Unverified50% confidenceFactExact time
The MLA architecture uses a 512-dimensional latent plus 64-dimensional RoPE design, which can reduce KV Cache by approximately one order of magnitude.
1
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50%
Confidence
Long-term
Relevance
9/23/2026
First Seen
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Related Claims
UnverifiedMLA theoretically reduces the GPU memory footprint of KV Cache to 5% to 13% of standard MHA by compressing KV matrices into low-dimensional latent vector caches.65% similarUnverifiedFor a 32-layer, 4096-dimensional model, the KV Cache for processing 2048 tokens occupies approximately 1 GB of VRAM65% similarUnverifiedvLLM引入PagedAttention技术将KV Cache分割为固定大小物理块按需分配，而llama.cpp采用预分配策略在模型加载时一次性保留完整KV Cache空间63% similarUnverifiedHuawei's Pangu Large Model 2.0 adopts a sparse architecture with 505 billion parameters and supports a 512K ultra-long context.63% similarUnverifiedvLLM的PagedAttention将KV Cache划分为固定大小物理块，通过块表实现地址映射，将碎片化率从60-80%降至不足4%，吞吐量提升可达24倍63% similar
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