- Rack Height: 10U
- Processor Supported: 2x Intel Xeon Scalable family
- Drive Bays: 16x 2.5" Hot-Swap NVMe
- Supports 16x NVIDIA Tesla V100 32 GB SXM3 GPUs
The TensorEX TS4-144580094-DPN is a 10U rack mountable Deep Learning & AI server supporting 2x Intel Xeon Scalable family processors, a maximum of 3 TB DDR4 memory, and eight Tesla V100 32 GB Volta GPUs (SXM3), with NVSwitch powered by NVLink 2.4 TB/s aggregate speed.
GPUs have provided groundbreaking performance to accelerate deep learning research with thousands of computational cores and up to 100x application throughput when compared to CPUs alone. Exxact has developed the Deep Learning DevBox, featuring NVIDIA GPU technology coupled with state-of-the-art NVSwitch powered by NVLink GPU-GPU interconnect technology, and a full pre-installed suite of the leading deep learning software, for developers to get a jump-start on deep learning research with the best tools that money can buy.
Features:
- Supports two Intel Xeon processor scalable family
- 16x Tesla V100 SXM3 32 GB (15.7 TFlops of single precision, 900 GB/s of memory bandwidth, and 32 GB of memory per board) GPUs
- NVIDIA DIGITS software providing powerful design, training, and visualization of deep neural networks for image classification
- Pre-installed standard Ubuntu 16 with Deep Learning software stack
- Google TensorFlow software library
- Automatic software update tool included
- A turn-key server with up to 2.4TB/s aggregate speed of NVSwitch powered NVLink GPU-GPU interconnect
EMLI (Exxact Machine Learning Images)
Most Popular | |||
Compare*Additional NGC (NVIDIA GPU Cloud) containers can be added upon request. | Conda EMLISeparated Frameworks | Container EMLIFlexible. Reconfigurable. | DIY EMLISimple. Clean. Custom. |
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Who is it for? | For developers who want pre-installed deep learning frameworks and their dependencies in separate Python environments installed natively on the system. | For developers who want pre-installed frameworks utilizing the latest NGC containers, GPU drivers, and libraries in ready to deploy DL environments with the flexibility of containerization. | For experienced developers who want a minimalist install to set up their own private deep learning repositories or custom builds of deep learning frameworks. |
Frameworks* | |||
TensorFlow V1 | — | — | |
TensorFlow V2 | — | ||
PyTorch | — | ||
MXnet | — | ||
Caffe | — | — | |
Caffe2 | — | ||
Chainer | — | — | |
Microsoft Cognitive Toolkit | — | — | |
Libraries* | |||
NVIDIA cuDNN | |||
NVIDIA Rapids | — | ||
Keras | — | ||
Theano | — | ||
OpenCV | — | ||
Software Environments | |||
NVIDIA CUDA Toolkit | |||
NVIDIA CUDA Dev Toolkit | — | ||
NVIDIA Digits | — | ||
Anaconda | — | ||
Container Management | |||
Docker | — | ||
Drivers | |||
NVIDIA-qualified Driver | |||
Orchestration | |||
Micro-K8s | Free upgrade available | Free upgrade available | Free upgrade available |
- Bronze 31XX
- Bronze 32XX
- Silver 41XX
- Silver 42XX
- Gold 51XX
- Gold 52XX
- Gold 61XX
- Gold 62XX
- Platinum 81XX
- Platinum 82XX
- DDR4 SDRAM
- DDR4 NVDIMM (Intel Optane DCPMM)
- Via Intel C621 chipset
- 10GBASE-T
- 16x PCI-E 3.0 x16 for RDMA via IB EDR
- 2x PCI-E 3.0 x16 on motherboard
- 2x PCI-E 3.0 x4 M.2 (2280, 22110)
- 16x 2.5" NVMe Hot-swap
- 6x 2.5" SATA3 Hot-swap
- 2x RJ45 10GBASE-T Ethernet LAN Ports
- 1x RJ45 Dedicated IPMI Port