Peking University

This comprehensive survey examines various approaches to make the training of large language models more efficient and environmentally sustainable. The research analyzes different techniques including model compression, efficient attention mechanisms, and hardware-aware training strategies that can significantly reduce the computational and energy costs. The authors provide a systematic comparison of different efficiency methods and their impact on model performance, training time, and energy consumption.

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.