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Deploy AI and Deep Learning
the Right Way

Advance Your Research with the Experts and Infrastructure Designs Chosen by Top AI Labs

Accessing Hidden Data Requires More than Just Algorithms

Running Artificial Intelligence (AI) projects requires a complex computing environment tailored to the needs of your specific project as well as your organization’s IT requirements.

Many traditional, processor-driven HPC systems do not have the power to effectively run AI applications and deep learning frameworks. Graphics processing unit (GPU)-based acceleration dramatically boosts the compute power of a standard system, allowing you to complete complex, compute intensive tasks quickly and efficiently.

However, GPUs aren’t the solution for all use cases and organizations and there is no single, turn-key way to configure your AI environment. That’s why it’s critical to have a partner with enough AI experience and expertise in big data storage, networking, and advanced computing to help you achieve your goals.



Faster, More Efficient AI with GPU Processors

Choose from servers supporting a variety of GPU processors, including the NVIDIA® Tesla® V100 GPU, one of the accelerators driving the next wave of scientific breakthroughs and enabling researchers to tackle challenges that were once impossible.

With over 500 HPC applications accelerated (including 10 of the top 10) as well as every AI deep learning framework, the Tesla V100 allows researchers to maximize the use of computing cycles. This provides the flexibility and power to needed in modern AI projects in medical imaging, pathology, health informatics, drug discovery, and other demanding use cases.

NVIDIA Tesla V100 Volta GPU Penguin Computing AI Deep Learning Accelerated

Selected Applications Supported by NVIDIA-based Penguin Computing GPU servers:

  • Amber
  • ANSYS Fluent
  • Gaussian
  • Gromacs
  • LS-DYNA
  • NAMD
  • OpenFOAM
  • Simulia Abaqus
  • VASP
  • WRF

Selected Deep Learning Frameworks Supported by NVIDIA-based Penguin Computing GPU servers:

  • Caffe2
  • Microsoft Cognitive Toolkit
  • MXNET
  • Pytorch
  • TensorFlow
  • Theano

Selected Applications Supported by NVIDIA-based Penguin Computing GPU servers:

  • Amber
  • ANSYS Fluent
  • Gaussian
  • Gromacs
  • LS-DYNA
  • NAMD
  • OpenFOAM
  • Simulia Abaqus
  • VASP
  • WRF

Selected Deep Learning Frameworks Supported by NVIDIA-based Penguin Computing GPU servers:

  • Caffe2
  • Microsoft Cognitive Toolkit
  • MXNET
  • Pytorch
  • TensorFlow
  • Theano

Create Breakthroughs in AI Effectiveness with Deep Learning

At its core, AI attempts to recreate the neural networks and pathways found in the human brain using immense computing power and complex algorithms, offloading and, thus speeding up, analysis of large amounts of data via computer-based “brains.” Machine learning is one way to achieve AI by teaching a computer how to classify and process different types of information so that it can perform image recognition, medical diagnosis, prediction, and other functions. Deep learning takes this one step further, training this computer ”brain” to recognize patterns in speech and other, even more complex data so that the computer can perform the functions of a voice-activated assistant, automatic machine translations, automatic image captioning, and other functions more similar to human thinking.

Recent years have seen amazing successes in natural speech translation, image recognition, medical diagnosis, financial market prediction, and much more. Penguin Computing helps accelerate deep learning workflows with:

  • State-of-the-art, cost-effective technology
  • Flexible configurations that scale
  • Expert services to keep your systems at peak performance and even run your jobs

Solutions for AI