Deployment

What a private LLM deployment means in practice, when it makes sense, and how to compare managed private inference, self-hosting, and hybrid architectures.

How to choose a hostable open-weight model based on task fit, hardware limits, governance needs, and support burden rather than hype.

Opening — Why this matters now The AI industry likes to pretend that training happens in neat, well-funded labs and deployment is merely the victory lap. Reality, as usual, is less tidy. Large language models are increasingly learning after release—absorbing their own successful outputs through user curation, web sharing, and subsequent fine‑tuning. This paper puts a sharp analytical frame around that uncomfortable truth: deployment itself is becoming a training regime. ...