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MoEZip: Routing-Aware KV Cache Compression for Sparse Mixture-of-Experts LLMs
Mixture-of-Experts (MoE) language models scale model capacity efficiently by activating a small subset of parameters per token. However, in long-context inference, memory remains a major bottleneck because the KV cache grows linearly with sequence length. Existing KV cache compression methods reduce
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