Model distillation, recognized in Gartner's 2025 Hype Cycle as reaching maturity, is gaining attention for its ability to improve the performance of AI models. Despite its origins dating back to 2006 and significant work in 2015, it has seen renewed relevance as enterprises seek to optimize costs while maintaining performance. With the emergence of models like DeepSeek, companies are evaluating how to leverage model distillation, allowing them to achieve greater efficiency in AI deployment. The trend reflects a shift toward embracing this technique for broader use in practical applications.
Model distillation has been re-emphasised. The foundation models are compute hungry and extremely expensive to run, and enterprises have started asking how they can get 80% of the performance at 10% of the cost.
Model distillation is finally gaining commercial traction. It unlocks both technical merit and acceleration for AI implementations, facilitating more efficient deployment.
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