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Cross-Architecture Distillation: Teaching Smaller, Faster Models from Powerful AI Teachers
Modern AI systems face a recurring engineering problem:
The model with the best quality is often not the model you can afford to deploy.
A large Transformer may produce excellent results but require expensive GPUs, high memory bandwidth, and hundreds of milliseconds — or even seconds — of inference time.
Meanwhile, production applications may require:
- low latency,
- lower GPU cost,
- smaller memory footprint,
- high throughput,
- edge deployment,
- CPU inference,
- or specialized hardware.
Knowledge distillation addresses this problem by transferring knowledge from a powerful teacher model to a smaller student model.
But traditional distillation usually assumes that the teacher and student look reasonably similar.
What happens when they do not?
Suppose our teacher is a large Transformer, while the model we actually want to deploy is a small CNN, RNN, State Space Model, Mixture-of-Experts variant, or another completely different architecture.
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