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Making Knowledge Distillation Cheap Enough to Run at Scale

Back to Articles Making Knowledge Distillation Cheap Enough to Run at Scale Team Article Published August 10, 2026 Upvote 30 +24 Antonio Tiene AntonioTN Follow MultiverseComputingCAI Iker García-Ferrero Iker Follow MultiverseComputingCAI Ali Hashemi ali-hashemi Follow MultiverseComputingCAI Bakbergen Ryskulov bryskulov-mc Follow MultiverseComputingCAI 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. …

Announced

10 Aug 2026 10:05 UTC