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Join us for a Virtual Event on the essential NVIDIA^® Solution design structure best practices for military readiness. Discover the importance of pre-tested reference architectures and modular design for scalability and easy upgrades. Learn about the secure supply chain, specific deployment security, and post-deployment support requirements. Hear about our deep relationships with leading technology providers to ensure procurement success and early access to cutting-edge NVIDIA technology.

Find out how Silicon Mechanics supports unusual use cases, including those requiring CDI and ruggedized or edge use cases. We will also delve into design best practices for specific workloads, including CFD modeling, big data, AI training vs inference, and AI on the Edge. This Virtual Event is a must-attend for those seeking to improve their military readiness through informed design decisions.

Topics: PNY, NVIDIA, Deep Learning, PNY PRO, Architecture, Military & Aerospace Electronics, NVIDIA RTX A2000, NVIDIA Networking solutions, Virtual Event, Silicon Mechanics, Source Code, AI Training

Data volumes have been increasing for years – and researchers expect they will continue to increase in the coming years.

Meanwhile, edge computing, 5G-fueled hyper-connectivity, artificial intelligence (AI), and other new technologies we’ve been hearing about for years are becoming real solution realities, not research projects.

For these and many other reasons, organizations and the technologists that support them are being forced to reimagine the data center that is still the heart of most data-reliant organizations. Modern data centers need to be ready for what’s next, ideally without downtime or unplanned costs.

Topics: PNY, NVIDIA, Deep Learning, AI, NVIDIA GPU, PNYPRO, Defense, aerospace, Virtualization, GPU acceleration, data center, 5G Networks, high performance computing, NVIDIA RTX, parallel processing, Thinkmate

The NVIDIA^® A100 Tensor Core GPU for PCIe (40GB or 80GB version) delivers unprecedented acceleration at every scale to power the world’s highest performing elastic data centers for AI, data analytics, and HPC. Powered by the NVIDIA Ampere architecture, NVIDIA A100 is the engine of the NVIDIA data center platform, providing up to 20x higher performance over the prior generation, and can be uniquely partitioned into seven GPU instances to dynamically adjust to shifting demands. The NVIDIA A100 (40GB or 80GB) delivers the world’s fastest memory bandwidth at over 2 terabytes per second (TB/s) to run the largest models and datasets.

Topics: PNY, NVIDIA, Deep Learning, AI, NVIDIA GPU, PNYPRO, hpc, data center, high performance computing, Multi-Instance GPU, NVIDIA A100, NVIDIA Data Center GPUs, NVIDIA Ampere, NVIDIA A100 80GB, NVIDIA A100 40GB, Quantum Expresso

Today, we live in a data-centric world where information is constantly generated and transported from devices to data centers for storage and analysis. Technologies such as 5G networking and AI at the Edge further accelerate data generation and big data analytic needs. The sheer volume of data needing to be processed makes NVIDIA GPUs like the NVIDIA A100 the de-facto choice for processing enormous and exponentiating datasets. Modern data centers are composed of hundreds, if not thousands of NVIDIA^® GPUs, just to meet demand for generating actionable insights quickly from massive amounts of raw data.

To maximize the GPU utilization, data center operators must ensure data flows rapidly from storage to individual GPUs across the entire data center, which prevents GPUs from idly waiting for data to arrive. As GPU performance improves and GPU memory size (frame buffer) also expands, the demand for faster network throughput becomes mandatory as well. Today, up to 400 gigabits per second (400 Gb/s) transfer rates in both Ethernet and InfiniBand are available. At this speed, traditional methods of CPU-based data packet processing simply consume too much system resources and start to impact overall system performance. NVIDIA SmartNIC and Data Processing Unit, (DPU) solutions have dedicated data processors to offload data processing from CPUs, improving data availability and supporting advanced new technologies like GPUDirect that measurably boost overall data center processing capabilities.

PNY is excited to be an NVIDIA Networking product partner carrying and supporting both NVIDIA Ethernet and InfiniBand product categories, including NVIDIA ConnectX SmartNICs, NVIDIA BlueField DPUs, associated accessories, and NVIDIA Spectrum Network Switches, and LinkX transceivers and cables.

Topics: Deep Learning, NVIDIA GPU, data center, server, Networking, Compute, NVIDIA Data Center GPUs, Mellanox, ethernet, 400 Gb/s, 200 Gb/s, 100 Gb/s, InfiniBand

The NVIDIA^® A100 Tensor Core GPU offers unprecedented acceleration at every scale and is accelerating the most important work of our time, powering the world’s highest-performing elastic data centers for AI, data analytics, and HPC. Powered by the NVIDIA Ampere architecture, A100 is the engine of the NVIDIA data center platform. A100 provides up to 20x higher performance over the prior generation and can be partitioned into seven GPU instances to dynamically adjust to shifting demands.

Topics: PNY, NVIDIA, Deep Learning, AI, PNY PRO, GPU rendering, NVIDIA GPU Cloud, genomics, hpc, Big Data Analytics, data center, 5G Networks, HTC VIVE Pro Eye, high performance computing, Ampere, NVIDIA RTX GPU, NVIDIA RTX, A100, NVIDIA A100, Tensor Core GPU, NVIDIA Data Center GPUs, Cloud Gaming

Throughout its 47-year history, SIGGRAPH has consistently defined the latest capabilities in graphics by showcasing innovative, cutting edge and emerging technologies across the many sub-fields of computer graphics. This year was no different even as the conference shifted to a virtual event and where NVIDIA once again cast a bright light on exciting new capabilities in advanced graphics, real-time ray tracing, and AI.   

Ground-breaking demos powered by NVIDIA^® Quadro^® RTX^™ products and featuring NVIDIA Research’s latest work, plus technical sessions exploring tools and solutions for graphics will inspire designers, artists, developers and researchers to do their best work with NVIDIA solutions from where ever they work.

 

Topics: PNY, NVIDIA, Deep Learning, NVIDIA GPU, PNYPRO, Tensor Cores, Quadro RTX, nvidia quadro rtx, Artificial Intelligence, NVIDIA NGX Technology, Blender, Quadro Experience, NGX API, AI In-Painting, AI Slow-Mo, AI Up-Res, SIGGRAPH 2020

The NVIDIA^® NGX neural graphics framework is a new deep learning-based SDK that will bring powerful new previously unrealizable AI-based features that accelerate and enhance graphics, image, and video processing to innovative applications from ISVs ranging from industry heavyweights to disruptive startups. NVIDIA NGX utilizes the Tensor Cores found in NVIDIA Quadro^® RTX^™ series products to implement previously unheard-of features while making it easier for developers to integrate AI features into their application(s) with pre-trained neural networks.

Topics: PNY, NVIDIA, Deep Learning, NVIDIA GPU, PNYPRO, Tensor Cores, Quadro RTX, nvidia quadro rtx, Artificial Intelligence, NVIDIA NGX Technology, Quadro Experience, NGX API, AI In-Painting, AI Slow-Mo, AI Up-Res

The annual Interservice/Industry Training, Simulation and Education Conference (I/ITSEC), the world’s largest modeling, simulation and training event, opens today in Orlando, Florida. If you are attending I/ITSEC 2019, we invite you to meet with PNY’s Partners to discuss your NVIDIA Quadro GPU needs, including NVIDIA Quadro MXM Embedded, GPU Down and the latest NVIDIA Quadro RTX Solutions. NVIDIA Quadro GPUs provide the extreme graphics and compute performance demanded by mission-critical aerospace and defense applications. 

Topics: VR, Simulation, NVIDIA Quadro, Deep Learning, AI, CAE, CAD, Technology, Embedded MXM, RT Cores, NVIDIA Turing Architecture, I/ITSEC, Modeling, nvidia quadro rtx, hpc, I/ITSEC 2019, GPU Down, AR, Interservice/Industry, NVIDIA Quadro MXM, Education, Scientific Visualization, Signal Processing, Mission Simulators

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

According to Data Science Central, a leading online resource for data practitioners, forecasts predict the big data market will approach $203 billion by 2020. Data science is powering the engine of modern enterprise – every industry from retail to financial services to healthcare is deriving insight from data to improve competitiveness and operational efficiency. Retailers are improving forecasting to reduce the cost of excess inventory. Financial services institutions are detecting fraudulent transactions. Healthcare providers are predicting the risk of disease more quickly. Even modest improvements in the accuracy of predictive machine learning models can translate into billions on the bottom line. The NVIDIA accelerated Data Science Workstation (DWS) solution with RAPIDS enables enterprises and data scientists to tap into GPU-accelerated machine learning (ML) and deep learning (DL) with faster model iteration, better prediction accuracy, and lowest data science total cost of ownership (TCO).

Topics: PNY, NVIDIA Quadro, Deep Learning, AI, PNYPRO, NVIDIA RTX Technology, Quadro RTX, nvidia quadro rtx, Data Science Workstation, NGC, hpc, graph analytics, data preparation, data science, RAPIDS, GPU-accelerated machine learning, NVIDIA CUDA, analytics, model training, CUDA-X