Commenters debate whether speed-optimized LLM architectures have viable use cases
3Sep 23 7:42 PM · 2d ago · 1 comment · 1 source · development 3 of 5
Discussion emerged about the diffusion LLM approach underlying Mercury 2.5, with one commenter arguing it is a 'dead end' and noting that frontier labs like Google have abandoned similar speed-focused experiments, while others questioned what practical applications justify the intelligence trade-offs.
“I honestly think the diffusion LLM approach is a dead end. It's telling that frontier labs like Google toyed around with it but didn't invest further even for their most speed and cost sensitive small models”
I honestly think the diffusion LLM approach is a dead endIt's telling that frontier labs like Google toyed around with it but didn't invest further even for their most speed and cost sensitive small modelsStill unclear for what, if any use cases this is pareto frontier