GPUs power AI training and inference. The accelerator, the provider and the scheduling model decide both performance and cost.
Kubernetes schedules GPUs as an extended resource such as nvidia.com/gpu, which a device plugin advertises on each node. Pods request whole GPUs.
Three things block cloud GPUs: quota (your account's permission, often 0), capacity (physical stock in a zone) and allocation to committed customers.
GPU memory for an LLM is weights (parameters × bytes per parameter) plus the KV cache for every token in flight, plus framework overhead.
Measure real GPU utilization, turn off idle GPUs, match each job to the smallest GPU that fits, share GPUs, then buy the right mix of on-demand and reserved.
MIG splits a GPU into isolated hardware slices, time slicing lets Pods take turns with no memory isolation, and MPS runs processes concurrently.
Run a model server such as vLLM in GPU Pods, route requests with a model-aware gateway, autoscale on queue depth and plan how weights load.
A neocloud is a cloud provider built around GPUs for AI. It sells GPU capacity, often cheaper than AWS, Azure or Google Cloud, with fewer services.
Buying GPUs pays off only at high sustained utilization over the hardware's useful life. Most teams rent, or own a steady baseline and rent the peaks.
EU-based clouds such as OVHcloud, Scaleway, IONOS and STACKIT, GPU clouds with European sites, Hetzner dedicated servers, and hyperscaler EU regions.
Create a managed Kubernetes cluster in minutes. Every new account gets €200 in credit, so you can explore the full platform with no charge until it's used or expires.