New Chunked KL Loss Slashes Knowledge Distillation VRAM to <6GB for 32K Context
ikergarcia1996 · reddit · 2026-08-11
A developer has open-sourced an efficient implementation of the KL-loss function for Knowledge Distillation. By adopting a chunking approach similar to Flash Attention, the method reduces VRAM usage from quadratic to linear.
At a 32K context length, VRAM requirements for KL-loss drop dramatically from roughly 85GB to just 5GB, with a 3x speedup at long contexts. This breakthrough enables local Knowledge Distillation for training small student models without massive hardware.
The implementation patches the model's lm-head forward pass and utilizes cached top-k logits. The author has released both the source code and an accompanying ArXiv paper detailing the optimizations.
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