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What is DataCrunch?

Boasting up to 8 NVidia® H100 80GB GPUs, each outfitted with 16,896 CUDA cores and 528 Tensor Cores, this setup exemplifies NVidia®'s cutting-edge technology, establishing a new benchmark for AI capabilities. The system is powered by the SXM5 NVLINK module, which delivers a remarkable memory bandwidth of 2.6 Gbps while facilitating peer-to-peer bandwidth of as much as 900GB/s. Additionally, the fourth generation AMD Genoa processors support a maximum of 384 threads, achieving a turbo clock speed of 3.7GHz. For NVLINK connectivity, the system makes use of the SXM4 module, which provides a staggering memory bandwidth that surpasses 2TB/s and offers P2P bandwidth of up to 600GB/s. The second generation AMD EPYC Rome processors are capable of managing up to 192 threads and feature a boost clock speed of 3.3GHz. The designation 8A100.176V signifies the inclusion of 8 RTX A100 GPUs, along with 176 CPU core threads and virtualization capabilities. Interestingly, while it contains fewer tensor cores than the V100, the architecture is designed to yield superior processing speeds for tensor computations. Furthermore, the second generation AMD EPYC Rome also comes in configurations that support up to 96 threads with a boost clock reaching 3.35GHz, thus further amplifying the system's overall performance. This impressive amalgamation of advanced hardware guarantees maximum efficiency for even the most demanding computational workloads. Ultimately, such a robust setup is essential for organizations seeking to push the boundaries of AI and machine learning tasks.

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
WithoutBG

Integrations Supported

AWS Parallel Computing Service
Amazon EC2 Inf1 Instances
Amazon EC2 Trn1 Instances
WithoutBG

API Availability

Has API

API Availability

Has API

Pricing Information

$3.01 per hour
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

DataCrunch

Company Location

Finland

Company Website

datacrunch.io

Company Facts

Organization Name

Amazon

Date Founded

2006

Company Location

United States

Company Website

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

Categories and Features

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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