Researchers train AI model that hits near-full performance with just 12.5 percent of its experts

Researchers at the Allen Institute for AI and UC Berkeley have built EMO, a mixture-of-experts model whose experts specialize in content domains instead of word types. That lets you strip out three-quarters of the experts while losing only about one percentage point of performance, a step that could make MoE models practical for memory-constrained settings for the first time. The article Researchers train AI model that hits near-full performance with just 12.5 percent of its experts appeared first on The Decoder .