On-Premises LLM: Myth, Reality, and Conditions for Success
Deploying AI without compromising confidentiality

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IA2025 V1 Article

On-Premises LLM: Myth, Reality, and Conditions for Success
Deploying AI without compromising confidentiality

Author : Romain FAREL

Publication date: July 10, 2026 | Lire en français

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5. On-Premises LLM: Myth, Reality, and Conditions for Success

Objective: to debunk myths; to establish the factual conditions.

On-premise LLM has become, in some committees, the knee-jerk response to: “ChatGPT is banned.”

As if hosting the system in-house automatically solved all issues: security, sovereignty, and control. In practice, this oversimplification doesn’t hold up for long.

Example

A major industrial group launched an on-premise LLM project with great enthusiasm: budget approved, GPUs ordered, ambitious presentations. Twelve months later, the model was still running, but only for three pilot users, within a fixed scope, and without any real business value. Not because the technology didn’t work. But because technical feasibility had been confused with industrial sustainability....

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