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What is Exafunction?

Exafunction significantly boosts the effectiveness of your deep learning inference operations, enabling up to a tenfold increase in resource utilization and savings on costs. This enhancement allows developers to focus on building their deep learning applications without the burden of managing clusters and optimizing performance. Often, deep learning tasks face limitations in CPU, I/O, and network capabilities that restrict the full potential of GPU resources. However, with Exafunction, GPU code is seamlessly transferred to high-utilization remote resources like economical spot instances, while the main logic runs on a budget-friendly CPU instance. Its effectiveness is demonstrated in challenging applications, such as large-scale simulations for autonomous vehicles, where Exafunction adeptly manages complex custom models, ensures numerical integrity, and coordinates thousands of GPUs in operation concurrently. It works seamlessly with top deep learning frameworks and inference runtimes, providing assurance that models and their dependencies, including any custom operators, are carefully versioned to guarantee reliable outcomes. This thorough approach not only boosts performance but also streamlines the deployment process, empowering developers to prioritize innovation over infrastructure management. Additionally, Exafunction’s ability to adapt to the latest technological advancements ensures that your applications stay on the cutting edge of deep learning capabilities.

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 EC2 Trn3 Instances
AWS Parallel Computing Service
Amazon EC2 Inf1 Instances
Amazon EC2 Trn1 Instances
Anyscale
PyTorch
TensorFlow
WithoutBG

Integrations Supported

AWS EC2 Trn3 Instances
AWS Parallel Computing Service
Amazon EC2 Inf1 Instances
Amazon EC2 Trn1 Instances
Anyscale
PyTorch
TensorFlow
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

Exafunction

Company Website

exafunction.com

Company Facts

Organization Name

Amazon

Date Founded

2006

Company Location

United States

Company Website

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

Categories and Features

Deep Learning

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

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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