Part 2 of the MoE mini-series turns to GLaM, Google's 1.2-trillion-parameter Mixture-of-Experts language model that matches or beats GPT-3 on zero-, one-, and few-shot benchmarks while activating only about 8% of its parameters per token and burning roughly a third of GPT-3's training energy. The efficiency story is the headline: sparse activation means the compute per inference is dramatically smaller than the parameter count suggests.

This review breaks down GLaM's expert routing, the training data curation, and the evaluation numbers that make the sparse-scaling argument concrete. For voice AI folks weighing dense versus sparse LLM back-ends for voice assistants, or thinking about MoE-based speech foundation models, GLaM is the empirical case study that turned MoE from a curiosity into a serious deployment consideration. The deep-dive walks through what GLaM proved and what it left as open work for the next generation of sparse models.