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gptkbp:instance_of
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gptkb:microprocessor
|
|
gptkbp:ai
|
gptkb:Tensor_Flow
gptkb:Caffe
gptkb:Chainer
gptkb:MXNet
gptkb:Py_Torch
|
|
gptkbp:compatibility
|
gptkb:NVIDIA_CUDA
gptkb:NVIDIA_Tensor_RT
gptkb:NVIDIA_cu_DNN
gptkb:NVIDIA_RAPIDS
|
|
gptkbp:cooling_system
|
liquid cooling
|
|
gptkbp:debut
|
gptkb:2017
|
|
gptkbp:designed_for
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gptkb:AI_technology
|
|
gptkbp:dimensions
|
24 x 12 x 24 inches
|
|
gptkbp:environmental_concerns
|
gptkb:Ro_HS
gptkb:Energy_Star
WEEE
|
|
gptkbp:equipment
|
gptkb:NVIDIA_Tesla_V100_GPUs
|
|
gptkbp:expansion_slots
|
4 PCIe slots
|
|
gptkbp:form_factor
|
gptkb:computer
desktop
rackmount
|
|
gptkbp:gpuarchitecture
|
Volta
|
|
gptkbp:gpucount
|
gptkb:4
|
|
gptkbp:has_programs
|
NVIDIA GPU Cloud (NGC)
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
NVIDIA's DGX Station
|
|
gptkbp:manufacturer
|
gptkb:NVIDIA
|
|
gptkbp:network
|
10 Gb E
|
|
gptkbp:operating_system
|
gptkb:Ubuntu_Linux
|
|
gptkbp:power_consumption
|
3 k W
|
|
gptkbp:power_supply
|
dual redundant power supplies
|
|
gptkbp:price
|
approximately $69,000
|
|
gptkbp:processor
|
gptkb:Intel_Xeon
|
|
gptkbp:ram
|
512 GB
|
|
gptkbp:release_year
|
gptkb:2017
|
|
gptkbp:security_features
|
gptkb:TPM_2.0
|
|
gptkbp:storage
|
8 TB SSD
|
|
gptkbp:successor
|
gptkb:DGX_Station_A100
|
|
gptkbp:support
|
gptkb:NVIDIA_DGX_OS
gptkb:NVIDIA_AI_Enterprise
gptkb:NVIDIA_Deep_Learning_SDK
NVIDIA Enterprise Support
|
|
gptkbp:target_audience
|
data scientists
AI researchers
machine learning engineers
|
|
gptkbp:target_market
|
research institutions
|
|
gptkbp:use_case
|
deep learning
|
|
gptkbp:user_interface
|
remote management
|
|
gptkbp:weight
|
approximately 60 kg
|
|
gptkbp:bfsParent
|
gptkb:NVLink
|
|
gptkbp:bfsLayer
|
5
|