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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

Description

In the past, tasks such as deep learning, 3D modeling, simulations, distributed analytics, and molecular modeling could take several days or even weeks to complete. Thanks to GPUonCLOUD’s specialized GPU servers, these processes can now be accomplished in just a few hours. You can choose from a range of pre-configured systems or ready-to-use instances equipped with GPUs that support popular deep learning frameworks like TensorFlow, PyTorch, MXNet, and TensorRT, along with libraries such as the real-time computer vision library OpenCV, all of which enhance your AI/ML model-building journey. Among the diverse selection of GPUs available, certain servers are particularly well-suited for graphics-intensive tasks and multiplayer accelerated gaming experiences. Furthermore, instant jumpstart frameworks significantly boost the speed and flexibility of the AI/ML environment while ensuring effective and efficient management of the entire lifecycle. This advancement not only streamlines workflows but also empowers users to innovate at an unprecedented pace.

Description

A robust platform optimized for training is equipped with NVIDIA® H100 Tensor Core GPUs, offering competitive pricing and personalized support. Designed to handle extensive machine learning workloads, it allows for efficient multihost training across thousands of H100 GPUs interconnected via the latest InfiniBand network, achieving speeds of up to 3.2Tb/s per host. Users benefit from significant cost savings, with at least a 50% reduction in GPU compute expenses compared to leading public cloud services*, and additional savings are available through GPU reservations and bulk purchases. To facilitate a smooth transition, we promise dedicated engineering support that guarantees effective platform integration while optimizing your infrastructure and deploying Kubernetes. Our fully managed Kubernetes service streamlines the deployment, scaling, and management of machine learning frameworks, enabling multi-node GPU training with ease. Additionally, our Marketplace features a variety of machine learning libraries, applications, frameworks, and tools designed to enhance your model training experience. New users can take advantage of a complimentary one-month trial period, ensuring they can explore the platform's capabilities effortlessly. This combination of performance and support makes it an ideal choice for organizations looking to elevate their machine learning initiatives.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

AI SpendOps
MXNet
NVIDIA DGX Cloud Lepton
NVIDIA DGX Cloud Serverless Inference
Nebius Token Factory
PyTorch
Shadeform
TensorFlow

Integrations

AI SpendOps
MXNet
NVIDIA DGX Cloud Lepton
NVIDIA DGX Cloud Serverless Inference
Nebius Token Factory
PyTorch
Shadeform
TensorFlow

Pricing Details

$1 per hour
Free Trial
Free Version

Pricing Details

$2.66/hour
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

GPUonCLOUD

Country

India

Website

gpuoncloud.com/gpu-as-a-service/

Vendor Details

Company Name

Nebius

Founded

2022

Country

Netherlands

Website

nebius.ai/

Product Features

Data Preparation

Collaboration Tools
Data Access
Data Blending
Data Cleansing
Data Governance
Data Mashup
Data Modeling
Data Transformation
Machine Learning
Visual User Interface

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

Alternatives

Alternatives