SC19: Boxx To Demo Workstations; Introduce Data Science System
November 18, 2019

SC19: Boxx To Demo Workstations; Introduce Data Science System

AUSTIN, TX — Boxx Technologies ( will demonstrate its Apexx W4L ProViz workstation at Supercomputing 2019 (SC19) in Denver, CO later this month. The W4L features an Intel Xeon W series processor and two Nvidia Quadro RTX GPUs. The company will also demonstrate a GoBoxx laptop and introduce a 16-GPU, rack mounted system for GPU-centric workflows. 
“Organizations shouldn’t have to wait for the latest technology that will be instrumental to their success,” says Bill Leasure, Boxx VP of Marketing. “That’s why Boxx works closely with our industry partners to quickly bring to market both CPU and GPU-centric systems unavailable from any other hardware manufacturer and purpose-built for machine learning, AI, or rendering.” 

Equipped with a 28-core (56 thread) Intel Xeon W-3275 processor and 128GB of memory, the powerful Apexx W4L ProViz features up to four Nvidia Quadro RTX GPUs. Purpose-built to accelerate rendering and simulation, Apexx W4L Pro Viz is well suited for artificial intelligence applications. Intel Xeon 3200 series processors offer up to 4.6GHz with Intel Turbo Boost Max Technology 3.0, 64 processor PCIe lanes for more I/O throughput for networking, graphics, and storage, and new Intel Deep Learning Boost for accelerated AI performance.

Rounding out the Boxx SC19 product line is the new Raxx P6G Jupiter featuring a 2nd generation, 64-core, AMD EPYC processor and up to 16 Nvidia Quadro RTX 6000 GPUs. Also available with an Intel processor, the 6U, rack-mounted system geared toward deep learning development, rendering, simulation and other GPU-centric workflows. Raxx P6G Jupiter dramatically accelerates GPU rendering and look development, enabling more refined visuals in less time. 

The Boxx booth will also host Pac Storage, a provider of data storage systems which support SAN/NAS and Cloud Gateways with 16GB Fibre Channel, as well as 10, 25, and 40GB connectivity scaling to Petabytes of storage.