Evaluating Open-Source Local LLMs: A Comparative Analysis with Proprietary Giants for Developer Applications
Conference (INTERMEDIATE level)
Room 7
This session will evaluate open-source local large language models (LLMs) and compare their performance, flexibility, and applicability to large proprietary foundational models. We will explore how model size impacts performance, the feasibility of fine-tuning models for specific projects or coding languages, and the advantages of using local open-source models over their commercial counterparts. Attendees will gain insights into the practical considerations for selecting and deploying LLMs in development environments, focusing on data privacy, cost-effectiveness, and customization.
Michael Lundsveen
Østfold University College
Michael is a senior engineer, researcher, and lecturer on generative AI at Østfold University College. Teaching a wide range of topics like Generative AI, Coding, Autonomous robotics, and digital fabrication. In recent years, he has focused mostly on GenAI, looking at tools, base models, and the technology's effects on society, and is investigating how technologies like large language models can be utilized in different sectors and fields.