledge and agree that it will not be exported, re-exported, or transferred -directly or indirectly -to any country or end user restricted under U.S. export laws, including, but not limited to, China, Russia, Iran, North Korea, or other embargoed destinations.\u003c\/strong\u003e\u003c\/span\u003e\u003cbr\u003e\u003cspan style=\"color: rgb(255, 42, 0);\"\u003e\u003cstrong\u003eThis hardware may not be used for military, nuclear, missile, chemical or biological weapons, or prohibited supercomputing applications. Buyers are responsible for complying with all applicable U.S. Export Administration Regulations (EAR) and sanctions programs.\u003c\/strong\u003e\u003c\/span\u003e\u003cbr\u003e\u003cspan style=\"color: rgb(255, 42, 0);\"\u003e\u003cstrong\u003eEnd-user compliance paperwork will be required before shipment.\u003c\/strong\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003chr\u003e\n\u003ch3 class=\"PDq2pG_selectionAnchorContainer\"\u003e\n\u003cstrong\u003eFAQs\u003c\/strong\u003e\u003cspan class=\"PDq2pG_selectionAnchor\"\u003e\u003c\/span\u003e\n\u003c\/h3\u003e\n\u003cp\u003e\u003cstrong\u003e1. What is the Dell NVIDIA RTX PRO 6000 Blackwell Server Edition used for?\u003cbr\u003e\u003c\/strong\u003eIt is designed for enterprise AI, machine learning, high-performance computing (HPC), virtualization, data analytics, and professional visualization workloads.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. How much memory does the RTX PRO 6000 Server Edition include?\u003cbr\u003e\u003c\/strong\u003e\u003cspan style=\"font-size: 0.875rem;\"\u003eThe GPU features 96GB of GDDR7 ECC memory, enabling large AI models, complex simulations, and memory-intensive enterprise applications.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Is this GPU compatible with Dell PowerEdge servers?\u003cbr\u003e\u003c\/strong\u003e\u003cspan style=\"font-size: 0.875rem;\"\u003eYes. The Dell NVIDIA RTX PRO 6000 Server Edition is designed for compatible Dell PowerEdge servers. Compatibility depends on the specific server model and configuration.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Does this GPU support virtualization?\u003cbr\u003e\u003c\/strong\u003eYes. The GPU supports NVIDIA vGPU technology (license required), making it suitable for virtual desktops, virtual workstations, and shared GPU environments.\u003c\/p\u003e\n\u003cp\u003e\u003cstrong\u003e5. Does the Server Edition include display outputs?\u003cbr\u003e\u003c\/strong\u003eNo. The Server Edition uses a passive cooling design and does not include display outputs. It is intended exclusively for enterprise server deployments.\u003c\/p\u003e","brand":"Dell","offers":[{"title":"Default Title","offer_id":49926220054757,"sku":"THH68","price":15795.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0252\/2280\/7632\/files\/Dell_NVIDIA_RTX_PRO_6000_Blackwell_Server_Edition.jpg?v=1782996028"}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0252\/2280\/7632\/collections\/NVIDIA.jpg?v=1746633146","url":"https:\/\/networkoutlet.com\/collections\/nvidia\/a100.oembed","provider":"Network Outlet","version":"1.0","type":"link"}