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What is Exostellar?

Manage cloud resources seamlessly from a unified interface, optimizing your computing capabilities within your budget while accelerating the development timeline. With no upfront investments required for acquiring reserved instances, you can effortlessly adjust to the fluctuating needs of your projects. Exostellar further refines resource utilization by automatically shifting HPC applications to cost-effective virtual machines. It leverages an advanced OVMA (Optimized Virtual Machine Array), which comprises diverse instance types that maintain key characteristics such as cores, memory, SSD storage, and network bandwidth. This design guarantees that applications operate continuously and without disruption, facilitating smooth transitions among different instance types while preserving current network connections and IP addresses. By inputting your existing AWS computing usage, you can uncover possible savings and improved performance that Exostellar’s X-Spot technology offers to your organization and its applications. This groundbreaking strategy not only simplifies resource management but also enables organizations to enhance their operational efficiency and adapt to changing market dynamics. Consequently, businesses can focus on innovation while ensuring their cloud infrastructure remains cost-effective and responsive.

What is AWS Inferentia?

AWS has introduced Inferentia accelerators to enhance performance and reduce expenses associated with deep learning inference tasks. The original version of this accelerator is compatible with Amazon Elastic Compute Cloud (Amazon EC2) Inf1 instances, delivering throughput gains of up to 2.3 times while cutting inference costs by as much as 70% in comparison to similar GPU-based EC2 instances. Numerous companies, including Airbnb, Snap, Sprinklr, Money Forward, and Amazon Alexa, have successfully implemented Inf1 instances, reaping substantial benefits in both efficiency and affordability. Each first-generation Inferentia accelerator comes with 8 GB of DDR4 memory and a significant amount of on-chip memory. In comparison, Inferentia2 enhances the specifications with a remarkable 32 GB of HBM2e memory per accelerator, providing a fourfold increase in overall memory capacity and a tenfold boost in memory bandwidth compared to the first generation. This leap in technology places Inferentia2 as an optimal choice for even the most resource-intensive deep learning tasks. With such advancements, organizations can expect to tackle complex models more efficiently and at a lower cost.

Media

Media

Integrations Supported

AWS Parallel Computing Service
Amazon EC2 Inf1 Instances
Amazon EC2 Trn1 Instances
Amazon Web Services (AWS)
WithoutBG

Integrations Supported

AWS Parallel Computing Service
Amazon EC2 Inf1 Instances
Amazon EC2 Trn1 Instances
Amazon Web Services (AWS)
WithoutBG

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

Exostellar

Company Location

United States

Company Website

exostellar.io

Company Facts

Organization Name

Amazon

Date Founded

2006

Company Location

United States

Company Website

aws.amazon.com/machine-learning/inferentia/

Categories and Features

Infrastructure-as-a-Service (IaaS)

Analytics / Reporting
Configuration Management
Data Migration
Data Security
Load Balancing
Log Access
Network Monitoring
Performance Monitoring
SLA Monitoring

Categories and Features

Deep Learning

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

Infrastructure-as-a-Service (IaaS)

Analytics / Reporting
Configuration Management
Data Migration
Data Security
Load Balancing
Log Access
Network Monitoring
Performance Monitoring
SLA Monitoring

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