Deep Learning Appliance

DEEP Gadget provides the best deep learning platform powered by seven water-cooled GPUs, ready-to-use software stack, and huge virtual GPU memory for DNNs.

Incredible Computing Power from 7 GPUs

7 GPUs in a single DEEP Gadget machine provides up to 70 TFLOPS single-precision peak performance. A closed-circuit water cooling system keeps high-density GPUs at a low temperature. DEEP Gadget works quietly even at full load and can be placed in your office.

Cost-Effective Gaming GPUs with Enterprise-Level Reliability

Water cooling ensures the long lifetime and reliability of gaming GPUs as with expensive high-end GPUs. Users can choose the appropriate GPU option based on their demands, applications, and budgets.

Ready-To-Use Deep Learning Software Stacks and Virtual GPU Memory for DNNs

DEEP Gadget is delivered with pre-installed software stacks including an operating system and deep learning frameworks, such as Caffe and TensorFlow. In addition, the VMDNN library develped by ManyCoreSoft is bundled with DEEP Gadget. Users do not need to worry about the small size of GPU memory because VMDNN virtually expands the memory space on demand during the training period of deep neural networks.

Water Cooling Solution

DEEP Gadget is created by ManyCoreSoft, who developed the first water-cooled GPU supercomputer in the world.

Supercomputer Chundoong Emerging Technologies in SC15
Supercomputer Chundoong of Seoul National University

ManyCoreSoft designed and built Chundoong, the first water-cooled GPU supercomputer in the TOP500 list, in October 2012.

Emerging Technology in SC15

The liquid cooling solution of ManyCoreSoft is accepted as one of ten Emerging Technologies in SC15.

Items Standalone Rackmount
Single-Socket Models
Dual-Socket Models
Dual-Socket Large Memory Models
CPU 1x Intel Xeon E5-2600 v4 series CPU 2x Intel Xeon E5-2600 v4 series CPUs Will be released soon
Maximum # of GPUs 7 7 6

One of the following:

  • NVIDIA GeForce GTX 1080 Ti
  • NVIDIA Tesla V100 for PCIe
  • NVIDIA Tesla P100 PCIe 16 GB or 12 GB
  • NVIDIA Tesla P40
  • AMD Radeon RX Vega 64
  • AMD Radeon Instinct MI25
PCIe 1x PCIe 3.0 x16, 6x PCIe 3.0 x8 2x PCIe 3.0 x16, 5x PCIe 3.0 x8 2x PCIe 3.0 x16, 4x PCIe 3.0 x8
Main Memory DD4 2400 MHz ECC Registered
64 GB, 128 GB, or 256 GB 64 GB, 128 GB, 256 GB, or 512 GB 1 TB
Primary Storage 250 GB, 500 GB, 1 TB, or 2 TB M.2 NVMe SSD
Secondary Storage

One of the following:

  • 4 TB SATA3 HDD
  • 8 TB SATA3 HDD
  • 12 TB SATA3 HDD
  • 16 TB RAID 6 HDD storage (6x 4 TB SATA3 HDD, Software RAID 6 4+2)
  • 32 TB RAID 6 HDD storage (6x 8 TB SATA3 HDD, Software RAID 6 4+2)
  • 48 TB RAID 6 HDD storage (6x 12 TB SATA3 HDD, Software RAID 6 4+2)
Motherboard ASUS X99-E WS ASUS Z10PE-D8 WS ASUS Z10PE-D16 WS
Power Supply 2x 1300 W (80 PLUS Platinum)
Operating System Ubuntu 16.04 LTS
  • For NVIDIA GPUs: CUDA Toolkit, cuBLAS, cuDNN, NCCL, NCCL 2, VMDNN Library
  • For AMD GPUs: ROCm, MIOpen
Deep Learning Frameworks
  • For NVIDIA GPUs: Caffe, TensorFlow, Caffe2, Torch, PyTorch, Theano, MXNet, Cognitive Toolkit
  • For AMD GPUs: hipCaffe, hipTensorFlow

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