Hosting your own Large Language Model (LLM) stack isn’t just possible—it’s a game-changer for businesses handling sensitive data. But is it worth the effort?

In this talk, we’ll demystify the process, from racking GPU servers to deploying open-weight models in production, and explore why enterprises are opting for private AI over cloud-based solutions.Drawing from real-world implementation at TNG Technology Consulting, we’ll walk through the full lifecycle of a self-hosted LLM infrastructure:

- Hardware & Deployment: Practical insights into GPU selection, Kubernetes orchestration, and scaling for performance.

- Security & Privacy: Architecting a resilient, zero-trust pipeline for confidential data.

- Open Models: Strategies to integrate cutting-edge models without sacrificing reliability.

- Proven Use Cases: See how private LLMs accelerate coding, knowledge management, and decision-making in regulated industries.Attendees will leave with actionable best practices, a reference architecture, and a clear roadmap for balancing cost, control, and innovation.

Whether you’re a DevOps engineer, CTO, or AI enthusiast, this talk will challenge assumptions about AI accessibility and inspire you to rethink how LLMs can—and should—be deployed.