Post-training KV-Cache compression methods typically either sample a subset of effectual tokens or quantize the data into lower numerical bit width. However, these methods cannot exploit redundancy in the hidden dimension of the KV tensors. This paper presents a hidden dimension compression approach called Palu, a KV-Cache compression framework that utilizes low-rank projection to reduce inference-time LLM memory usage. Palu decomposes the linear layers into low-rank matrices, caches compressed intermediate states, and reconstructs the full keys and values on the fly. To improve accuracy, compression rate, and efficiency, Palu further encompasses (1) a medium-grained low-rank decomposition scheme, (2) an efficient rank search algorithm, (3) low-rank-aware quantization compatibility enhancements, and (4) optimized GPU kernels with operators fusion. Extensive experiments with popular LLMs show that Palu compresses KV-Cache by 50% while maintaining strong accuracy and delivering up to 1.89x on the RoPE-based attention module. When combined with quantization, Palu's inherent quantization-friendly design yields small to negligible extra accuracy degradation while saving additional memory than quantization-only methods and achieving up to 2.91x speedup for the RoPE-based attention. Moreover, it maintains comparable or even better accuracy (up to 1.19 lower perplexity) compared to quantization-only methods. These results demonstrate Palu's superior capability to effectively address the efficiency and memory challenges of LLM inference posed by KV-Cache. Our code is publicly available at: https://github.com/shadowpa0327/Palu
Towards Economical Inference: Enabling DeepSeek's Multi-Head Latent Attention in Any Transformer-based LLMs
School of Computer Science, Fudan University（复旦大学计算机科学学院）；School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）；Institute of Modern Languages and Linguistics, Fudan University（复旦大学现代语言与语言学研究所）；Institute of Trustworthy Embodied Artificial Intelligence, Fudan University（复旦大学可信具身人工智能研究所）；Shanghai Collaborative Innovation Center of Intelligent Visual Computing（上海智能视觉计算协同创新中心）；Pengcheng Laboratory（鹏城实验室）；Hikvision Inc（海康威视公司）
ConCise: Training-Free Conclusion-Chain State Compression for Cost-Efficient Multi-Step RAG Services
National Natural Science Foundation of China（国家自然科学基金委员会）；Guangdong Higher Education Association（广东省高等教育协会）；Guangdong Provincial Higher Education Institutions（广东省高等教育机构）；Beijing Normal University at Zhuhai Education Reform Project（珠海市北京师范大学教育改革项目）
