- Rack Height: 4U
- Processor Supported: 2x AMD EPYC 7001/7002 Series
- Drive Bays: 24x 2.5" Hot-Swap
- Supports up to 8x PCI 4.0 x16 Double-Wide cards
The TensorEX TS4-173535991-DPN is a 2U rack mountable Deep Learning & AI server supporting 2x AMD EPYC 7002-Series processors, a maximum of 8 TB DDR4 memory, and up to 8x Double-Wide GPUs.
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 TensorEX TS4-173535991-DPN, featuring NVIDIA GPU 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 2x AMD EPYC 7002-Series processors (Socket SP3)
- Up to 8x Double-Wide NVIDIA GPUs
- NVIDIA DIGITS software providing powerful design, training, and visualization of deep neural networks for image classification
- Pre-installed standard Ubuntu 18.04 w/ Deep Learning software stack
- Google TensorFlow software library
- Automatic software update tool included
NVIDIA Tesla GPU Options
Model | Standard Memory | Memory Bandwidth (GB/s) | CUDA Cores | Tensor Cores | Single Precision (TFLOPS) | Double Precision (TFLOPS) | Power (W) | Explore | |
---|---|---|---|---|---|---|---|---|---|
![]() | A100 40 GB PCI-E 4.0 | 40 GB HBM2e | 1555 | 6912 | 432 | 19.5 | 9.7 | 400 | Specs |
![]() | V100S 32 GB PCI-E 3.0 | 32 GB HBM2 | 1134 | 5120 | 640 | 16.4 | 8.2 | 250 | Specs |
![]() | V100 32 GB PCI-E 3.0 | 32 GB HBM2 | 900 | 5120 | 640 | 14 | 7 | 250 | Specs |
![]() | V100 16 GB PCI-E 3.0 | 16 GB HBM2 | 900 | 5120 | 640 | 14 | 7 | 250 | Specs |
![]() | P100 16 GB PCI-E 3.0 | 16 GB HBM2 | 732 | 3584 | - | 9.3 | 4.7 | 250 | Specs |
![]() | P100 12 GB PCI-E 3.0 | 12 GB HBM2 | 549 | 3584 | - | 9.3 | 4.7 | 250 | Specs |
![]() | T4 PCI-E 3.0 | 16 GB GDDR6 | 320 | 2560 | 320 | 8.1 | 0.24 | 75 | Specs |
![]() | P4 PCI-E 3.0 | 8 GB GDDR5 | 192 | 2560 | - | 5.5 | 0.17 | 75 | Specs |
NVIDIA Quadro GPU Options
Model | Standard Memory | Memory Bandwidth (GB/s) | CUDA Cores | Tensor Cores | Single Precision (TFLOPS) | Double Precision (TFLOPS) | Power (W) | Explore | |
---|---|---|---|---|---|---|---|---|---|
![]() | RTX A6000 | 48 GB GDDR6 | 768 | 10752 | 336 | TBD | TBD | 300 | Specs |
![]() | A40 | 48 GB GDDR6 | 696 | 10752 | 336 | TBD | TBD | 300 | Specs |
![]() | Quadro GV100 | 32 GB HBM2 | 870 | 5120 | 640 | 14.8 | 7.9 | 250 | Specs |
![]() | Quadro GP100 | 16 GB HBM2 | 717 | 3584 | - | 10.3 | 5.2 | 235 | Specs |
![]() | Quadro RTX 8000 | 48 GB GDDR6 | 672 | 4608 | 576 | 16.3 | 0.51 | 295 | Specs |
![]() | Quadro RTX 8000 (Passive) | 48 GB GDDR6 | 624 | 4608 | 576 | 14.9 | 0.47 | 250 | Specs |
![]() | Quadro RTX 6000 | 24 GB GDDR6 | 624 | 4608 | 576 | 16.3 | 0.51 | 295 | Specs |
![]() | Quadro RTX 6000 (Passive) | 24 GB GDDR6 | 624 | 4608 | 576 | 14.9 | 0.47 | 250 | Specs |
![]() | Quadro RTX 5000 | 16 GB GDDR6 | 870 | 3072 | 384 | 11.2 | 0.35 | 200 | Specs |
![]() | Quadro RTX 4000 | 8 GB GDDR6 | 416 | 2304 | 288 | 7.1 | 0.22 | 160 | Specs |
![]() | Quadro P6000 | 24 GB GDDR5X | 432 | 3840 | - | 12 | 0.39 | 250 | Specs |
![]() | Quadro P5000 | 16 GB GDDR5 | 288 | 2560 | - | 8.9 | 0.28 | 180 | Specs |
* Passive GPUs only for select systems that support passive cooling.
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. |
---|---|---|---|
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 |
- 7001-Series
- 7002-Series
- DDR4 SDRAM
- Via first CPU
- 1000BASE-T
- 8x PCI-E 4.0 x16 slots (Supports double-wide cards)
- 3x PCI-E 4.0 x8 slots
- 24x 2.5" Hot-swap drive bays
- 2x RJ45 1000BASE-T Ethernet LAN Ports