Last updated · Fusionative
Short answer
GPU as a service (GPUaaS) is the on-demand rental of GPU compute over the network, so customers can train and run AI models without buying hardware. Providers sell whole GPU servers, single GPUs, fractions of a GPU or inference endpoints, priced by the hour, by reservation or per token.
Common GPUaaS pricing models
- On-demand GPU-hour: pay per hour of a GPU or GPU server, with no commitment.
- Reserved capacity: a lower rate in return for a term commitment, typical for training clusters.
- Spot or preemptible: discounted capacity that can be reclaimed.
- Per-token or per-request inference: pay for model output rather than hardware.
What customers expect
Customers expect what they get from a public cloud: sign up, pick a GPU type, launch a VM, container or Kubernetes cluster in minutes, see usage and spend, and stay isolated from other tenants. Enterprise and public-sector buyers add data residency, private networking and audit trails.
What an operator needs to launch GPUaaS
- GPU capacity with drivers, fabric and storage that are maintained and monitored.
- Tenant isolation at the cluster, namespace, VM or hardware level.
- A self-service portal and API with a catalog of GPU flavors and AI services.
- Quotas, usage metering, pricing and billing or chargeback.
- Observability for GPU health, utilization and service levels.
Where FusioNative fits
FusioNative turns GPU servers and Kubernetes clusters an operator already owns into GPUaaS: tenants, projects, a self-service catalog, quotas and metering, plus model serving with vLLM, SGLang, NVIDIA NIM and Ollama, on NVIDIA and AMD accelerators.