On-Premise AI
AI deployed on hardware the client owns and operates inside their own data centre or office facility, with no dependency on external cloud or model providers for inference.
How it works
On-premise AI takes private AI one step further: the inference hardware itself is owned and operated by the client. Common drivers are data residency requirements that prohibit any external cloud, ITAR or defence-adjacent restrictions, NHS Trust environments where the workload must run inside the Trust's network, and operational continuity requirements that cannot tolerate dependency on an external provider. Hardware sizing depends on workload: a single H100 or L40S handles small-team RAG and document-processing workloads; multi-GPU clusters are required for larger inference throughput. Ayoob AI ships on-premise AI for NHS Trusts, dental practices, defence-adjacent firms, and manufacturers under OEM data-handling restrictions.
Related terms
Private AI
AI deployed on infrastructure the client controls (on-premise, in the client's cloud tenancy, or air-gapped), with no third-party LLM provider in the data path and no inference-time data export.
Air-Gapped AI
AI deployed inside a network that has no connection to the public internet, used for the most security-sensitive workloads where any external connectivity is prohibited.
Data Residency
The geographic location where data is stored and processed, with regulatory requirements (UK GDPR, sector-specific rules) often constraining where personal or regulated data can travel.
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