A Visual Guide to Attention Variants in Modern LLMs
9 hours ago
- #LLM
- #DeepLearning
- #Attention-Mechanisms
- The article provides a visual guide to various attention variants used in modern Large Language Models (LLMs), including Multi-Head Attention (MHA), Grouped-Query Attention (GQA), Multi-Head Latent Attention (MLA), Sliding Window Attention (SWA), DeepSeek Sparse Attention (DSA), Gated Attention, and Hybrid Attention.
- Multi-Head Attention (MHA) is the standard transformer mechanism where several self-attention heads run in parallel to build a richer representation of the input.
- Grouped-Query Attention (GQA) reduces KV-cache memory by sharing key-value projections among query heads, making it a popular choice for efficient inference.
- Multi-Head Latent Attention (MLA) compresses KV-cache representations to save memory, offering better modeling performance at large scales compared to GQA.
- Sliding Window Attention (SWA) limits attention to a fixed local window of tokens, reducing memory and compute costs for long-context inference.
- DeepSeek Sparse Attention (DSA) uses a learned sparse pattern to select relevant past tokens, differing from SWA's fixed window approach.
- Gated Attention modifies full-attention blocks with stability-oriented changes, often used in hybrid architectures.
- Hybrid Attention combines cheaper linear or state-space sequence modules with occasional full-attention layers for long-context efficiency, as seen in models like Qwen3-Next and Kimi Linear.
- The article concludes that hybrid architectures are promising for long-context efficiency but are still novel and less optimized for inference compared to classic setups like GQA.