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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 Amazon EC2 Capacity Blocks for ML?

Amazon EC2 Capacity Blocks are designed for machine learning, allowing users to secure accelerated compute instances within Amazon EC2 UltraClusters that are specifically optimized for their ML tasks. This service encompasses a variety of instance types, including P5en, P5e, P5, and P4d, which leverage NVIDIA's H200, H100, and A100 Tensor Core GPUs, along with Trn2 and Trn1 instances that utilize AWS Trainium. Users can reserve these instances for periods of up to six months, with flexible cluster sizes ranging from a single instance to as many as 64 instances, accommodating a maximum of 512 GPUs or 1,024 Trainium chips to meet a wide array of machine learning needs. Reservations can be conveniently made as much as eight weeks in advance. By employing Amazon EC2 UltraClusters, Capacity Blocks deliver a low-latency and high-throughput network, significantly improving the efficiency of distributed training processes. This setup ensures dependable access to superior computing resources, empowering you to plan your machine learning projects strategically, run experiments, develop prototypes, and manage anticipated surges in demand for machine learning applications. Ultimately, this service is crafted to enhance the machine learning workflow while promoting both scalability and performance, thereby allowing users to focus more on innovation and less on infrastructure. It stands as a pivotal tool for organizations looking to advance their machine learning initiatives effectively.

Media

Media

Integrations Supported

AWS Neuron
AWS Nitro System
AWS Trainium
Amazon EC2
Amazon EC2 G5 Instances
Amazon EC2 Inf1 Instances
Amazon EC2 P4 Instances
Amazon EC2 P5 Instances
Amazon EC2 Trn1 Instances
Amazon EC2 Trn2 Instances
Amazon EC2 UltraClusters
Amazon EKS
Amazon Elastic Container Service (Amazon ECS)
Amazon SageMaker
Amazon Web Services (AWS)
Greenovative
HPE Ezmeral
PyTorch
TensorFlow

Integrations Supported

AWS Neuron
AWS Nitro System
AWS Trainium
Amazon EC2
Amazon EC2 G5 Instances
Amazon EC2 Inf1 Instances
Amazon EC2 P4 Instances
Amazon EC2 P5 Instances
Amazon EC2 Trn1 Instances
Amazon EC2 Trn2 Instances
Amazon EC2 UltraClusters
Amazon EKS
Amazon Elastic Container Service (Amazon ECS)
Amazon SageMaker
Amazon Web Services (AWS)
Greenovative
HPE Ezmeral
PyTorch
TensorFlow

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

Amazon

Date Founded

1994

Company Location

United States

Company Website

aws.amazon.com/ec2/capacityblocks/

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

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

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