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Description
Amazon EC2 P4d instances are designed for optimal performance in machine learning training and high-performance computing (HPC) applications within the cloud environment. Equipped with NVIDIA A100 Tensor Core GPUs, these instances provide exceptional throughput and low-latency networking capabilities, boasting 400 Gbps instance networking. P4d instances are remarkably cost-effective, offering up to a 60% reduction in expenses for training machine learning models, while also delivering an impressive 2.5 times better performance for deep learning tasks compared to the older P3 and P3dn models. They are deployed within expansive clusters known as Amazon EC2 UltraClusters, which allow for the seamless integration of high-performance computing, networking, and storage resources. This flexibility enables users to scale their operations from a handful to thousands of NVIDIA A100 GPUs depending on their specific project requirements. Researchers, data scientists, and developers can leverage P4d instances to train machine learning models for diverse applications, including natural language processing, object detection and classification, and recommendation systems, in addition to executing HPC tasks such as pharmaceutical discovery and other complex computations. These capabilities collectively empower teams to innovate and accelerate their projects with greater efficiency and effectiveness.
Description
Deploy GPU-accelerated instances that can be finely tuned to suit your AI requirements and financial plan. Secure access to thousands of GPUs within a cutting-edge AI data center, ideal for extensive training and inference operations. The trend in the AI landscape is clearly leaning towards GPU cloud solutions, allowing for the creation and deployment of innovative models while alleviating the challenges associated with infrastructure management and resource limitations. AI-focused cloud providers significantly surpass conventional hyperscalers in terms of availability, cost efficiency, and the ability to scale GPU usage for intricate AI tasks. Ori boasts a diverse array of GPU types, each designed to meet specific processing demands, which leads to a greater availability of high-performance GPUs compared to standard cloud services. This competitive edge enables Ori to deliver increasingly attractive pricing each year, whether for pay-as-you-go instances or dedicated servers. In comparison to the hourly or usage-based rates of traditional cloud providers, our GPU computing expenses are demonstrably lower for running extensive AI operations. Additionally, this cost-effectiveness makes Ori a compelling choice for businesses seeking to optimize their AI initiatives.
API Access
Has API
API Access
Has API
Integrations
AWS Batch
AWS Deep Learning AMIs
AWS Neuron
AWS Nitro System
AWS Trainium
Amazon EC2
Amazon EC2 Capacity Blocks for ML
Amazon EC2 G5 Instances
Amazon EC2 P5 Instances
Amazon EC2 Trn1 Instances
Integrations
AWS Batch
AWS Deep Learning AMIs
AWS Neuron
AWS Nitro System
AWS Trainium
Amazon EC2
Amazon EC2 Capacity Blocks for ML
Amazon EC2 G5 Instances
Amazon EC2 P5 Instances
Amazon EC2 Trn1 Instances
Pricing Details
$11.57 per hour
Free Trial
Free Version
Pricing Details
$3.24 per month
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Amazon
Founded
1994
Country
United States
Website
aws.amazon.com/ec2/instance-types/p4/
Vendor Details
Company Name
Ori
Founded
2018
Country
United Kingdom
Website
www.ori.co
Product Features
Deep Learning
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization
