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What is GPUonCLOUD?

Previously, completing tasks like deep learning, 3D modeling, simulations, distributed analytics, and molecular modeling could take days or even weeks. However, with GPUonCLOUD's specialized GPU servers, these tasks can now be finished in just a few hours. Users have the option to select from a variety of pre-configured systems or ready-to-use instances that come equipped with GPUs compatible with popular deep learning frameworks such as TensorFlow, PyTorch, MXNet, and TensorRT, as well as libraries like OpenCV for real-time computer vision, all of which enhance the AI/ML model-building process. Among the broad range of GPUs offered, some servers excel particularly in handling graphics-intensive applications and multiplayer gaming experiences. Moreover, the introduction of instant jumpstart frameworks significantly accelerates the AI/ML environment's speed and adaptability while ensuring comprehensive management of the entire lifecycle. This remarkable progression not only enhances workflow efficiency but also allows users to push the boundaries of innovation more rapidly than ever before. As a result, both beginners and seasoned professionals can harness the power of advanced technology to achieve their goals with remarkable ease.

What is GPUBank?

GPUBank operates as a cloud-based service that allows developers, machine-learning professionals, and research teams to lease GPU resources, thereby improving their AI and GPU-heavy tasks. The user-friendly web console enables users to choose between NVIDIA or AMD GPU instances, select a preferred deployment region, and launch pre-set configurations for diverse applications such as model training, inference, fine-tuning, image generation, and framework development. With an array of templates like PyTorch, vLLM, ComfyUI, and Ollama, GPUBank caters to varying project requirements effectively. Furthermore, the platform enhances usability by offering an API, a command-line interface, a Python SDK, and a Terraform provider to streamline resource management and provisioning. GPU compute pricing is billed by the second and includes charges for the CPU, memory, local NVMe storage, public IPv4, and network traffic associated with the selected instance's hourly rate, while additional fees apply for persistent disks. Users can choose from on-demand, interruptible, or reserved capacities across multiple data center locations, allowing them to customize their resource allocation based on specific project needs. This flexibility not only ensures efficiency but also positions GPUBank as an attractive solution for those aiming to manage their computational expenses strategically. As a result, it significantly enhances the overall productivity and effectiveness of various computational projects.

Media

Media

No images available

Integrations Supported

MXNet
PyTorch
TensorFlow

Integrations Supported

Terraform

API Availability

API Availability

Has API

Pricing Information

$1 per hour

Pricing Information

$0.23/GPU/hour
On-demand RTX 4090 compute starts at $0.23 per GPU-hour, billed by the second with no minimum compute duration. Prices are in USD and exclude VAT. Customers add funds to a prepaid balance using cryptocurrency, with a $25 minimum top-up. Compute price includes vCPU, RAM, local NVMe, one public IPv4 address, and traffic. Persistent disk is $0.03 per GB-month. Interruptible and reserved rates are available at discounts from on-demand pricing.

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

GPUonCLOUD

Company Location

India

Company Website

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

Company Facts

Organization Name

GPU Compute

Date Founded

2026

Company Location

Estonia

Company Website

gpubank.io

Categories and Features

AI Cloud Providers

Not specified

AI Infrastructure

Not specified

Cloud GPU

Not specified

Categories and Features

Cloud GPU

Not specified

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