Benchmark Averages Hide the Failures That Matter: Quantizing ESM-2 for Protein Variant-Effect Prediction

<jats:p>We benchmark six numerical precision configurations for ESM-2 protein language models across throughput, memory footprint and predictive accuracy, on two workloads with sharply different characteristics: bulk embedding extraction and deep mutational scanning (DMS) variant-effect scoring. Accuracy is evaluated on the complete ProteinGym substitution benchmark — 201 assays, 2.41M variants — at three model scales spanning 650M to 15B parameters, with a paired bootstrap clustered on protein. Three findings follow, and each contradicts a common practice. First, benchmark averages conceal the failure that decides deployability: no configuration shifts mean correlation by more than 0:007 at any scale, yet INT8 dynamic quantization — indistinguishable from fp32 on that mean at 3B (p = 0:34) — takes a single assay from ρ = 0:591 to 0:223. Selection must be made on worst-case, not mean, behaviour. Second, fidelity measured against fp32 bounds risk but cannot rank quality: over 3015 assay/configuration pairs it predicts the magnitude of ground-truth change (r = 0:56–0:81) but not its direction, and the INT4 effect differs significantly between 650M and 3B (+0:0101, p = 0:0007) with no monotone trend to extrapolate. Third, quantizing a large model is dominated by using a small one: of eighteen scale/configuration combinations only three are Pareto-optimal over accuracy, memory and speed, and all three are 650M. The one catastrophic failure we observe is a defect of default symmetric activation scaling, not of W8A8 itself: asymmetric activation quantization, a one-line change needing no calibration, removes every damaged assay. We also give a label-free screen for at-risk targets, and report four measurement artifacts encountered during this study, three of which inverted the result they were meant to measure.</jats:p>