MoE models activate few experts per token but suffer from workload imbalance, causing inefficiency in multi-device systems. Mixture of Grouped Experts solves this by:
Grouping experts and forcing tokens to activate an equal number per group
Inherently balancing compute load across devices
Boosting throughput (especially for inference) by eliminating bottlenecks
MoGE retains MoE's parameter efficiency while enabling optimal hardware utilization.
Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity
The surgence of Mixture of Experts (MoE) in Large Language Models promises a small price of execution cost for a much larger model parameter count and learning capacity, because only a small fraction of parameters are activated for each input token.arXiv.org
benda
in reply to AstroMancer5G (she/her) • • •Baby shah has lived his life in a western bubble. If the zionist carpet bomb half the country, any Iranian left will still spit in the face of baby shah. He's no iranian, he's just more zionist trash.
He misses the days where Iran would posture like a super power and send its children to Africa to die for western hegemony.
Make CENTO Great Again!