AI Coding Summit

AI Coding Summit 2026

In generative AI, the largest large language models (LLMs) often dominate the headlines, hailed as the best solutions for the most complex and diverse tasks. While they certainly have their place, are they the best option for every enterprise use case?Smaller language models are gaining traction for their ability to deliver high performance with lower cost and resource requirements. These models are quicker, easier to fine-tune, and better suited for targeted business needs, making them an attractive alternative for many organizations. In this session, we will:-Explore the technical structure and content of LLMs.-Discuss how smaller, purpose-built models can be more efficient and effective for enterprise tasks, including how model optimization techniques can boost performance even more.-Demonstrate how smaller LLMs can provide faster, more cost-effective solutions while still meeting the demands of specialized use cases.

When Less Is More: A Technical Overview of LLMs and the Strength of Smaller Models

Join us for an interactive workshop where you'll train your first neural network using the classic MNIST handwritten digit dataset. In this hands-on session, participants will use OpenShift AI 3.x Workbenches to build and train a PyTorch model that recognizes handwritten digits from images.This beginner-friendly workshop provides a practical introduction to machine learning workflows in an enterprise-grade AI platform. Working directly in Jupyter notebooks, attendees will clone a ready-to-use GitHub repository, explore the MNIST dataset, and walk through the complete model training pipeline—from data preprocessing to model evaluation. The session uses CPU-optimized configurations, making it accessible without requiring specialized GPU resources.Whether you're new to AI/ML or curious about how the models you use actually function, this workshop offers practical, immediately applicable experience.Takeaways: Participants will leave with a working neural network, a complete training notebook they can reference, and foundational knowledge of deploying and utilizing ML workloads on OpenShift AI.

Legare Kerrison