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HIAWATHA, Iowa--(BUSINESS WIRE)--Crystal Group, Inc., a leading designer and manufacturer of rugged computer and electronic hardware, announced today that it has been named a preferred original equipment manufacturer (OEM) in the NVIDIA® Partner Network (NPN). Crystal Group joins a select group of companies that develop and build their solutions using NVIDIA GPU-accelerated technology.

This heightened collaboration accelerates Crystal Group’s capacity to architect and certify its rugged high-performance compute solutions that integrate NVIDIA’s latest technologies to meet customers’ immediate and evolving needs for demanding industrial and defense AI applications at the edge.

Crystal Group is currently designing and testing new compute and visualization solutions using NVIDIA A100 and RTX A6000 GPUs. The unprecedented speed and capacity to collect, process and prioritize incoming sensor data are vital to the accuracy of split-second, real-time decisions and operations.

Download NVIDIA nForce Networking Controller for Windows to net driver. Update Windows network adapter drivers for your Acer Ferrari laptop. WLan Driver 802.11n Rel.

  • NVIDIA Mellanox LinkX ™ cables and transceivers offers cost-effective, complete Ethernet and InfiniBand interconnect solutions for creating high-bandwidth fabrics. LinkX products are designed to operate in the intensely demanding InfiniBand HPC supercomputer, Ethernet Hyperscale and enterprise environments from 1G-to-400Gb/s.
  • Cumulus, based in Mountain View, Calif., supports more than 100 hardware platforms with Cumulus Linux, its operating system for network switches. Our ultrafast NVIDIA Mellanox Spectrum switches already ship with Cumulus Linux and SONiC, the open source offering forged in Microsoft’s Azure cloud and managed by the Open Compute Project.

“Edge computing needs and opportunities for seamless AI, autonomy and visualization capabilities are increasing exponentially – both in complexity and performance expectations,” said Jim Shaw, executive vice president of Engineering for Crystal Group. “Integrating NVIDIA GPUs into our rugged hardware solutions ensures critical applications, like power distribution, oil and gas exploration, and autonomous vehicles on our roadways and in theater, have the data processing power and speed needed for flawless performance in highly unpredictable, challenging environments.”

“The NVIDIA OEM program provides our partners the resources and access to NVIDIA GPU, networking and software technologies,” said Craig Wiener, director of Strategic Partners at NVIDIA. Edited to add some pix. Notice the 'bad' service is called 'NVIDIA Network Stream Service'. However, there are 3 similar services, but none exactly identical; NVIDIA Network Service, NVIDIA Streamer Network Service, and NVIDIA Streamer Service. Also, running GeForce Experience v2.11.3.5 (not a beta).

“The NVIDIA OEM program provides our partners the resources and access to NVIDIA GPU, networking and software technologies,” said Craig Wiener, director of Strategic Partners at NVIDIA. “NPN OEM partners, such as Crystal Group, have the technology support to develop sophisticated accelerated computing platforms to solve today’s most complex compute challenges.”

By integrating NVIDIA GPUs into its rugged, high-performance compute offerings, Crystal Group enables its government and industrial customers to analyze petabytes of data in critical edge environments where failure isn’t an option.

About Crystal Group, Inc.

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Crystal Group, Inc. is a technology leader in rugged computer hardware, specializing in the design and manufacture of custom and commercial rugged servers, embedded computing, networking devices, displays, and data storage for high reliability in harsh environments. A small employee-owned business founded in 1987, Crystal Group provides the defense, government and industrial markets with integrated solutions that bring seamless, real-time artificial intelligence, autonomy and cybersecurity to demanding edge applications.

Crystal Group products meet or exceed IEEE, IEC, and military standards, including MIL-STD-810, 167-1, 461, and MIL-S-901, and are backed by a five-plus-year warranty. All products are manufactured in the company’s facility, certified to ISO 9001:2015/AS9100D quality management standards.

©2020 Crystal Group Inc. All rights reserved. All marks are property of their respective owners. Design and specifications are subject to change.


The NVIDIA CUDA® Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers.

Deep learning researchers and framework developers worldwide rely on cuDNN for high-performance GPU acceleration. It allows them to focus on training neural networks and developing software applications rather than spending time on low-level GPU performance tuning. cuDNN accelerates widely used deep learning frameworks, including Caffe2, Chainer, Keras, MATLAB, MxNet, PaddlePaddle, PyTorch, and TensorFlow. For access to NVIDIA optimized deep learning framework containers that have cuDNN integrated into frameworks, visit NVIDIA GPU CLOUD to learn more and get started.

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Comparing the throughput on a single DGX-1V server, cuDNN 7.6.5 vs. DGX-A100, cuDNN 8.0.5, on 20.11 NGC container. End-to-end performance runs to convergence.

What’s New in cuDNN 8.1


cuDNN 8.1 is optimized for A100 GPUs delivering up to 5x higher performance versus V100 GPUs out of the box and includes new optimizations and APIs for applications such as conversational AI and computer vision. It has been redesigned for ease of use, application integration, and offers greater flexibility to developers.

cuDNN 8.1 highlights include:

  • Support for BFloat16 for CNNs on NVIDIA Ampere architecture GPUs
  • New and easy C++ front-end API available in open source, wraps flexible v8 backend C API
  • Flexibly fuse operators such as convolutions, point-wise operations and reductions to speed up CNNs
  • New optimizations for computer vision, speech, and natural language understanding networks

cuDNN 8.1 is now available as six smaller libraries, providing granularity when integrating into applications. Developers can download cuDNN or pull it from framework containers on NGC.

Read the latest cuDNN release notes for a detailed list of new features and enhancements.

Key Features

  • Tensor Core acceleration for all popular convolutions including 2D, 3D, Grouped, Depth-wise separable, and Dilated with NHWC and NCHW inputs and outputs
  • Optimized kernels for computer vision and speech models including ResNet, ResNext, EfficientNet, EfficientDet, SSD, MaskRCNN, Unet, VNet, BERT, GPT-2, Tacotron2 and WaveGlow
  • Supports FP32, FP16, BF16 and TF32 floating point formats and INT8, and UINT8 integer formats
  • Arbitrary dimension ordering, striding, and sub-regions for 4d tensors means easy integration into any neural net implementation
  • Speed up fused operations on any CNN architecture

cuDNN is supported on Windows and Linux with Ampere, Turing, Volta, Pascal, Maxwell, and Kepler GPU architectures in data center and mobile GPUs.

cuDNN Accelerated Frameworks

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Learn More

  • Blogs on Programming Tensor Cores in cuDNN
  • Related libraries and software:
    • DALI: For fast AI data-preprocessing
    • NVIDIA GPU Cloud: For containers
  • Find other cuDNN developers on NVIDIA Developer Forums
  • For questions or to provide feedback, please contact [email protected]
  • To file bugs or report an issue,register on NVIDIA Developer Zone
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