GPUBank Description
GPUBank serves as a cloud-based platform where developers, machine-learning experts, and research groups can rent GPU resources to enhance their AI and GPU-intensive workloads. Users can easily navigate the web console to select either NVIDIA or AMD GPU instances, pick a deployment location, and initiate pre-configured setups for a variety of purposes, including model training, inference, fine-tuning, image generation, and framework development. The platform offers a range of templates, such as PyTorch, vLLM, ComfyUI, and Ollama, catering to diverse project needs. Additionally, GPUBank provides an API, a command-line interface, a Python SDK, and a Terraform provider to facilitate easy provisioning and management of usage. Pricing for GPU compute is calculated per second and encompasses the costs for the chosen instance's CPU, memory, local NVMe storage, public IPv4, and network traffic as part of the hourly rate; however, persistent disks incur additional charges. Customers have the flexibility to opt for on-demand, interruptible, or reserved capacity across various data center locations, ensuring they can tailor their resources to fit specific project requirements effectively. This versatility makes GPUBank an appealing choice for those looking to optimize their computational needs in a cost-effective manner.
GPUBank Alternatives
Hivenet Compute gives you on-demand RTX 5090 and RTX Pro 6000 GPUs, plus vCPU instances, billed per second while they run. Hivenet owns and operates the infrastructure, with data centres in France, the UAE and the US, so EU teams can keep workloads and data in Europe.
Use it for training and fine-tuning, inference, notebooks, rendering and batch jobs. Launch from ready-made templates in a few minutes, or automate everything through the public Compute API. If you'd rather not run the serving layer yourself, the Inference API gives you an OpenAI-compatible endpoint.
Teams get organisations with role-based access, prepaid credits, and S3-compatible object storage alongside compute.
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IONOS offers GPU Servers that deliver a high-performance computing framework aimed at managing tasks that demand significantly more power than standard CPU systems can provide. This infrastructure features top-tier NVIDIA GPUs, including the H100, H200, and L40s, in addition to specialized AI accelerators like Intel Gaudi, facilitating extensive parallel processing for demanding applications. By utilizing GPU-accelerated instances, the cloud infrastructure is enhanced with dedicated graphical processors, enabling virtual machines to execute intricate calculations and handle data-heavy tasks at a much faster rate compared to traditional servers. This solution is especially well-suited for fields such as artificial intelligence, deep learning, and data science, where training models on extensive datasets or executing rapid inference processes is necessary. Furthermore, it accommodates big data analytics, scientific simulations, and visualization tasks, including 3D rendering or modeling, that necessitate substantial computational capacity. As a result, organizations seeking to optimize their processing capabilities for complex workloads can greatly benefit from this advanced infrastructure.
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Trooper.AI
Trooper.AI offers dedicated GPU servers designed for people who need real control over their AI workloads. Each server is a fully private, bare-metal machine — no shared GPUs, no noisy neighbors, no abstraction layers. You get full root access and a system that behaves like your own hardware, just without the upfront investment.
Servers are provisioned within minutes and can be equipped with ready-made AI environments at the click of a button. This includes popular tools for language models, image generation, data science, automation, and full Linux desktop workflows. Everything runs directly on the machine, with persistent storage and no forced containerization or platform lock-in.
Trooper.AI operates exclusively from European data centers and is run from Germany, ensuring compliance with GDPR and the EU AI Act. This makes the platform especially suitable for developers, startups, and businesses that care about data sovereignty and regulatory clarity.
The hardware portfolio ranges from affordable GPUs for experimentation to high-end systems for serious training and inference. Fast NVMe storage, automated backups, public access with SSL, and a simple web interface and API are included by default.
A key differentiator is sustainability: Trooper.AI relies on professionally refurbished high-end hardware, extending the lifecycle of powerful components while reducing electronic waste. Usage-based pricing with pause and freeze options allows tight cost control.
Trooper.AI positions itself as a small, focused European alternative to hyperscale clouds — built for users who want performance, transparency, and ownership over their AI infrastructure.
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Hinode
Hinode serves as a cloud workspace designed specifically for creators working with AI-generated images and videos. This platform efficiently manages your files, applications, and provides a shared directory for models and datasets, ensuring continuity between sessions. When extensive GPU resources are required for computations, you can link a powerful machine such as an NVIDIA L4 with 24 GB of VRAM, L40S with 48 GB, or the RTX PRO 6000 boasting 96 GB, all accompanied by RAM options ranging from 32 to 256 GB. Upon launching, these machines automatically load your previously saved workspace, which means all the necessary tools are readily available for your use.
Accessing your projects is seamless through a streamed Ubuntu desktop available in the Hinode app for Mac, Windows, and Linux, or via Chrome and Edge without needing to install anything. Additionally, it supports SSH connections through the hinode command-line tool, allows access via VS Code Remote-SSH, and connects through an MCP interface for Claude and other assistants. Installing applications like ComfyUI, PyTorch, JupyterLab, Ollama, vLLM, AI Toolkit, Axolotl, and several others is a simple one-click process, enhancing the overall user experience and productivity. Furthermore, the platform continually evolves, incorporating new tools and features to meet the demands of its users.
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Pricing
Pricing Starts At:
$0.23/GPU/hour
Pricing Information:
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.
Company Details
Company:
GPU Compute
Year Founded:
2026
Headquarters:
Estonia
Website:
gpubank.io
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Host LLMs in Production With On-Demand GPUs
Deploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
Product Details
Platforms
Web-Based
Types of Training
Training Docs
Customer Support
Online Support
GPUBank Features and Options
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