How does adaptive model compression work in ChatGPT deployment pipelines?

Adaptive model compression dynamically reduces model size using pruning, distillation, and quantization based on runtime constraints.

Adaptive compression allows ChatGPT systems to adjust model efficiency based on workload and hardware constraints. Techniques include weight pruning (removing less important connections), knowledge distillation (training smaller models to mimic larger ones), and dynamic quantization.

In production, compression may be applied conditionally based on latency budgets or GPU availability. For example, edge deployments may use heavily compressed models while cloud systems use full-scale models.

This enables flexible tradeoffs between accuracy and efficiency.