Multi-Head Attention

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14:15

2026-06-25

dev.to

large-language-models

Why KV Cache Matters — How MQA, GQA, and MLA Make LLM Inference Faster

KV Cache reduces duplicated computation in autoregressive LLM inference by storing previously computed Key and Value tensors, but creates a memory bottleneck as context length grows. To address this, …

15:12

2026-06-15

dev.to

large-language-models

How Transformers Work — From Self-Attention to Modern LLM Architecture

A developer explains how the Transformer architecture works, from self-attention to modern LLMs. The key innovation is that Transformers compare tokens directly via attention rather than processing se…

13:14

2026-05-23

dev.to

large-language-models

Multi-Head Latent Attention (MLA)

**Summary:** Multi-Head Latent Attention (MLA) is an attention mechanism used in DeepSeek-V2/V3 and Kimi K2.x models that compresses the Key-Value (KV) cache by projecting full KV pairs into a shared,…

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Multi-Head Latent Attention 2 DeepSeek-V2 1 DeepSeek-V3 1 Kimi K2.x 1 Transformer 1 RNN 1 GPT 1 RoPE 1