Top-K logits caching
AssessTechniques
An optimization that stores only the teacher model’s most likely token logits for later reuse.
Why it's here
Placed in Assess: 1 article(s) of evidence from 1 source(s), led by research-stage coverage, with 1 in the last 30 days. Confidence 24%. Low accumulated evidence, so it defaults conservatively pending more signal.
Evidence (1)
- 7Hugging Face Blog·8/10/2026researchHugging Face shows cheaper large-scale knowledge distillation for LLMs
The article presents a method for making knowledge distillation for large language models much cheaper by caching the teacher model’s top-K logits offline and using a fused chunked KL loss. The approach reduces VRAM usage enough to make distillation experiments feasible on a single GPU in some cases, instead of requiring large multi-GPU setups.