Exploring and Enhancing Advanced MoE Models: From Deepspeed-MoE to DeepSeek-V3

This study explores the application and optimization of Mixture of Experts (MoE) models in large-scale language models. We analyze the fundamental principles and limitations of the MoE architecture, followed by an in-depth examination of optimization strategies in advanced MoE architectures, including Switch Transformer, Deepspeed-MoE, PR-MoE, Mixtral 8×7B, Glam, DBRX and DeepSeek-V3. Through comprehensive benchmarks across five dimensions (popular aggregated results, commonsense reasoning, world knowledge, code and math), we compare these MoE models against the LLaMA family, Qwen 2.5 72B Base, GPT family and Claude family. Our findings indicate that MoE is a model architecture capable of maintaining high performance even under computational constraints. Techniques such as pyramid structures with residual connections, sparse activation, adaptive adjustment of expert numbers and sizes and shared expert isolation contribute to performance enhancement. Compared to standard Transformer models, MoE can achieve at least a 50% reduction in inference computation while preserving model performance. Furthermore, MoE achieves higher benchmark scores in mathematical and coding tasks. Open-source MoE models, exemplified by DeepSeek-V3, democratize AI research by lowering barriers to entry and fostering global collaboration. Community-driven initiatives, such as standardized simulation-to-real transfer protocols and resource-sharing platforms, address key challenges.