A hands-on tutorial implementing Multi-Head Latent Attention (MLA), the memory-efficient attention mechanism used in DeepSeek-V3. Covers the KV cache memory bottleneck in standard Transformers, then explains how MLA uses low-rank projections to compress key-value representations, reducing cache memory by 4-16x. Also details query compression, RoPE integration with separate content and positional components, causal masking, and provides complete PyTorch implementation code. Compares MLA to other KV cache optimization strategies like MQA, GQA, and quantization.
Table of contents
Build DeepSeek-V3: Multi-Head Latent Attention (MLA) ArchitectureThe KV Cache Memory Problem in DeepSeek-V3Multi-Head Latent Attention (MLA): KV Cache Compression with Low-Rank ProjectionsQuery Compression and Rotary Positional Embeddings (RoPE) IntegrationAttention Computation with Multi-Head Latent Attention (MLA)Implementation: Multi-Head Latent Attention (MLA)Multi-Head Latent Attention and KV Cache OptimizationSummarySort: