Making Knowledge Distillation Cheap Enough to Run at Scale

Making Knowledge Distillation Cheap Enough to Run at Scale Making Knowledge Distillation Cheap Enough to Run at Scale Team Article Published August 10, 2026 Upvote 42 Antonio Tiene AntonioTN MultiverseComputingCAI Iker García-Ferrero Iker MultiverseComputingCAI Ali Hashemi ali-hashemi MultiverseComputingCAI Bakbergen Ryskulov bryskulov-mc 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.

This OpenSourceRelease is relevant to the technology intelligence record because it involves NVIDIA, GitHub, gpt-oss, qwen. The source article should remain the factual reference for follow-up coverage.

• Making Knowledge Distillation Cheap Enough to Run at Scale Team Article Published August 10, 2026 Upvote 42 Antonio Tiene AntonioTN MultiverseComputingCAI Iker García-Ferrero Iker MultiverseComputingCAI Ali Hashemi ali-hashemi MultiverseComputingCAI Bakbergen Ryskulov bryskulov-mc 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.
• The distillation step is what decides most of the final quality, but it's also usually the most expensive part of the pipeline.
• Keeping both the teacher and student loaded, and producing a probability distribution over the entire vocabulary for every token, requires enormous amounts of VRAM, typically feasible only with hundreds of GPUs and careful tensor-parallelism strategies.