If you are one of the millions of creative and technical professionals that rely on NVIDIA® Quadro® graphics cards in your office workstation, but find yourself working from home during the COVID-19 pandemic on your personal workstation or professional PC, then here are three reasons why you should utilize a system with an NVIDIA Quadro graphics card while working from home.
Modern GPUs have the ability to connect multiple displays to a single graphics card. Multi-screen environments increase the impact of visual imaging across commercial environments, and enhance enthusiast home gaming environments.
Technologies such as NVIDIA Surround and AMD Eyefinity offer the ability to bind multiple displays together in software to create a massive virtual display. However, Surround and Eyefinity are both consumer-level technologies, which come with a multitude of limitations and compromises, such as support for only 3 to 6 monitors, which are not suitable for creative professional, interactive digital signage, live events, security, or industrial applications.
Topics: NVIDIA, NVIDIA Quadro, Immersive Displays, Pro Tip, Quadro RTX, NVIDIA Turing Architecture, Artificial Intelligence, Multi-display, NVIDIA Mosaic, GPU acceleration, Quadro RTX GPUs, Education, NVIDIA Quadro Solutions
Data security is the top priority of every CTO. If you are deploying Quadro RTX graphics cards in mission-critical workstations or servers, and want to secure your data, Quadro RTX graphics cards are the right choice. PNY can help you permanently disable the data path on the VirtualLink (USB Type-C) port to prevent unauthorized data access through VirtualLink.
Topics: PNY, NVIDIA, NVIDIA Quadro, PNY PRO, NVIDIA GPUs, NVIDIA RTX Technology, Pro Tip, rtx 6000, rtx 5000, RTX 4000, NVIDIA Quadro RTX 6000, nvidia quadro rtx, virtuallink, GeForce RTX, NVIDIA Quadro RTX 5000, QuadroRTX8000, NVIDIA Quadro RTX 8000, RTX Server
In this blog post, let’s address the difference between the following driver variations and help you choose the right driver for your graphics card investment.
- GeForce Game-Ready driver vs. Studio driver
- Quadro ODE driver vs. QNF driver
- Standard Package vs DCH Package
This is going to be a long blog post, but by the end, you will have an Ubuntu environment connected to the NVIDIA GPU Cloud platform, pulling a TensorFlow container and ready to start benchmarking GPU performance.
Let's split this into four phases:
1) Install Ubuntu 18.04 LTS and NVIDIA Graphics Driver
2) Install Docker CE and NVIDIA Docker v 2.0
3) Setup NVIDIA GPU Cloud and pull down GPU optimized docker containers
4) Run the TensorFlow benchmark
It's time to get started!
In this blog post, I will go over the hardware considerations I used when putting together a system for benchmarking GPU performance for Deep Learning using Ubuntu 18.04, NVIDIA GPU Cloud (NGC) and TensorFlow. Keep in mind that everyone will have different budgets and requirements for their own systems, which can and will result in a wide range of configurations. My particular list should serve only as a reference; your system will likely be different based on your own requirements.
Before we dive into the details, let’s go over what we are seeking to accomplish. Our goal is to build a system to test the compute performance between different GPUs; therefore the GPU should be the only variable that changes between the different test runs. To ensure the consistency of our tests, we will remove any potential bottlenecks that will negatively impact GPU performance.
Topics: PNY, NVIDIA, NVIDIA Quadro, PNYPRO, NVIDIA GPU Cloud, Pro Tip, Turing, Quadro RTX, Artificial Intelligence, GeForce RTX, NGC, GPU-accelerated machine learning, NVIDIA CUDA, CUDA-X, Linux, Tensorflow, benchmark
As a PC enthusiast, I love pitting hardware solutions against each other to determine their relative performance when completing a particular task. This process is also known as “Benchmarking.” Benchmarking results are usually considered the best tool to evaluate the merits of competing systems when making a purchase decision.
In this 3-part blog series, we’ll discuss how to build a system, with an emphasis on benchmarking GPU performance for Deep Learning using Ubuntu 18.04, NVIDIA GPU Cloud (NGC) and TensorFlow.
Topics: Deep Learning, NVIDIA GPU, NVIDIA GPUs, NVIDIA Quadro GPUs, CUDA, NVIDIA RTX Technology, Pro Tip, Tensor Cores, Quadro RTX, NVIDIA Turing Architecture, nvidia quadro rtx, Artificial Intelligence, NVIDIATuring, GeForce RTX, Data Science Workstation, NGC, data science, RAPIDS, GPU-accelerated machine learning, NVIDIA CUDA, analytics, CUDA-X, Linux, Tensorflow, benchmark
SOLIDWORKS Visualize is a powerful tool used by engineers and designers to turn CAD files into photorealistic rendered images or immersive VR experiences. The ability to quickly generate photorealistic quality rendering is crucial for design reviews, factory floor (or other) training, and collaboration with marketing and sales.
Today, we will conclude our three part discussion about VirtualLink.
In Tip #7, we covered the intended application of VirtualLink to simplify future VR headset connection, then in Tip #9, we covered the alternative applications, such as high-speed data transfer and easy monitor hook up. Today, we will cover the potential issues with the inclusion of this new standard and how PNY can assist you in dealing with these issues.
In our PNY Pro Tip #7, we introduced the latest VirtualLink addition to the Turing RTX GPU output offering, and how it was developed to make VR more accessible. While the idea of one cable connection for VR is great, there are currently no VR HMDs (Head Mounted Display) available with VirtualLink. In this follow up blog, we will go over 3 ideas that our readers can use the VirtualLink port for.