DeepSeek-V3 Explained, Part 1: Understanding Multi-Head Latent Attention

Author(s): Nehdiii

Originally published on Towards AI.

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This is the first article of our new series “DeepSeek-V3 Explained”, where we will try to demystify DeepSeek-V3 [1, 2], the latest model open-sourced by DeepSeek.

In this series, we aim to cover two major topics:

Major architecture innovations in DeepSeek-V3, including MLA (Multi-head Latent Attention) [3], DeepSeekMoE [4], auxiliary-loss-free load balancing [5], and multi-token prediction training.Training of DeepSeek-V3, covering the pre-training, fine-tuning, and reinforcement learning (RL) alignment phases.

This article mainly focuses on Multi-head Latent Attention, which was first introduced during the development of DeepSeek-V2 and later adopted in DeepSeek-V3 as well.

Background We begin with a review of standard Multi-Head Attention (MHA), explaining the need for a Key-Value (KV) cache during inference. We then explore how MQA (Multi-Query Attention) and GQA (Grouped-Query Attention) aim to optimize memory and computational efficiency. Finally, we touch on how RoPE (Rotary Positional Embedding) integrates positional information into the attention mechanism.Multi-head Latent Attention An in-depth introduction to MLA, covering its core motivations, the need for decoupled RoPE, and how it improves performance compared to traditional attention mechanisms.References.

To better understand MLA and to make this article self-contained we’ll revisit several related concepts in this section before diving into the details of… Read the full blog for free on Medium.

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Published via Towards AI