AWQ vs GPTQ vs BitNet — what's the difference? | Rudrite Research

Rudrite Research compares three methods for shrinking large language models: AWQ scales salient weights, GPTQ compensates rounding with second-order math, and BitNet trains ternary weights to turn matrix multiplication into addition.

AWQ vs GPTQ vs BitNet Three ways to shrink an LLM — scale the salient weights, compensate the rounding with second-order math, or train ternary so the matmul becomes addition. A clear, side-by-side comparison with examples — part of Rudrite Research.