GPU Cloud

Renting graphics cards (GPUs) in the cloud, billed by the hour or second of use. It is how teams train and run AI models without buying hardware that costs tens of thousands of dollars.

GPU cloud is the service of renting GPU (graphics card) processing power over the internet. GPUs are the standard hardware of modern AI because they execute thousands of mathematical operations in parallel: exactly what training and running neural networks requires. Since high-end AI cards are expensive and become obsolete quickly, renting by the hour almost always beats buying.

The typical uses are:

Training and fine-tuning: adapting a model with your data, using the GPU only for the hours of the training run.
Inference: hosting a model (image, voice, or language) that serves user requests.
Rendering and research: occasional heavy workloads that do not justify owning hardware.

The market has two supplier profiles: general clouds (AWS, Google Cloud, Azure) and GPU-specialized providers (such as RunPod, Vast.ai, and Salad), which usually offer lower hourly prices, machine marketplaces, and ready-made templates for AI frameworks. Many offer serverless GPU: you pay only for the seconds the model is actually processing.

Concrete example: a startup wants to fine-tune an open-source image model. Instead of buying a workstation with a professional GPU, it rents a machine with an 80 GB GPU for a few hours, trains, downloads the resulting weights, and shuts the instance down, paying only for the time used.