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.
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.
- Score: 0.0
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- 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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