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DisaggKD: Efficient Knowledge Distillation through Teacher-Student Disaggregation and a Fused Loss
Knowledge distillation (KD) frameworks place teacher and student on the same GPUs, so per-GPU memory limits the teachers that fit. DisaggKD separates them across nodes: teacher replicas stream hidden states to the student, a KD loss over row windows and vocabulary shards equals the full-vocabulary l
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