Lesson 1119 of 2116
Model Distillation: Smaller Models Trained From Larger
Distillation trains small models to mimic large ones. Useful for cost and latency — when the trade-offs fit.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2Model Distillation: When and How
- 3The premise
- 4AI model families: distillation and the rise of small specialists
Concept cluster
Terms to connect while reading
Section 1
The premise
Model distillation enables smaller models to approximate larger ones; useful for cost and latency.
What AI does well here
- Distill when latency or cost is critical and quality acceptable
- Test distilled model quality against original on your use case
- Maintain access to original for fallback or quality-sensitive cases
- Plan for re-distillation as base models improve
What AI cannot do
- Get full base model capability from distilled model
- Substitute distillation for use case clarity
- Eliminate the quality trade-off
Key terms in this lesson
Section 2
Model Distillation: When and How
Section 3
The premise
Model distillation creates economical alternatives to large models; warranted when use case stable and high-volume.
What AI does well here
- Distill when use case is stable and high-volume
- Test distilled quality against original on your data
- Maintain access to original for fallback
- Plan re-distillation as base models improve
What AI cannot do
- Get full base capability from distilled model
- Substitute distillation for use case clarity
- Eliminate the quality trade-off
Section 4
AI model families: distillation and the rise of small specialists
Section 5
The premise
Distillation produces small models trained on the behavior of a larger one. On narrow tasks they often match the teacher, run cheaper, and serve faster — at the cost of breadth.
What AI does well here
- Specialize in the distribution of training data they saw
- Serve at much lower cost and latency than the teacher
- Match teacher quality on in-distribution prompts
What AI cannot do
- Generalize beyond the distribution they were trained on
- Self-update when the teacher learns new behaviors
- Replace the teacher when prompt patterns drift
Section 6
AI Distillation: Training a Cheap Model from an Expensive One
Section 7
The premise
Distillation generates training data with a strong model and uses it to fine-tune a small model for your specific task — yielding huge cost wins on narrow workloads.
What AI does well here
- High-volume narrow tasks where Sonnet/GPT-5 is overkill
- Generate synthetic training data with a teacher model
- Fit the student on a small open-weight base
- Maintain quality on the narrow task while losing generality
What AI cannot do
- Match the teacher on tasks outside the training distribution
- Skip license review of teacher model outputs
- Stay good forever — you'll need to refresh
- Replace having an eval set
End-of-lesson quiz
Check what stuck
15 questions · Score saves to your progress.
Tutor
Curious about “Model Distillation: Smaller Models Trained From Larger”?
Ask anything about this lesson. I’ll answer using just what you’re reading — short, friendly, grounded.
Progress saved locally in this browser. Sign in to sync across devices.
Related lessons
Keep going
Creators · 40 min
Prompt Caching Comparison: Anthropic, OpenAI, Gemini
How prompt caching works across vendors and where it pays off.
Creators · 40 min
Local Model Family: Gemma
Gemma is Google DeepMind open-model family, useful for local and single-accelerator experiments when students want polished small models.
Creators · 11 min
AI Token Cost Optimization: From Pilot to Production Without Sticker Shock
Token costs sneak up. A pilot at $200/month becomes a production system at $20,000/month. Here's how teams keep cost under control as they scale.