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Hugging Face 提出高效知识蒸馏方法，显存占用大幅降低
原标题：Making Knowledge Distillation Cheap Enough to Run at Scale August 10, 2026
AI 摘要
Hugging Face 团队发表论文，提出两种系统优化方法：离线缓存教师模型的 Top-K logits，以及融合分块 KL 损失，以大幅降低大语言模型知识蒸馏的显存占用和训练成本。该方法使长上下文蒸馏可在单 GPU 上运行，并支持大规模实验。
以上摘要由 AI 生成，可能存在误差。事实请以原文为准。
正文节选
Knowledge distillation, training a smaller student model to match the performance of a larger teacher, is a well-known technique in Machine Learning. With the recent wave of open-source Large Language Models, such as gpt-oss, Qwen, GLM, or Kimi, it has become a mainstream research topic again. Deploying these very large models is expensive: the recent Kimi-K3 model has 2.8 trillion parameters and needs roughly 3TB of VRAM just to load. Compressing them into smaller models and recovering the orig
发布时间：—
抓取时间：2026-08-10 18:19
来源机构：Hugging Face