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arXiv · 2412.10856

RWKV-edge: Deeply Compressed RWKV for Resource-Constrained Devices

Wonkyo Choe·Yangfeng Ji·Felix Xiaozhu Lin
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Abstract

To deploy LLMs on resource-contained platforms such as mobile robots and smartphones, non-transformers LLMs have achieved major breakthroughs. Recently, a novel RNN-based LLM family, Repentance Weighted Key Value (RWKV) has shown strong computational efficiency; nevertheless, RWKV models still have high parameter counts which limited their deployment. In this paper, we propose a suite of compression techniques, ranging from model architecture optimizations to post-training compression, tailored to the RWKV architecture. Combined, our techniques reduce the memory footprint of RWKV models by 3.4x -- 5x with only negligible degradation in accuracy; compared to transformer LLMs with similar accuracy, our models require 4x less memory footprint.

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Wonkyo Choe, Yangfeng Ji, Felix Xiaozhu Lin. 2025-10-28. RWKV-edge: Deeply Compressed RWKV for Resource-Constrained Devices. https://arxiv.org/abs/2412.10856

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