Lior Sinai • 2/22/2025

DeepSeek’s Multi-Head Latent Attention

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This article provides a detailed analysis of DeepSeek's Multi-Head Latent Attention (MLA), a key innovation in their V3 and R1 models. It explains how MLA combines attention, KV caching, and LoRA to compress vectors, reducing inference cache size. The post includes the mathematical foundations, implementation details in Julia using Flux.jl, and discusses potential performance trade-offs and enhancements like weight absorption and decoupled RoPE.

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#julia #Low Rank Adaptation #Transformer Architecture
DeepSeek’s Multi-Head Latent Attention

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