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Model Distillation: Small Models, Big IQ

SEPT 15, 2025•OPTIMIZATION•8 MIN READ

You do not need a 70B parameter model to classify customer support tickets. You need a 7B model that thinks like a 70B model.

Teacher-Student Architecture

Knowledge Distillation is the process of training a smaller "Student" model (e.g., Llama-3-8B) to mimic the output probabilities (logits) of a larger "Teacher" model (e.g., Llama-3-70B).

The Process:

  1. Run your dataset through the 70B model to generate "Gold Standard" answers.
  2. Train the 8B model not just on the ground truth, but on the *logic* of the 70B model.
  3. Deploy the 8B model on cheaper hardware (NVIDIA L40S or even A10s).

This approach reduces inference latency by ~400% and VRAM requirements by 80%, allowing massive scale on mid-tier hardware.