MLKV: Multi-Layer Key-Value Heads for Memory Efficient Transformer Decoding (2025.findings-naacl)
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| Challenge: | Multi-Layer Key-Value (MLKV) sharing reduces memory usage by 6x compared to Multi-Query Attention and Grouped-Query Attributes. |
| Approach: | They propose a novel approach that extends KV sharing across transformer layers to reduce memory usage beyond what was possible with Multi-Query Attention and Grouped-Query Attributes. |
| Outcome: | The proposed approach reduces KV cache size by 6x with minimal performance loss and scales linearly with model size, batch size, and sequence length. |
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| Challenge: | Existing methods to reduce memory usage for large language models neglect inter-layer dependency between layers and huge memory consumption in pre-computation. |
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MadaKV: Adaptive Modality-Perception KV Cache Eviction for Efficient Multimodal Long-Context Inference (2025.acl-long)
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Kunxi Li, Zhonghua Jiang, Zhouzhou Shen, ZhaodeWang ZhaodeWang, Chengfei Lv, Shengyu Zhang, Fan Wu, Fei Wu
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