# Making Knowledge Distillation Cheap Enough to Run at Scale

DevFeed: [Making Knowledge Distillation Cheap Enough to Run at Scale](<https://devfeed.tech/articles/making-knowledge-distillation-cheap-enough-to-run-at-scale-7021.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/MultiverseComputingCAI/efficient-knowledge-distillation>)

Author: Antonio Tiene; Iker García-Ferrero; Ali Hashemi; Bakbergen Ryskulov

Published: 2026-08-10T10:05:36Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [LLM Techniques](<https://devfeed.tech/topics/llm-techniques.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Post-training optimization](<https://devfeed.tech/topics/post-training-optimization.md>), [Model Routing](<https://devfeed.tech/topics/model-routing.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [PyTorch](<https://devfeed.tech/topics/pytorch.md>), [gpt-oss](<https://devfeed.tech/topics/gpt-oss.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>)

Tags: [blog](<https://devfeed.tech/tags/blog.md>), [caching](<https://devfeed.tech/tags/caching.md>), [compute](<https://devfeed.tech/tags/compute.md>), [cost](<https://devfeed.tech/tags/cost.md>), [gpt](<https://devfeed.tech/tags/gpt.md>), [gpt-oss](<https://devfeed.tech/tags/gpt-oss.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [large-language-models](<https://devfeed.tech/tags/large-language-models.md>), [long-context](<https://devfeed.tech/tags/long-context.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [memory](<https://devfeed.tech/tags/memory.md>), [performance](<https://devfeed.tech/tags/performance.md>), [pytorch](<https://devfeed.tech/tags/pytorch.md>), [qwen](<https://devfeed.tech/tags/qwen.md>), [research](<https://devfeed.tech/tags/research.md>), [training](<https://devfeed.tech/tags/training.md>)

## AI overview

This article presents an efficient approach to knowledge distillation for large language models. It caches the teacher model's top-K logits offline and uses a fused, memory-efficient chunked KL-divergence loss, avoiding simultaneous teacher-student residency and full vocabulary-by-sequence-length matrices. The changes reduce VRAM use and training cost, enabling long-context distillation on a single GPU and making larger-scale experimentation more practical.

## Source excerpt

A Blog post by Multiverse Computing on Hugging Face