In order to fuel massive digital twins, NVIDIA has just unveiled NVIDIA® OVXTM, a new computing system.
NVIDIA OVX optimized for running sophisticated digital twin simulations in NVIDIA OmniverseTM, a real-time, physically accurate world simulation and 3D design collaboration platform.
In order to create digital twins that are accurate to the physical world, the OVX system combines high-performance GPU-accelerated computing, graphics, and AI with high-speed storage access, low-latency networking, and precision timing.
Modeling entire structures, factories, cities, and even the entire planet will modeled using OVX’s simulation of complex digital twins.
“Physically accurate digital twins are the future of how we design and build,” said Bob Pette, NVIDIA’s vice president of Professional Visualization. As a result of digital twins, business strategy across all sectors will need to rethought. True, real-time, always-synchronous, industrial-scale digital twins can powered by the OVX portfolio of systems.
With OVX, architects, engineers, and city planners will be able to create massive, physically accurate digital twins of buildings or create true-to-reality simulated environments with accurate time synchronization between the real and virtual worlds.
Before deploying them into the real world, factories, warehouses, robots, and autonomous vehicles can all evaluated and tested in a virtual environment with multiple autonomous systems interacting in real-time.
To run these extremely complex simulations and scenarios, as well as generate the data necessary for intensive machine learning development, NVIDIA OVX will provide the scale, performance, and compute capabilities required for the current project.
Computing System Specifications & Availability
The OVX server has 1 terabyte of RAM, 16 terabytes of NVMe storage, and eight NVIDIA A40 GPUs. Additionally, there are three NVIDIA ConnectX®-6 Dx 200Gbps NICs.
Large-scale digital twin simulations can accelerated by scaling the OVX computing system from a single pod of eight OVX servers to an OVX SuperPOD of 32 OVX servers connected via NVIDIA Spectrum-3 switch fabric, or even multiple OVX SuperPODs.
NVIDIA-Certified systems at the heart of OVX solutions, having rigorously tested and validated to ensure they deliver on promised levels of performance, manageability, security, and scalability.
NVIDIA and OEM system builders will collaborate to offer full enterprise-level support for OVX solutions and Omniverse software. Inspur, Lenovo, and Supermicro will all sell NVIDIA OVX by the end of the year.
Watch Jensen Huang, CEO of NVIDIA, speaking at GTC 2022 to find out more about NVIDIA Omniverse. Attending GTC is free, and it will allow you to attend sessions hosted by NVIDIA and other industry leaders.
American multinational technology firm Nvidia Corporation headquartered in Santa Clara, California, and founded in Delaware.
Graphics Processing Units (GPUs), APIs for data science and high-performance computing, and system-on-a-chip units (SoCs) for the mobile computing and automotive markets are all products of this software and fabless company.
Nvidia has expanded its presence in the gaming industry with the release of the Shield Portable, Shield Tablet, and Shield Android TV as well as its cloud gaming service, GeForce Now, and its GPUs used for edge-to-cloud computing and supercomputers.
Nvidia’s API CUDA enables programmers to take advantage of the greater number of cores present in GPUs to parallelize BLAS operations, which commonly used in machine learning algorithms and are therefore a primary use case for Nvidia GPUs in deep learning and accelerated analytics.
Prior to Elon Musk’s announcement at Tesla Autonomy Day 2019 that the company had developed its own SoC and Full Self-Driving computer, Nvidia hardware installed in many Tesla vehicles.
Scientists and engineers use these graphics processing units, as do scientists in laboratories, developers, and large corporations. When “deep-learning neural networks combined with Nvidia graphics processing units (GPUs)” in 2009, a huge leap forward made in the field.
Andrew Ng found that the speed of deep-learning systems could increased by about 100 times using GPUs that year thanks to the work done by the Google Brain using Nvidia GPUs to create Deep Neural Networks capable of machine learning.
Nvidia creates a family of powerful computers called DGX.
Using a cluster of eight graphics processing units (GPUs), Nvidia released the DGX-1 in April 2016 to facilitate more effective deep learning by users. Moreover, it deployed Nvidia Tesla K80 and P100 GPU-based virtual machines to Google Cloud in November 2016.
Microsoft has expanded its N series to include GPU servers powered by Nvidia Tesla K80s, which feature 4992 cores each. AWS’s P2 instance followed later that year, and it supports up to 16 Nvidia Tesla K80 GPUs.
In May of 2018, scientists in Nvidia’s artificial intelligence division had the epiphany that a robot might be able to learn a task by observing a human performing it.
They developed a system that, with some minor tweaks and testing, will be ready to operate the next generation of universal robots. Nvidia not only produces GPUs, but also provides parallel processing capabilities to researchers and scientists so that they can run high-performance applications with minimal overhead.
FAQs – People Also Ask
What does NVIDIA Omniverse do?
Based on Pixar’s Universal Scene Description and NVIDIA RTXTM technology, NVIDIA OmniverseTM is a scalable, multi-GPU real-time reference development platform for 3D simulation and design collaboration.
Is Omniverse the same as metaverse?
As opposed to the multiverse, which is a collection of independent universes, the metaverse is one continuous, interconnected space. On the other hand, the omniverse includes both the metaverse and the multiverse in its entirety.
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