AI in Business
GPU Cloud in plain English.
Also known as: GPU rental,cloud GPU,GPU as a service
The one-sentence version
A service that rents graphics processors by the hour or second so you can train, fine-tune, or run AI models without buying hardware.
A GPU cloud rents the specialised chips AI models run on, usually billed per second or per hour, so a developer can spin up an H100 for an afternoon of fine-tuning and shut it down afterwards. The big clouds (AWS, Google Cloud, Azure) offer GPUs, but a wave of specialist providers such as RunPod, Lambda, and CoreWeave undercut them, often by half or more, and add conveniences like one-click templates for popular models. There are three common shapes: on-demand instances you manage yourself, spot or community instances that are cheaper but can be interrupted, and serverless endpoints where you upload a model and pay only while requests are being served. The trade-offs are availability (popular GPUs sell out), reliability on the cheapest tiers, and the fact that you are responsible for the software stack. For most people who just want to call a model, a hosted inference provider is simpler; a GPU cloud makes sense when you need to train, run custom or unusual models, or control costs at scale.