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arXiv · 2505.17331

ECHO-LLaMA: Efficient Caching for High-Performance LLaMA Training

Maryam Dialameh·Rezaul Karim·Hossein Rajabzadeh·Omar Mohamed Awad·Hyock Ju Kwon·Boxing Chen·Walid Ahmed·Yang Liu
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Abstract

This paper introduces ECHO-LLaMA, an efficient LLaMA architecture designed to improve both the training speed and inference throughput of LLaMA architectures while maintaining its learning capacity. ECHO-LLaMA transforms LLaMA models into shared KV caching across certain layers, significantly reducing KV computational complexity while maintaining or improving language performance. Experimental results demonstrate that ECHO-LLaMA achieves up to 77\% higher token-per-second throughput during training, up to 16\% higher Model FLOPs Utilization (MFU), and up to 14\% lower loss when trained on an equal number of tokens. Furthermore, on the 1.1B model, ECHO-LLaMA delivers approximately 7\% higher test-time throughput compared to the baseline. By introducing a computationally efficient adaptation mechanism, ECHO-LLaMA offers a scalable and cost-effective solution for pretraining and finetuning large language models, enabling faster and more resource-efficient training without compromising performance.

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Maryam Dialameh, Rezaul Karim, Hossein Rajabzadeh, Omar Mohamed Awad, Hyock Ju Kwon, Boxing Chen, Walid Ahmed, Yang Liu. 2025-05-22. ECHO-LLaMA: Efficient Caching for High-Performance LLaMA Training. https://arxiv.org/abs/2505.17331

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