How does DeepSeek's Mixture-of-Experts system improve its efficiency

Key Strategies for Improved Efficiency

1. Fine-Grained Expert Segmentation:
DeepSeekMoE introduces a method of segmenting experts into smaller, more specialized units. By splitting the feedforward neural network's (FFN) intermediate hidden dimensions, the system can activate a greater number of fine-grained experts without increasing overall parameter count. This fine segmentation allows for a more precise allocation of knowledge across experts, ensuring that each expert focuses on distinct aspects of the data, thus enhancing specialization and reducing redundancy among activated parameters[1][2].

2. Shared Expert Isolation:
The architecture isolates certain experts to function as shared entities that are always activated. This strategy captures and consolidates common knowledge across various contexts, which mitigates redundancy among other routed experts. By compressing common knowledge into these shared experts, DeepSeekMoE ensures that each routed expert can concentrate on unique information, thereby improving parameter efficiency and specialization[2][4].

Performance Outcomes

DeepSeekMoE demonstrates significant performance gains with fewer computations. For instance, a model with 2 billion parameters achieves comparable results to larger models (e.g., GShard with 2.9 billion parameters) while using only about 40% of the computational resources[1]. Furthermore, when scaled to 16 billion parameters, it maintains competitive performance against other models like LLaMA2 while significantly reducing computational demands[1][2].

In summary, DeepSeek's MoE system enhances efficiency by allowing for targeted activation of specialized experts and minimizing redundancy through shared knowledge structures. This results in a powerful yet resource-efficient model capable of handling complex tasks effectively.