Standard Multi-Head Attention . . . . . . . . . . . . . . . . 6 2.1.2 Low-Rank Key-Value Joint Compression . . . . . . . . . . . . . . . . . . . 7 2.1.3 Decoupled Rotary Position Embedding . . . . . . of both worlds, we introduce MLA, an attention mechanism equipped with low-rank key-value joint compression. Empirically, MLA achieves superior performance compared with MHA, and meanwhile significantly innovative archi- tectures. For attention, we design MLA, which utilizes low-rank key-value joint compression to eliminate the bottleneck of inference-time key-value cache, thus supporting efficient inference

services that drive adoption among consumers and large organizations. But as the cost-performance ratio of open models continues to improve – and if the infrastructure to support them becomes more turnkey