Self-Hosted LLMs: From Zero to Inference
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Cedric Clyburn
Red Hat
Senior Developer Advocate
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Roberto Carratalá
Red Hat
Principal AI Architect
Over the past few months, I’ve had the chance to work on a research and development project exploring the feasibility of adopting solutions powered by Small and Large Language Models—with one unique twist: inference had to be performed at the edge.
This journey led me to scratch the surface of the vast, fascinating, and rapidly evolving world of LLMs, with a particular focus on inference servers. Along the way, I encountered a fair share of head-scratching moments and valuable insights that I’m excited to share with you.
In this talk, we’ll cover the key concepts behind LLM inference, untangle the tricky jargon, and give you a glimpse into the primary tools and solutions you can leverage if you ever need or want to explore LLM local inference.
World Congress 2025
Cedric Clyburn
Red Hat
Senior Developer Advocate
Roberto Carratalá
Red Hat
Principal AI Architect
Emanuele Fabbiani
xtream
Head of AI
Tomislav Tipurić
Nephos
CTO
Harshita Seth
NVIDIA
Senior Solution Architect
Lavinia Ghita
NVIDIA
Solution Architect
Sergio Perez
NVIDIA
Solution Architect
Ziv Ilan
NVIDIA
Solution Architect GenAI