Efficient Large Language Model Deployment: A Survey and Empirical Study

This comprehensive survey investigates various approaches for deploying large language models efficiently, focusing on reducing computational resources and energy consumption.

The research evaluates different deployment strategies including model compression, quantization, and hardware acceleration techniques, providing empirical evidence of their effectiveness.

The authors present a systematic comparison of deployment methods and their impact on model performance, latency, and energy usage.