The TensorEX TS2-171138844-DPN is a 2U rack mountable Deep Learning NVIDIA GPU server supporting 2x AMD EPYC 7002-Series processors, a maximum of 8 TB DDR4 memory, and four A100 Ampere GPUs (SXM4), with up to 600GB/s NVLINK interconnect.

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 TS2-171138844-DPN, featuring NVIDIA GPU technology coupled with state-of-the-art NVLINK 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 AMD EPYC 7002-Series processors (Socket SP3)
• 4x A100 40 GB SXM4 GPUs (19.5 TFlops of single precision, 1555 GB/s of memory bandwidth, and 40 GB of memory per board)
• 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
• A turn-key server with up to 600GB/s NVLINK interconnect

NVIDIA SXM4 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 SXM4	40 GB HBM2e	1555	6912	432	19.5	9.7	400	--

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