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What is NVIDIA Run:ai?

NVIDIA Run:ai is a powerful enterprise platform engineered to revolutionize AI workload orchestration and GPU resource management across hybrid, multi-cloud, and on-premises infrastructures. It delivers intelligent orchestration that dynamically allocates GPU resources to maximize utilization, enabling organizations to run 20 times more workloads with up to 10 times higher GPU availability compared to traditional setups. Run:ai centralizes AI infrastructure management, offering end-to-end visibility, actionable insights, and policy-driven governance to align compute resources with business objectives effectively. Built on an API-first, open architecture, the platform integrates with all major AI frameworks, machine learning tools, and third-party solutions, allowing seamless deployment flexibility. The included NVIDIA KAI Scheduler, an open-source Kubernetes scheduler, empowers developers and small teams with flexible, YAML-driven workload management. Run:ai accelerates the AI lifecycle by simplifying transitions from development to training and deployment, reducing bottlenecks, and shortening time to market. It supports diverse environments, from on-premises data centers to public clouds, ensuring AI workloads run wherever needed without disruption. The platform is part of NVIDIA's broader AI ecosystem, including NVIDIA DGX Cloud and Mission Control, offering comprehensive infrastructure and operational intelligence. By dynamically orchestrating GPU resources, Run:ai helps enterprises minimize costs, maximize ROI, and accelerate AI innovation. Overall, it empowers data scientists, engineers, and IT teams to collaborate effectively on scalable AI initiatives with unmatched efficiency and control.

What is CloudPhysics?

HPE CloudPhysics is an intuitive SaaS platform tailored to oversee and evaluate IT infrastructures, delivering crucial insights and comprehensive reports that aid in the enhancement, repair, and adjustment of data centers to align with changing requirements. This cutting-edge solution simulates possible migrations to diverse cloud environments, estimating both expenses and viability, while also constructing an accurate virtual model of your infrastructure on an individual machine level, thereby furnishing you with essential information to make educated choices regarding cloud versus on-premises solutions. Rather than depending on tedious spreadsheets, users can quickly access migration strategies and cost analyses for the cloud, simplifying the decision-making journey. The platform facilitates the effective classification, grouping, and consolidation of all workloads within the data center into a singular perspective. By utilizing HPE CloudPhysics’ comprehensive workload-sizing and costing frameworks, businesses can align themselves with optimized cloud pricing models, realizing significant enterprise-level benefits. Moreover, prior to investing in new hardware and resources for the upcoming IT budget cycle, it is crucial to gain a comprehensive understanding of current workloads to ensure the most strategic investment decisions are made. This forward-thinking strategy not only boosts cost-effectiveness but also guarantees that your IT infrastructure is well-prepared for future challenges and opportunities. Ultimately, organizations can achieve greater agility and responsiveness in a rapidly evolving technological landscape.

Media

Media

Integrations Supported

HPE Ezmeral

Integrations Supported

HPE Ezmeral

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
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

NVIDIA

Date Founded

1993

Company Location

United States

Company Website

www.nvidia.com/en-us/software/run-ai/

Company Facts

Organization Name

CloudPhysics

Date Founded

2011

Company Location

United States

Company Website

www.cloudphysics.com

Categories and Features

Deep Learning

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

Virtualization

Archiving & Retention
Capacity Monitoring
Data Mobility
Desktop Virtualization
Disaster Recovery
Namespace Management
Performance Management
Version Control
Virtual Machine Monitoring

Categories and Features

Virtualization

Archiving & Retention
Capacity Monitoring
Data Mobility
Desktop Virtualization
Disaster Recovery
Namespace Management
Performance Management
Version Control
Virtual Machine Monitoring

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