OSS + ELC EU 2026

Large Language Models often dominate headlines, but many real world AI applications don't need billions of parameters to be effective. Tasks such as classification, information extraction, intent detection, summarization and domain specific assistants can often be served effectively by smaller open source models with lower latency, reduced infrastructure requirements and improved privacy.

This session introduces Small Language Models (SLMs), explores the trade-offs between model size, accuracy, cost and performance and discusses techniques such as quantization, distillation and domain adaptation that make these models practical for deployment on laptops and edge devices.

Attendees will leave with a framework for selecting the right model for a given use case, understanding why bigger is not always better and how open-source SLMs are making AI more accessible to developers and organizations with limited compute resources.