It is a language model executed on own or dedicated infrastructure rather than necessarily being called through a public model API.
Local AI infrastructure
Install a local LLM: from model to usable business component
An LLM is not automatically a production system. Between model and value lie selection, resources, access, expectation management and integration into defined workflows.
Technical prerequisites
What is clarified before starting
- A business target process and quality criteria
- A suitable model format and sufficient memory
- Rules for permitted inputs and user access
Typical use cases
Where this architecture is used
- Summarising and structuring internal text
- Drafting, translation and knowledge dialogue
- Local API components for business applications
At a glance
Technical context without false precision
| Model selection | review language, context, task profile and licence |
|---|---|
| Execution | align CPU/GPU, memory and runtime |
| Use | clear approvals, guidance and human control |
Scope and limitations
What this solution intentionally does not promise
Local models can produce inaccurate or incomplete outputs. Subject-matter review and clear source handling remain necessary for relevant results.
FAQ
Frequently asked questions
Suitable models depend on language, task, licence, hardware and desired quality criteria. The selection is documented in the project.
Sources and technical context
These sources support technical context. They are not a project commitment and do not replace individual assessment.