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arXiv · 2503.11108

Time and Memory Trade-off of KV-Cache Compression in Tensor Transformer Decoding

Yifang Chen·Xiaoyu Li·Yingyu Liang·Zhenmei Shi·Zhao Song·Yu Tian
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Abstract

The key-value (KV) cache in the tensor version of transformers presents a significant bottleneck during inference. While previous work analyzes the fundamental space complexity barriers in standard attention mechanisms [Haris and Onak, 2025], our work generalizes the space complexity barriers result to tensor attention version. Our theoretical contributions rely on a reduction from communication complexity and deduce the memory lower bound for tensor-structured attention mechanisms when $d = \Omega(\log n)$. Furthermore, we introduce two types of tensor attention cache and present a trade-off between time and memory for two scenarios. Overall, our work provides a theoretical foundation for us to understand the time-memory tradeoff of KV-Cache compression in tensor attention decoding and offers more perspectives in developing more memory-efficient tensor attention Transformer architectures.

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Yifang Chen, Xiaoyu Li, Yingyu Liang, Zhenmei Shi, Zhao Song, Yu Tian. 2025-03-14. Time and Memory Trade-off of KV-Cache Compression in Tensor Transformer Decoding. https://arxiv.org/abs/2503.11108

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