{"id":36676,"date":"2025-11-20T12:27:11","date_gmt":"2025-11-20T11:27:11","guid":{"rendered":"https:\/\/www.graviton.at\/letterswaplibrary\/4-examples-of-when-you-really-need-model-distillation-and-how-to-try-it-yourself\/"},"modified":"2025-11-20T12:27:11","modified_gmt":"2025-11-20T11:27:11","slug":"4-examples-of-when-you-really-need-model-distillation-and-how-to-try-it-yourself","status":"publish","type":"post","link":"https:\/\/www.graviton.at\/letterswaplibrary\/4-examples-of-when-you-really-need-model-distillation-and-how-to-try-it-yourself\/","title":{"rendered":"4 Examples Of When You Really Need Model Distillation (and How To Try It Yourself)"},"content":{"rendered":"

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Hi everyone, I\u2019m part of the Nebius Token Factory team and wanted to share some insights from our recent post on model distillation with compute<\/strong> (full article here<\/a>).<\/p>\n

We highlighted 4 concrete scenarios where distillation makes a big difference<\/strong>:<\/p>\n

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  1. High-latency inference:<\/strong> When your large models are slow to respond in production, distillation lets you train a smaller student model that retains most of the teacher\u2019s accuracy but runs much faster.<\/li>\n
  2. Cost-sensitive deployments:<\/strong> Big models are expensive to run at scale. Distilled models cut compute requirements dramatically, saving money without sacrificing quality.<\/li>\n
  3. Edge or embedded devices:<\/strong> If you want to run AI on mobile devices, IoT, or constrained hardware, distillation compresses the model so it fits into memory and compute limits.<\/li>\n
  4. Rapid experimentation \/ A\/B testing:<\/strong> Training smaller distilled models allows you to quickly iterate on experiments or deploy multiple variants, since they are much cheaper and faster to run.<\/li>\n<\/ol>\n

    How we do it at Nebius Token Factory:<\/strong><\/p>\n