llama.cpp Expands MoE Experts at Runtime
💡Explore runtime-only MoE expert expansion in llama.cpp—no retraining required.
⚡ 30-Second TL;DR
What Changed
Routes more experts than the model’s native top-K, with an example of expanding from 8 to a larger number.
Why It Matters
This could give developers a way to trade additional inference compute for potentially richer MoE behavior without retraining models. However, practitioners should benchmark quality, latency, memory use, and routing stability because the proposal is experimental and the article does not establish a quality improvement.
What To Do Next
Clone the moe-expansion llama.cpp branch, run the documented Qwen 3.6 35B A4B+ test, and benchmark top-K expansion against native routing for latency, memory, and task quality.
Key Points
- •Routes more experts than the model’s native top-K, with an example of expanding from 8 to a larger number.
- •Uses an adaptive threshold and linear influence decay from 99% to 50% across selected experts.
- •Supports configurable layer ranges and all llama.cpp backends without changing model weights.
- •Tested on Qwen 3.6 35B A4B+ and documented in a dedicated llama.cpp branch.
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