The Feeding and Care of Large Language Models
As more organisations move from experimenting with LLMs to running them in production, a critical question emerges: how do you deploy and operate these models while maintaining control over your data, costs, and performance?
In this interactive webinar, we'll explore the practical realities of self-hosted LLM deployment—from the motivations driving enterprises toward sovereign AI to the day-to-day challenges of keeping inference pipelines healthy.
What we'll cover:
Why organisations choose self-hosting: data sovereignty, compliance, security, and the control it offers over quality, cost, and latency
The three core LLM pipelines: inference, RAG, and fine-tuning
The current landscape of inference options—from APIs to open-source platforms to bare-metal infrastructure
Setting up and operating a serving pipeline: what it takes to get models running and keep them that way
A look at Infratailors and how we're approaching deployment optimisation
Format: This is a discussion webinar, not a lecture. We'll pause for quick polls throughout and leave ample time for Q&A. Come ready to share your own experiences and challenges.
Whether you're still experimenting, running a pilot, or scaling production workloads, you'll leave with a clearer picture of what self-hosted LLM operations actually involves.
The webinar is hosted by Infratailors (www.infratailors.ai).