Advantages of the technical realization of the hybrid expert architecture
The 235 billion total parameters of the model are designed with sparse activation, and only 22 billion (9.4%) parameters are activated per inference, which improves its computational efficiency by 3-5 times over the dense model. Specific implementation features include:
- Dynamic routing mechanism intelligently assigns expert modules based on input content
- 8-bit floating-point quantization reduces memory usage by 50% while maintaining the original 94% precision.
- Hierarchical parametric activation strategies to optimize resource allocation for long text processing
Real-world tests show that in the math proof task, the architecture is 2.3x faster than the same-sized dense model for inference while maintaining MathQA-85% accuracy. In typical deployment scenarios, the FP8 version requires only 30GB of video memory to run, reducing the cost of landing large models by 60%.
This answer comes from the articleQwen3-235B-A22B-Thinking-2507: A large-scale language model to support complex reasoningThe
May not be reproduced without permission:AI productivity tools " MoE Architecture for Qwen3-235B-A22B-Thinking-2507 Achieves Optimal Balance Between Performance and Efficiency
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