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DeepSeekMoE: Bridging Efficiency and Capacity in Large Language Models using DeepSeek Model from China
The DeepSeekMoE model, as outlined in the provided architecture, represents a significant advancement in the design of large-scale language models by integrating Mixture of Experts (MoE) with novel attention mechanisms and normalization strategies
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DeepSeekMoE is a novel language model architecture that integrates Mixture of Experts (MoE) with a Multi-Head Latent Attention (MLA) mechanism and RMSNorm to achieve unprecedented scalability and inference efficiency. By introducing shared experts, dynamic routing, and latent variable caching, DeepSeekMoE reduces computational costs by up to 40% compared to traditional MoE models while maintaining state-of-the-art accuracy.
This article explores its architecture, mathematical foundations, and empirical performance, positioning it as a groundbreaking solution for deploying large-scale models in resource-constrained environments.
1. Architectural Overview
The DeepSeekMoE architecture stacks L Transformer blocks, each comprising:
- Multi-Head Latent Attention (MLA)
- Mixture of Experts (MoE) Layer
- RMSNorm for stabilization.
1.1 Mixture of Experts (MoE) Layer
- Dynamic Expert Routing: For an input token embedding utut, the router selects kk out of NsNs experts (k≤4k≤4) using a gating network:
g(ut)=Softmax(Wgut),Top-k experts selected,g(ut)=Softmax(Wgut),Top-k experts selected,
where WgWt is a learnable weight matrix.
Shared Experts: Unlike conventional MoE models, DeepSeekMoE designates a subset of experts as shared across tokens or layers, reducing redundancy. The final output…
