Edit the Model You Deploy: Quantization Weakens Knowledge Edits Through Their Conditioning

Knowledge edits and unlearning updates are computed on full-precision weights, but the served model is quantized, which can weaken or undo them. For unlearning, the explanation given is that an update smaller than the quantization step is rounded away. We show that this explanation does not carry over to closed-form (locate-and-edit) edits. By a classical dither argument, rounding on a fixed, evenly spaced grid without clipping is unbiased on an update of any size when the weights' bin positions are uniform and independent of it, and on four models single edits below the bin width keep at least 99% of their margin, on average, when only the edited matrix is quantized. The damage comes instead from the model the edit was solved against: quantized layers below the edit move the key it fires at, and quantized layers above change the read-out its value was fitted to. Quantizing everything except the edited matrix reproduces 90–101% of the damage to a single MEMIT edit of one layer (33–91% of it generic at int3), on seven models (124M–8B), two datasets and weight-only round-to-nearest, NF4, AWQ and served GPTQ and AWQ at 3 and 4 bits. At the served int4, an edit loses 4–16% of its margin, and at 3 bits 20–56%. Solving the same closed form inside the quantized model returns 90–100% of the 3-bit single-edit loss on four of five models (72–96% for released MEMIT), 77–104% under 3-bit GPTQ and AWQ, and 72–95% on held-out paraphrases; on Qwen2.5-3B, whose symmetric int3 model is itself broken, it buys efficacy at the cost of locality. Where the dither's assumptions fail, the explanation holds: gradient unlearning at a small learning rate, stored in bf16, changes mainly near-zero weights, whose bin positions are not uniform, and int4 rounding delivers 5–29% of such an update for seven of eight methods on a 1B model. All code and result files will be public at https://github.com/xxx/xxx upon acceptance.