The increased number of routed experts per layer in DeepSeek-V3 significantly impacts its performance by enhancing model capacity and efficiency. Here's a detailed breakdown:
Increased Model Capacity
DeepSeek-V3 increases the number of routed experts per layer from 160 in previous versions to 256, which allows for greater specialization and diversity among experts[1]. This increase in the number of experts means that each expert can focus on a more specific subset of tasks or knowledge domains, potentially leading to better overall model performance. The model's ability to activate only the top 8 experts for each token ensures that computational resources are efficiently utilized, as only a fraction of the total parameters are engaged at any given time[4][9].
Load Balancing and Routing Efficiency
One of the challenges with increasing the number of experts is the risk of routing collapse, where a subset of experts becomes overly utilized while others remain idle. DeepSeek-V3 addresses this issue by introducing bias terms that dynamically adjust during training to ensure load balance across experts[2][4]. These bias terms influence routing decisions without affecting the final output weights, ensuring that the model maintains optimal routing based on token affinity while preventing overloading of certain experts.
Computational Efficiency
The use of a hybrid routing strategy, combining soft and hard routing, allows DeepSeek-V3 to scale up modeling capacity with minimal computational overhead. By activating only the top 8 experts for each token, the model achieves significant computational efficiency compared to traditional dense models, where all parameters are always active[5][9]. This efficiency is crucial for large-scale models like DeepSeek-V3, as it reduces both training and inference times while minimizing memory usage.
Specialization and Knowledge Representation
DeepSeek-V3's architecture promotes specialization among experts by allowing each to focus on specific knowledge domains. This specialization is enhanced by the presence of shared experts, which capture common knowledge applicable across all tokens[3][4]. The combination of shared and routed experts ensures that the model can handle both general and specialized knowledge effectively, leading to improved performance on diverse tasks.
Avoidance of Redundancy
By increasing the number of experts and reducing their size, DeepSeek-V3 reduces redundancy in the model. Each expert is smaller but more numerous, allowing for a vast increase in possible expert combinations for each token without increasing the total number of parameters[3]. This approach ensures that each expert learns unique information, maximizing the model's representational capacity.
In summary, the increased number of routed experts in DeepSeek-V3 enhances model performance by improving specialization, efficiency, and load balancing, while also reducing redundancy and computational costs. These innovations make DeepSeek-V3 a powerful tool for large-scale language modeling tasks.
Citations:
[1] https://fireworks.ai/blog/deepseek-model-architecture
[2] https://machinelearningatscale.substack.com/p/deepseek-v3-model
[3] https://www.chrishayduk.com/p/understanding-deepseek-part-i-deepseekmoe
[4] https://gonzoml.substack.com/p/deepseek-v3-technical-details
[5] https://mlfrontiers.substack.com/p/understanding-deepseek-v3
[6] https://www.byteplus.com/en/topic/375456
[7] https://mccormickml.com/2025/02/12/the-inner-workings-of-deep-seek-v3/
[8] https://epoch.ai/gradient-updates/how-has-deepseek-improved-the-transformer-architecture
[9] https://www.kisekilabs.com/blog-posts/why-deepseek-v3-matters-in-the-world-of-llms
[10] https://semianalysis.com/2025/01/31/deepseek-debates/