The TensorEX TS4-1686564-DPN is a Deep Learning & AI server supporting 2x Intel Xeon Scalable Family processors, a maximum of 768 GB DDR4 memory, and up to 4x NVIDIA 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 Deep Learning DevBox, featuring NVIDIA GPU technology coupled with state-of-the-art PCIe peer to peer 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 2x Intel Xeon Scalable Family processors (Skylake-SP)
• 4x NVIDIA Tesla or Quadro GPUs
• Optional - 2x pairs NVLink Bridge for GV100, NVLink Bridge for GP100, NVLink Bridge for RTX 8000/6000, or NVLink Bridge for RTX 5000
• 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
	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 EMLI Separated Frameworks	Container EMLI Flexible. Reconfigurable.	DIY EMLI Simple. 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

See What’s Inside