Proposed architecture for inferencing sparse MOE models increasing Active parameters using layered + linear decay. Succinct reasoning without any model training or fine tune. [p]

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User Specific-Tax-6700 implements the expert expansion function for sparse MoE models in llama.cpp. This solution allows the number of experts in runtime routing to exceed the native top-K limit (e.g., expanding 8 to x), using a hierarchical and linear decay mechanism, setting adaptive thresholds, and a influence decay range from 99% to 50%. This function supports all backends and enables streamlined inference without model training or fine-tuning. Related tests have been completed on the Qwen 3.6 35B A4B+ model; the implementation is located in the moe-expansion branch of the GitHub repository.

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Qwen 3.6llama.cpp

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llama.cpp MoE expert expansion 支持稀疏 MoE 推理扩展

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llama.cpp1
Qwen 3.61

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R r/MachineLearning en 2026-09-07 02:41

User Specific-Tax-6700 implements the MoE expert expansion function in llama.cpp, supporting sparse MoE models with a larger number of routing experts than the native top-K (8 variants x). This solution uses a hierarchical plus linear decay mechanism, setting adaptive thresholds and an influence decay range from 99% to 50%, along with limiting the number of layers. This feature relies solely on runtime, applies to all backends, and enables streamlined inference without model training or fine-tuning. It has been tested on the Qwen 3.6 35B A4B+ model, and relevant documentation can be found in the GitHub repository moe-expansion branch.