This technical analysis explores how llama.cpp implements the Mixture-of-Experts (MoE) architecture for the Qwen3.6-35B-A3B model. The post breaks down the routing mechanism that selects which experts to activate for each token, the memory layout for sparse expert weights, and the computational savings achieved through conditional computation. It also discusses practical considerations for deploying such models on consumer hardware, including memory bandwidth bottlenecks and quantization strategies. For developers working on LLM inference engines or optimizing local model deployment, this provides valuable insights into the internals of one of the most popular inference frameworks.
A deep dive into the MoE architecture of Qwen3.6-35B-A3B as implemented in llama.cpp, covering routing, sparse activation, and performance trade-offs.