Why Multi-head Latent Attention Is the Ultimate LLM Memory Saver
Discover how Multi-head Latent Attention slashes KV cache memory requirements by up to 93 percent without sacrificing model accuracy. This detailed breakdown explains the low-rank compression math keeping modern open-source LLMs incredibly cheap and fast.