RAG & Retrieval
Multi-Head Latent Attention (MLA)
Multi-head latent attention is an attention design introduced by DeepSeek that compresses keys and values into a small shared latent vector, so the model caches that latent instead of full per-head keys and values.
In depth
Multi-head latent attention is an attention design introduced by DeepSeek that compresses keys and values into a small shared latent vector, so the model caches that latent instead of full per-head keys and values. In the DeepSeek-V2 paper it cut the KV cache by 93.3% versus DeepSeek 67B and raised maximum generation throughput 5.76x. MLA has since been reused in other open models, including Kimi Linear, which mixes it with linear attention layers.
Primary source: arxiv.org
Example
In practice, developers reach for Multi-Head Latent Attention (MLA) when they need the capability described above as part of an AI feature or workflow.
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FAQ
Common questions
What is Multi-Head Latent Attention (MLA)?
Multi-head latent attention is an attention design introduced by DeepSeek that compresses keys and values into a small shared latent vector, so the model caches that latent instead of full per-head keys and values.
Why does Multi-Head Latent Attention (MLA) matter for AI developers?
Multi-Head Latent Attention (MLA) sits in the RAG & Retrieval part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Multi-Head Latent Attention (MLA)?
Developers Digest publishes tutorials and videos that cover RAG & Retrieval topics including Multi-Head Latent Attention (MLA). Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
RAG & Retrieval
MTEB
The Massive Text Embedding Benchmark, a suite for comparing embedding models across many tasks instead of a single similarity dataset.
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ALiBi
ALiBi (Attention with Linear Biases) is a way to give a transformer position information by penalizing attention scores in proportion to the distance between query and key, instead of adding positional embeddings to the token embeddings.
RAG & Retrieval
Multimodal RAG
An extension of RAG that retrieves and processes not just text but also images, tables, code snippets, diagrams, and other non-text content.
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