On-premise AI runs inside your organization's own data center or office — not on a vendor's shared cloud.
On-premise AI (on-prem AI) deploys models and inference infrastructure on hardware your organization owns — servers in a rack, a private GPU cluster, or an air-gapped lab.
Teams choose it when data cannot leave the building: defense, finance, healthcare, and large enterprises with strict IT policies. NVIDIA DGX systems and HPE AI infrastructure are common hardware stacks for this model.
Compared to personal localized AI, on-prem is organizational scale — shared GPUs, IT-managed updates, and integration with internal apps. Vendors like VMware Private AI sell integrated stacks; open-source serving uses vLLM or Hugging Face Text Generation Inference (TGI) on Kubernetes.