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  1. Intel I210-T1 Ethernet Server Adapter

  2. HPE DL380 G10 8SFF GPU 2x Xeon Gold 6134 256GB RAM Tesla M10 2x 960GB SSD

    HPE DL380 G10 8SFF GPU 2x Xeon Gold 6134 256 GB RAM 1x Nvidia Tesla M10 32GB GDDR5 2x 960GB 6G SATA SSD Rails. Installed HDD 2 x 960GB 6G SATA SSD. Manufacturer Part Number DL380 G10 8SFF GPU. Manufacturer HPE.

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  3. Dell PowerEdge R730 2x 12-Core E5-2670v3 2.3GHz 256GB GRID K2 GPU SSD 2U Server

    Dell PowerEdge R730 16x 2.5" Bay 2U Server. 256GB DDR4 Ram (16x 16GB). 1 x Dell NVidia GRID K2 8GB GPU Accelerator Card. 2 x 200GB 2.5" SAS 12G Solid State Drives SSD - K41XJ. x16 way Backplane for up to 16 x 2.5" SAS, SATA or SSD Hot-Swap Drives.

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  4. QUAD GPU Server Nvidia Accelerated HPC *12288 Cores* Bioinformatics Arioc

    Hsquare Cluster Node- 2U Rack Server with 4 x Tesla K10 -GPU. 2 x CPU Sockets, BOTH CPU installed ( 12 CPU Cores/ Logical Processors/ 24threads). Server Options / upgrades . Storage: 2 X 100GB 2.5" SFF SSD.

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  5. Supermicro SYS-1028GR-TR GPU SuperServer 1U CTO Rack Server

    This server is designed for parallel processing, the GPU is used in a wide range of applications, including graphics and video rendering. 1x SuperMicro SYS-1028GR-TR SuperServer CTO Chassis. 1x Supermicro 1U Rail Kit.

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  6. Dell PowerEdge C4140 Server + 768G RAM 2x Gold 6148 40Core Support 4X PCIE GPU

    2x Gold 6148 =40Core. 24X 32G 2400MHz=768G. R emote console. Rack rails. Cooling fan Hard drive bays Network card 2x2.5 ” SATA.

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  7. NVIDIA RTX A6000 48GB GPU Server 10,752 CUDA 336 Tensor 84 RT Cores AI Graphics

    Graphic: 1x NVIDIA RTX A6000 48GB GPU. HPE ProLiant DL380 Gen10 Rack Server. Dual Socket, Both CPU's installed (40 Cores / 80 Logical Processors /Threads). Tested with Windows Server 2019 /2022 / Ubuntu /CentOS /Linux.

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  8. Supermicro SYS-1029GQ-TRT 2x Xeon Silver 4114 128 GB RAM Rails

    To accelerate machine learning, graphics, and data science on Supermicro SYS-based systems, these servers use Tensor Core and CUDA GPUs, the Tesla P100 and Tesla V100. The Supermicro SYS-1029GQ-TRT is optimal for solving Linear Algebra and machine learning (inference and forecasting) problems for recurrent neural networks.

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  9. DVS i7-9700F 16GB DDR4 P620 GPU 250GB SSD 2TB NVMe VisionLC-HD2 2U Media Server

    16GB ( 2 x 8GB Kit) DDR4 Ram CMK16GX4M2A2666C16. 1 x Nvidia P620 Quadro 2GB GDDR5 4-Port Mini-DP GPU. 1 x 250GB 2.5" Samsung Evo 860 SATA SSD. Max power ≈ 5W. 1 x Dual HDMI Input Capture Card VisionLC-HD2.

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