Exxact Valence Deep Learning & AI Workstation - 1x Intel Xeon W processor - VWS-1735800-DPW
The Valence VWS-1735800-DPW is a Deep Learning & AI Workstation supporting 1x Intel Xeon W processor, a maximum of 1 TB DDR4 memory, and up to 4x Double-Wide cards.
The World's Fastest Deep Learning Workstation Right at Your Desk
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 Workstation, 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:
- NVIDIA DIGITS software providing powerful design, training, and visualization of deep neural networks for image classification
- Pre-installed standard Ubuntu 18.04/20.04 w/ Exxact Machine Learning Image (EMLI)
- Google TensorFlow software library
- Automatic software update tool included
- A turn-key workstation with superior PCIe peer to peer topology
An EMLI Environment for Every Developer
Conda EMLI
For developers who want pre-installed deep learning frameworks and their dependencies in separate Python environments installed natively on the system.
Container EMLI
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.
DIY EMLI
For experienced developers who want a minimalist install to set up their own private deep learning repositories or custom builds of deep learning frameworks.
Parallelize your Workload
Not Just for Gaming
GPUs aren’t just for gaming but for accelerating scientific and engineering challenges. Execute workloads that require immense parallelization; simultaneously train deep learning iterations, split render tasks of large 3D scenes, or simulate molecular dynamics calculations in batches for higher throughput and lower time-to-completion. Our graphics processing units are turning into general processing units enabling our every innovation.
About
The Valence VWS-1735800-DPW is a Deep Learning & AI Workstation supporting 1x Intel Xeon W processor, a maximum of 1 TB DDR4 memory, and up to 4x Double-Wide cards.
The World's Fastest Deep Learning Workstation Right at Your Desk
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 Workstation, 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:
- NVIDIA DIGITS software providing powerful design, training, and visualization of deep neural networks for image classification
- Pre-installed standard Ubuntu 18.04/20.04 w/ Exxact Machine Learning Image (EMLI)
- Google TensorFlow software library
- Automatic software update tool included
- A turn-key workstation with superior PCIe peer to peer topology
An EMLI Environment for Every Developer
Conda EMLI
For developers who want pre-installed deep learning frameworks and their dependencies in separate Python environments installed natively on the system.
Container EMLI
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.
DIY EMLI
For experienced developers who want a minimalist install to set up their own private deep learning repositories or custom builds of deep learning frameworks.
Parallelize your Workload
Not Just for Gaming
GPUs aren’t just for gaming but for accelerating scientific and engineering challenges. Execute workloads that require immense parallelization; simultaneously train deep learning iterations, split render tasks of large 3D scenes, or simulate molecular dynamics calculations in batches for higher throughput and lower time-to-completion. Our graphics processing units are turning into general processing units enabling our every innovation.