Knowledge Distillation for Inference: Smaller Models, Real Speed

Large models are expensive to serve. Distillation transfers knowledge into smaller models.

This reduces latency and cost while keeping quality for targeted tasks.

A larger teacher model provides targets for the student.

Quality depends on the teacher's reliability and the diversity of training data.

Distill on the exact tasks you care about, not generic benchmarks.

This yields strong performance where it matters most.

Compare distilled models against the teacher on a fixed test suite.

Guard against regressions in critical user flows.

Route low-risk tasks to distilled models and escalate when needed.

Use distillation to power on-device or edge workloads.

Pick distillation datasets that mirror production intent and traffic shape.

Blend supervised targets with teacher logits for stable convergence.

Weight rare but critical tasks to avoid performance cliffs.

Use curriculum schedules to start with easy tasks and expand coverage.

Add temperature scaling to soften teacher outputs when needed.

Control overfitting with held-out evaluation sets and early stopping.

Label ambiguous examples to reduce inconsistent supervision signals.

Track per-task loss to see where the student falls behind.

Ship distilled models behind feature flags before full rollout.

Compare user outcomes with A/B tests to validate quality parity.

Monitor escalation rates to detect missing capabilities quickly.

Define rollback thresholds for quality drops in critical flows.

Keep a small teacher fallback pool for high-risk queries.

Log student failures to inform future distillation rounds.

Report cost savings alongside quality to guide product decisions.

Document deployment criteria so releases stay consistent.

Include latency and cost budgets in release readiness checks.

Align stakeholder expectations on which tasks remain teacher-only.

Monitor long-term drift to ensure student quality stays stable over time.

Is distillation better than quantization? They can be combined for bigger gains.

How much quality is lost? It depends on the task and dataset quality.

What is the biggest risk? A student model that generalizes poorly outside training tasks.