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Service

Teacher-Student Model Distillation

A large model compressed into a smaller, faster one.

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What we do​

  • Teacher to studentA small model learns from a large model's full output distribution, not only its answers.
  • Cheaper servingFewer parameters generate tokens faster and serve more users per GPU.
  • Edge deploymentSized for phones, edge servers and other hardware the teacher cannot run on.

How we work​

  1. Set the target. We choose or train the teacher, and size the student for your hardware.
  2. Distil. The teacher generates soft labels and reasoning traces; the student trains to match them.
  3. Verify. The student and the teacher answer the same evaluation set.

What you get​

  • The student model, sized for your deployment.
  • Its benchmark against the teacher on your evaluation set.

Start​

Tell us the model you run today and the hardware it must fit, and we reply with a scope and a quote. Email sale@iapp.co.th or call 02-124-4041.