Do not deploy a model without a framework
The topic is not only model choice. Documents, users, prompts, logs, expected performance and production maintenance must be framed.
Local inference on AMD/NVIDIA GPUs
Local LLM enterprise architecture for Europe, UK, US and Canada
Unix Consulting helps frame local LLM architecture, data, performance, AMD/NVIDIA GPUs, permissions and limits for internal AI assistants in France, Europe, the UK, US, Canada and international markets.
Technical points
- Architecture choice: workstation, server, AMD/NVIDIA GPU, private cloud, cloudless LLM or isolated environment.
- Document management, RAG, segmentation, permissions, logs and traceability.
- Monitoring, backups, updates, tests and usage rules.
Goal
- Create a useful assistant without exposing sensitive information.
- Keep an architecture maintainable by IT.
- Retain evidence and understandable rules for audit contexts.
Does local inference on AMD/NVIDIA GPUs prevent all data leakage?
No. It reduces some external exposure risks, but permissions, documents, logs and usage still need governance.
Are GPUs mandatory?
It depends on models, volume, expected latency and number of users. Scoping avoids unnecessary oversizing.
Can local GPU inference connect to internal documents?
Yes, with document governance, access rights and suitable traceability.