Making Knowledge Distillation Cheap Enough to Run at Scale

A new technique for knowledge distillation of large language models drastically reduces VRAM usage by caching teacher top-K logits and using a fused chunked KL-divergence loss, enabling distillation on a single GPU and making large-scale experiments practical.

This Wire brief sits within Fusion42's coverage of AI Infrastructure and Generative AI.