Technical breakthroughs and application value of MoE architecture
The Qwen3 release of Mixed Expert Models (MoE) represents a significant advance in parameter efficiency optimization. Among them, the Qwen3-30B-A3B model adopts the design of 30 billion total parameters/three billion activated parameters, which can surpass the performance of the traditional dense model QwQ-32B by activating only one-tenth of the parameters. This breakthrough stems from three major technological innovations: optimization of dynamic routing algorithms for expert networks, improvement of hierarchical activation mechanisms, and enhancement of expert task specialization.
The technical specifications show that the Qwen3-235B-A22B model contains 235 billion total parameters and 22 billion activation parameters, using a 94-layer Transformer structure with 128 expert networks (8 per activation). Compared to similar dense models, the MoE version reduces training cost by 401 TP3T and inference energy by 601 TP3T while maintaining comparable performance.The open-weighted Qwen3-30B-A3B model achieves traditional 70B parameter-level performance in HuggingFace benchmarks using only 3 billion activation parameters.
This architecture is particularly well suited for edge computing scenarios, enabling large models with hundreds of billions of parameters to run on consumer-grade GPUs (e.g., RTX 4090). The team's real-world testing shows that deploying MoE models on A100 GPUs increases throughput by 3x over traditional dense models, paving the way for pervasive deployment of AI services.
This answer comes from the articleQwen3 Released: A New Generation of Big Language Models for Thinking Deeply and Responding FastThe
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