A derivation- and implementation-oriented treatment of LLM quantization outliers: geometric diagnostics, mixed-precision decomposition, SmoothQuant scaling, affine shifts, fixed and learned rotations, structured learned transforms, graph fusion, calibration, and runtime cost.
A derivation- and implementation-oriented treatment of scalar weight-only post-training quantization: round-to-nearest, layer reconstruction geometry, OBD/OBS/OBQ, GPTQ compensation and lazy block updates, AWQ activation-aware scaling, calibration state, packed artifacts, and runtime kernels.
A derivation- and implementation-oriented treatment of quantization calibration: observer state, clipping objectives, representative corpora, output-aware error, sensitivity scans, Hessian alternatives, mixed-precision optimization, and runtime-feasible precision maps.