# What is on-premise AI?

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.



## Sources
1. [NVIDIA — DGX systems](https://www.nvidia.com/en-us/data-center/dgx-systems/)
2. [HPE — AI solutions](https://www.hpe.com/us/en/solutions/ai.html)
3. [VMware — What is private AI?](https://www.vmware.com/topics/glossary/content/private-ai.html)
4. [vLLM (GitHub)](https://github.com/vllm-project/vllm)
5. [Hugging Face — Text Generation Inference](https://github.com/huggingface/text-generation-inference)

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