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 original capabilities through knowledge distillation has therefore become standard practice, with companies like Nvidia (Nemotron 3 Puzzle 75B) or Multiverse Computing (Hypernova 60B) recently releasing high-quality compressed models.
Making Knowledge Distillation Cheap Enough to Run at Scale
A Blog post by Multiverse Computing on Hugging Face
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Key takeaways
Researchers developed a method to reduce the cost of knowledge distillation, a technique used to compress large language models, by caching the teacher's output and using a memory-efficient loss function.
- The method, called offline distillation, reduces VRAM usage by caching the teacher's top-K logits and using a fused, chunked KL loss.
- The new method can train large language models on a single GPU, making large-scale experimentation practical.
- The method reduces VRAM usage by 15.6 times and speeds up training by 5 times compared to the previous method.
- The method was tested on a GPT-OSS 20B model and reduced the number of GPU nodes required from four to one.
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