Multi-Head Latent Attention (MLA)
Multi-Head Latent Attention (MLA) is a new attention mechanism that enhances the efficiency of models like DeepSeek-V2 and DeepSeek-V3. It achieves significant compression of the key-value (KV) cache by projecting them into a low-dimensional latent space, resulting in a 5-10x reduction in size with minimal quality loss. This innovation alters the implementation of prefix caching, chunked prefill, and paged attention in these models.
- ▪MLA replaces standard Multi-Head Attention (MHA) in specific AI models.
- ▪It compresses KV cache using low-rank projections, achieving substantial size reduction.
- ▪The compression ratio can reach up to 64 times in certain configurations.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3934991) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Sirajuddin Shaik Posted on May 23 Multi-Head Latent Attention (MLA) #ai #machinelearning #llm Compressing KV cache via low-rank projections — the attention mechanism behind DeepSeek-V2/V3 and Kimi K2.x Why This Matters Multi-Head Latent Attention (MLA) is the attention variant that replaces standard Multi-Head Attention (MHA) in DeepSeek-V2, DeepSeek-V3, and Kimi K2.x models.
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