survey of lightweight model distillation in edge deployments

Research and field reports show lightweight distillation can deliver meaningful latency and power gains for edge inference, provided task-specific validation remains strict (arXiv).

see also: small language models win on edge maintenance · model distillation factories appear across teams

evidence stack

• Distilled models reduce compute and memory footprints.
• Calibration quality determines downstream reliability.
• Domain shift can erase distillation gains quickly.

method boundary

Distillation benefits hold when deployment context matches training assumptions.

my take

Distillation works best as a lifecycle discipline, not a one-off compression step.

linkage

• [[small language models win on edge maintenance]]
• [[model distillation factories appear across teams]]
• [[queue aware batching improves gpu utilization stability]]

ending questions

which calibration check best prevents hidden quality decay after distillation?