> Markdown version of [/events/world-congress-2026-europe/sessions/1286-compress-cut-and](https://www.wearedevelopers.com/events/world-congress-2026-europe/sessions/1286-compress-cut-and). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Compress, Cut, and Distill: The Latest Gen AI Model Compression Techniques in Practice - **Date:** Friday, Jul 10, 2026 - **Time:** 12:15–14:15 (120 min) - **Room:** Room M2 (40 Seats) - **Event:** World Congress 2026 Europe ## Description This training lab explores the art and engineering of compressing large language models to make them cheaper, faster, and easier to deploy while preserving practical capability. Designed for a broad audience that spans beginners to advanced practitioners, the workshop will introduce foundational concepts for newcomers, share implementation patterns and pitfalls for experienced engineers, and highlight cutting-edge research directions for specialists. Workshop Preparation: - Please bring your own laptop. - Please review the following document and prepare accordingly before the workshop: <https://developer.nvidia.com/dli/getready> ## Speaker ### [Sergio Perez](https://www.wearedevelopers.com/@sergio-perez) Senior Solution Architect at NVIDIA ## Related talks at this congress - [Smaller Voice Models](https://www.wearedevelopers.com/events/world-congress-2026-europe/sessions/1467-smaller-voice-models) — Sohaib Ahmad - [Fine-Tuning Small Language Models for Agentic AI](https://www.wearedevelopers.com/events/world-congress-2026-europe/sessions/1389-fine-tuning-small) — Björn Buchhold - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/events/world-congress-2026-europe/sessions/1094-tour-de-force-open) — Christin Pohl - [Teaching AI to Code in Every Language with NVIDIA NeMo](https://www.wearedevelopers.com/events/world-congress-2026-europe/sessions/994-teaching-ai-to-code) — Antonio Rueda-Toicen, Marco Gullotto DE, Nikita Pavlichenko