Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models

• URL: http://arxiv.org/abs/2502.00046v1
• Date: Thu, 16 Jan 2025 08:54:44 GMT
• Title: Optimization Strategies for Enhancing Resource Efficiency in Transformers & Large Language Models
• Authors: Tom Wallace, Naser Ezzati-Jivan, Beatrice Ombuki-Berman,
• Abstract summary: This study explores optimization techniques, including Quantization, Knowledge Distillation, and Pruning.<n>4-bit Quantization significantly reduces energy use with minimal accuracy loss.<n>Hybrid approaches, like NVIDIA's Minitron approach combining KD and Structured Pruning, further demonstrate promising trade-offs between size reduction and accuracy retention.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Advancements in Natural Language Processing are heavily reliant on the Transformer architecture, whose improvements come at substantial resource costs due to ever-growing model sizes. This study explores optimization techniques, including Quantization, Knowledge Distillation, and Pruning, focusing on energy and computational efficiency while retaining performance. Among standalone methods, 4-bit Quantization significantly reduces energy use with minimal accuracy loss. Hybrid approaches, like NVIDIA's Minitron approach combining KD and Structured Pruning, further demonstrate promising trade-offs between size reduction and accuracy retention. A novel optimization equation is introduced, offering a flexible framework for comparing various methods. Through the investigation of these compression methods, we provide valuable insights for developing more sustainable and efficient LLMs, shining a light on the often-ignored concern of energy efficiency.

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