Unverified50% confidenceFactExact time
GQA和MLA等技术可显著压缩KV缓存大小，DeepSeek V3采用了MLA来降低长上下文场景的内存开销
1
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50%
Confidence
Long-term
Relevance
9/10/2026
First Seen
Sources
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UnverifiedDeepSeek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79% 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73% similarUnverified通过GGUF量化等压缩技术，大模型可以在普通笔记本上流畅运行，Ollama、LM Studio等工具降低了本地部署门槛72% similarVerified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%72% similarUnverifiedMLA将高维K、V矩阵投影至低秩潜在空间，压缩比可达8-16倍71% similar
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