Making Knowledge Distillation Cheap Enough to Run at Scale

Knowledge distillation can compress large language models into smaller students, but conventional online methods are costly because they keep teacher and student models in memory simultaneously and compute full vocabulary distributions at every token. Multiverse Computing’s research proposes offline distillation, which caches each teacher output’s top 100 logits for reuse, alongside a fused chunked KL-divergence loss that processes small sequence segments without materializing full vocabulary-by-sequence tensors. Tests on an H200 GPU found that these approaches preserved near-identical training loss to online distillation while substantially reducing memory use, with the fused method offering the largest gains at long context lengths. In a 32K-token GPT-OSS 20B experiment, the method reportedly reduced hardware needs from four GPU nodes to one, lowered step time by roughly fivefold, and improved per-GPU throughput. The resulting 3.2B-parameter student retained much of the accuracy of its 8B-parameter Llama teacher, and the chunked-loss implementation has been open-sourced.

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