Unverified50% confidenceFactExact time
DeepSeek-V2提出MLA（Multi-head Latent Attention），通过低秩压缩将KV Cache压缩至MHA的5%左右
1
Sources
50%
Confidence
Long-term
Relevance
7/15/2026
First Seen
Sources
Related Claims
VerifiedMLA (Multi-head Latent Attention) was first proposed by the DeepSeek team in DeepSeek-V2, and significantly reduces KV Cache memory usage by compressing Key-Value pairs into a low-dimensional latent space.83% similarUnverifiedDeepSeek's innovations in Multi-head Latent Attention (MLA) and FP8 mixed-precision training compress KV Cache memory usage to 5%–13% of standard multi-head attention.79% similarVerifiedDeepSeek V4 adopts a proprietary hierarchical compressed attention strategy, featuring two alternating attention modes: CSA (Compressed Sink Attention) and HCA (Heavy Compressed Attention).79% similarUnverifiedDeepSeek-V2引入Multi-Head Latent Attention（MLA）机制和DeepSeekMoE架构，其每百万token推理成本约为同期GPT-4级别模型的百分之一74% similar
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