HyperStation DLX-4R

Featuring a 14-core, high-performance Intel Xeon W processor, up to four GPUs, and 512GB DDR4-2666MHz, the HyperStation DLX-4R allows developers and researchers to experiment on models of a more complicated nature, using much larger data sets.

Intel Xeon Scalable processors take AI performance to the next level with Intel Deep Learning Boost (Intel DL Boost), a new set of embedded processor technologies designed to accelerate AI workloads such as image recognition, object detection, speech recognition and language translation. It extends Intel Advanced Vector Extensions 512 (Intel AVX-512) with a new Vector Neural Network Instruction (VNNI) that significantly increases deep learning inference performance over previous generations.

Our default specification with 4x RTX 2080 Ti GPU's can deliver up to 54 TFLOPS of FP32 compute performance. With the ability to scale up to a maximum of 4x Tesla V100 GPU's, for the most demanding development and training needs.

All of our systems are thoroughly tested for any potential thermal throttling and are available pre-installed with Ubuntu, and any framework you require, including CUDA, DIGITS, Caffe Pytorch, Tensorflow, Theano, and Torch.

Powered by 4x NVIDIA RTX 2080 Ti Turbo GPUs (configurable with up to 4x NVIDIA Titan RTX's), and the Intel Xeon W-2175, the HyperStation DLX-4R is designed for users requiring the ultimate in GPU power and stability within a desktop form factor.

Plus, with 12 CPU Cores, and Turbo Boost speeds of up to 4.3GHz, this is a brilliant 'all-rounder', not only for heavy GPU workloads but also single-thread and multi-threaded CAD/CAM applications.

Recommended for: Autodesk VRED VRAY RT Keyshot SolidWorks Visualize Catia Live NVIDIA Iray

Powered by 4x NVIDIA RTX 2080 Ti Turbo GPUs, and the Intel Xeon W-2175, the HyperStation DLX-4R is designed for users requiring the ultimate in GPU power and stability within a desktop form factor.

Plus, with 12 CPU Cores, and Turbo Boost speeds of up to 4.3GHz, this is a brilliant 'all-rounder', not only for heavy GPU workloads but also single-thread and multi-threaded applications.