ML//model compression

Model compression reduces the storage, memory traffic or computation required by a model while trying to preserve useful capability. It is a family name, not one trick.

Model compression reduces the storage, memory traffic or computation required by a model while trying to preserve useful capability. It is a family name, not one trick.

Quantization stores numbers with fewer bits.

Pruning removes parameters or structures judged unnecessary.

Distillation trains a smaller student to reproduce a larger teacher.

Factorization replaces large tensors with cheaper structured representations.

The useful metric is not “percentage compressed” in isolation. What matters is whether the target runtime converts that structural change into lower latency, higher throughput or cheaper deployment without unacceptable quality loss.

Smaller on paper is only valuable when the machine notices.