Model Compression

Model compression reduces the computational footprint of an already-trained model through structural modification of its parameters, producing a deployable compressed artifact with lower memory and inference cost ^[81]. Its Parametric Compression classification explicitly encodes reduction as the defining operation: unlike all Parametric Update techniques that modify parameters to improve capability, Parametric Compression modifies parameters to reduce resource consumption while attempting to preserve capability. Its Computational Efficiency coordinate designates an objective of footprint reduction that inherently lacks a target for intrinsic capability development. Compared to KD, which achieves efficiency through architectural reduction of a student trained from teacher outputs (Cross-Model Transfer, Teacher-Derived), Model Compression achieves efficiency by directly restructuring a single model's existing parameters, i.e., no second model is required. Its D3 (Data Requirements) profile diverges fundamentally based on the chosen algorithmic implementation: Data-free compression methods operate strictly as Parameter-Only interventions; because they consume zero training or calibration data, they completely bypass data-provenance pipelines, making them optimal for strict zero-data-access operational deployments. Conversely, calibration-dependent compression methods require a Unlabeled calibration dataset to calculate activation statistics and minimize quantization error ^[64]. The Compressed Artifact is a structurally altered model artifact—not a parameter overlay applied to the original—and must be registered as a distinct model version requiring its own validation suite: the compression operation introduces approximation errors whose distribution across the input space cannot be inferred from the original model's validation record; full behavioral regression testing on the compressed artifact against the original model's validated operational envelope is required.% before deployment equivalence can be asserted. From a regulatory and change-control perspective, this category encompasses two dimensionally distinct approaches. Post-Training Quantization (PTQ, e.g., GPTQ ^[64]) operates as a training-data-free (Parameter-Only) algebraic derivation applied after the model is fully trained. In contrast, QAT simulates lower precision during active GD, classifying it as a distinct Parametric Update training event with differing validation and data-provenance requirements.