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What is NVIDIA GPU-Optimized AMI?

The NVIDIA GPU-Optimized AMI is a specialized virtual machine image crafted to optimize performance for GPU-accelerated tasks in fields such as Machine Learning, Deep Learning, Data Science, and High-Performance Computing (HPC). With this AMI, users can swiftly set up a GPU-accelerated EC2 virtual machine instance, which comes equipped with a pre-configured Ubuntu operating system, GPU driver, Docker, and the NVIDIA container toolkit, making the setup process efficient and quick. This AMI also facilitates easy access to the NVIDIA NGC Catalog, a comprehensive resource for GPU-optimized software, which allows users to seamlessly pull and utilize performance-optimized, vetted, and NVIDIA-certified Docker containers. The NGC catalog provides free access to a wide array of containerized applications tailored for AI, Data Science, and HPC, in addition to pre-trained models, AI SDKs, and numerous other tools, empowering data scientists, developers, and researchers to focus on developing and deploying cutting-edge solutions. Furthermore, the GPU-optimized AMI is offered at no cost, with an additional option for users to acquire enterprise support through NVIDIA AI Enterprise services. For more information regarding support options associated with this AMI, please consult the 'Support Information' section below. Ultimately, using this AMI not only simplifies the setup of computational resources but also enhances overall productivity for projects demanding substantial processing power, thereby significantly accelerating the innovation cycle in these domains.

What is GPUEater?

Persistence container technology streamlines operations through a lightweight framework, enabling users to be billed by the second rather than enduring long waits of hours or months. The billing process, which will be conducted through credit card transactions, is scheduled for the subsequent month. This innovative technology provides exceptional performance at a cost-effective rate compared to other available solutions. Moreover, it is poised for implementation in the world's fastest supercomputer at Oak Ridge National Laboratory. A variety of machine learning applications, such as deep learning, computational fluid dynamics, video encoding, and 3D graphics, will gain from this technology, alongside other GPU-dependent tasks within server setups. The adaptable nature of these applications showcases the extensive influence of persistence container technology across diverse scientific and computational domains. In addition, its deployment is likely to foster new research opportunities and advancements in various fields.

Media

Media

Integrations Supported

AWS Marketplace
Amazon Web Services (AWS)
Azure Marketplace
Gaia
Keras
NVIDIA NGC
OpenLIT
PyTorch
TensorFlow

Integrations Supported

AWS Marketplace
Amazon Web Services (AWS)
Azure Marketplace
Gaia
Keras
NVIDIA NGC
OpenLIT
PyTorch
TensorFlow

API Availability

Has API

API Availability

Has API

Pricing Information

$3.06 per hour
Free Trial Offered?
Free Version

Pricing Information

$0.0992 per hour
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Amazon

Date Founded

1994

Company Location

United States

Company Website

aws.amazon.com/marketplace/pp/prodview-7ikjtg3um26wq

Company Facts

Organization Name

GPUEater

Company Location

United States

Company Website

gpueater.com

Categories and Features

Deep Learning

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

HPC

Categories and Features

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