Navigating LLM Deployment: Tips, tricks, and techniques

Unlock best practices for deploying self-hosted LLMs—optimize performance, ensure reliability, and tackle real-world challenges in critical industries

By Meryem Arik

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June 25, 2025

Self-hosted Language Models are going to power the next generation of applications in critical industries like financial services, healthcare, and defense. Self-hosting LLMs, as opposed to using API-based models, comes with its own host of challenges – as well as needing to solve business problems, engineers need to wrestle with the intricacies of model inference, deployment and infrastructure. In this talk we are going to discuss the best practices in model optimisation, serving and monitoring – with practical tips and real case-studies.

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About the author

Meryem Arik

Meryem co-founded TitanML with the vision of creating a seamless and secure infrastructure for enterprise LLM deployments. Beyond her contributions to TitanML, Meryem is dedicated to sharing her insights on the practical and ethical adoption of AI in enterprise.