MLA: How Low-Rank Projections Cut Attention Memory by 10x

Multi-Head Latent Attention (MLA) achieves 5-10x KV cache compression through low-rank latent projections, directly addressing a critical bottleneck in LLM inference. Already deployed in DeepSeek-V3 and Kimi K2.x, this technique maintains model quality while enabling higher batch sizes and longer context windows. For production systems, MLA represents a fundamental breakthrough in how efficiently attention-based inference can be scaled.