Unverified50% confidenceFactExact time
DeepSeek的前缀缓存实现基于MLA（Multi-head Latent Attention）架构，MLA通过低秩压缩将KV Cache的存储需求降低了数十倍
1
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50%
Confidence
Long-term
Relevance
8/26/2026
First Seen
Sources
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Verified多头潜在注意力机制（MLA）通过将KV投影到低维潜在空间压缩存储，将KV Cache显存占用降低至传统方法的5%至13%78% similarUnverifiedDeepSeek V4通过MLA等压缩技术将KV Cache的显存占用压缩到前代的10%以下76% similarVerifiedMLA将KV对压缩到低维潜在空间中进行缓存，推理时通过上投影矩阵恢复完整的Key和Value，将KV缓存压缩了数倍72% similarUnverifiedDeepSeek V4的DSA（吸收注意力/Differential Sparse Attention）机制在百万上下文场景下，计算量降至V3.2的27%，KV缓存降至10%71% similarUnverified量化KV Cache（压缩为INT8或INT4）可将缓存内存降低50-75%，但主流推理框架支持成熟度参差不齐71% similar
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