Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Amazon EC2 G4 instances are specifically designed to enhance the performance of machine learning inference and applications that require high graphics capabilities. Users can select between NVIDIA T4 GPUs (G4dn) and AMD Radeon Pro V520 GPUs (G4ad) according to their requirements. The G4dn instances combine NVIDIA T4 GPUs with bespoke Intel Cascade Lake CPUs, ensuring an optimal mix of computational power, memory, and networking bandwidth. These instances are well-suited for tasks such as deploying machine learning models, video transcoding, game streaming, and rendering graphics. On the other hand, G4ad instances, equipped with AMD Radeon Pro V520 GPUs and 2nd-generation AMD EPYC processors, offer a budget-friendly option for handling graphics-intensive workloads. Both instance types utilize Amazon Elastic Inference, which permits users to add economical GPU-powered inference acceleration to Amazon EC2, thereby lowering costs associated with deep learning inference. They come in a range of sizes tailored to meet diverse performance demands and seamlessly integrate with various AWS services, including Amazon SageMaker, Amazon ECS, and Amazon EKS. Additionally, this versatility makes G4 instances an attractive choice for organizations looking to leverage cloud-based machine learning and graphics processing capabilities.
Description
Utilize Spot Virtual Machines to access excess computing power at substantially reduced prices. This approach is ideal for deploying workloads that can withstand interruptions and are not bound by strict time constraints. You can efficiently run tasks related to development, testing, quality assurance, advanced analytics, big data, machine learning, AI, as well as batch processing, video rendering, and image transcoding—all while keeping costs remarkably low. It's important to focus on applications that are resilient to disruptions and can operate without a defined completion timeline when utilizing Spot VMs. As Azure faces a shortage of available computing resources, it's crucial to prioritize high-performance computing tasks, batch processing operations, or visual rendering projects to make the most of the resources at hand. Adopting this strategy can lead to significant cost efficiencies while still fulfilling diverse computing needs.
API Access
Has API
API Access
Has API
Integrations
AMD Radeon ProRender
Amazon EC2
Amazon EKS
Amazon Elastic Inference
Amazon SageMaker
Amazon Web Services (AWS)
Azure Linux
Azure Marketplace
CUDA
Microsoft Azure
Integrations
AMD Radeon ProRender
Amazon EC2
Amazon EKS
Amazon Elastic Inference
Amazon SageMaker
Amazon Web Services (AWS)
Azure Linux
Azure Marketplace
CUDA
Microsoft Azure
Pricing Details
No price information available.
Free Trial
Free Version
Pricing Details
No price information available.
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/g4/
Vendor Details
Company Name
Microsoft
Founded
1975
Country
United States
Website
azure.microsoft.com/en-us/products/virtual-machines/spot
Product Features
Deep Learning
Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization
Product Features
Virtual Machine
Backup Management
Graphical User Interface
Remote Control
VDI
Virtual Machine Encryption
Virtual Machine Migration
Virtual Machine Monitoring
Virtual Server