Model Compression Techniques for Efficient LLM Deployment

The rapid advancements in large language models (LLMs) have unlocked transformative potential across industries, with state-of-the-art models excelling in popular AI reasoning benchmarks. However, their practical application often presents challenges due to their significant size, high computational costs, deployment complexities, and latency issues. To address these limitations, model compression techniques have emerged as effective solutions to reduce model size and computational requirements while preserving core capabilities.

This tutorial will provide a comprehensive overview of key techniques, including quantization, pruning, and knowledge distillation. Participants will gain an understanding of the theoretical foundations behind each method, explore their use cases, and engage in practical, hands-on examples to solidify their learning.