Author(s): Nehdiii

Originally published on Towards AI.

This article marks the second entry in our DeepSeek-V3 series, focusing on a pivotal architectural breakthrough in the DeepSeek models [1, 2, 3]: DeepSeekMoE [4].

Vegapunk №02 One Piece Character Generated with ChatGPT

In this article, we’ll explore how Mixture-of-Experts (MoE) functions, why it has gained popularity in LLMs, and the challenges it presents. We’ll also examine the balance between expert specialization and knowledge sharing, and how DeepSeekMoE aims to optimize this trade-off.

And the best part: to make these concepts more intuitive, we’ll break it all down using a restaurant analogy, illustrating each element in MoE through the roles of chefs in a kitchen.

In case you are interested in the other articles of DeepSeek series, here are the links:

Part 1 : Multi-head Latent Attention

Table of contents for this article:

Background: Introduce the workings of MoE, highlighting its advantages and the challenges it poses, while also addressing the trade-off between expert specialization and knowledge sharing.DeepSeekMoE Architecture: Describe the concepts of fine-grained expert segmentation and shared expert isolation.Evaluation: Highlight DeepSeekMoE’s performance through a series of insightful experiments.Summary.References.

In the context of LLMs, MoE usually involves substituting the FFN layer in Transformer architectures with an MoE layer, as illustrated in the figure below.

Figure 1. Illustration of MoE… Read the full blog for free on Medium.

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Published via Towards AI