Mixture of Experts (MoEs) in Transformers
This educational post explains the concept of Mixture of Experts (MoE) in transformer models. It contrasts dense scaling with sparse MoE architectures, where only a subset of experts is activated per token, leading to better compute efficiency. The post includes a practical example using gpt-oss-20b, estimating inference speed on an M3 Ultra Mac (~115 tok/s). It covers the router, load balancing, and training curves, and points to the transformers library implementation. Aimed at practitioners wanting to understand MoEs.
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