It is infrastructure that provides selected models to a defined group of users or applications in an organisation.
Local AI infrastructure
Your own AI server: infrastructure aligned with your operations
An own AI server can be useful when data paths, operations and integration should be designed deliberately. We begin with workload, user count and protection needs rather than a blanket hardware recommendation.
Technical prerequisites
What is clarified before starting
- A clear use case and expected concurrency
- Ownership for operating system, network and patching
- Capacity headroom for models, context and future use
Typical use cases
Where this architecture is used
- Internal knowledge assistance
- Secure prototypes close to existing business systems
- Local APIs for defined processes
At a glance
Technical context without false precision
| Pilot | defined user group and one or a few use cases |
|---|---|
| Team | multi-user operation with agreed access paths |
| Scale | expandable GPU, storage and operating architecture |
Scope and limitations
What this solution intentionally does not promise
A server is not an isolated security promise. Segmentation, access, patching, backup and monitoring are parts of a shared operating concept.
FAQ
Frequently asked questions
Depending on the design, local model requests can operate without a public model API. Updates, procurement or external integrations may still need connectivity.
Sources and technical context
These sources support technical context. They are not a project commitment and do not replace individual assessment.