Executive summary
Conclusion
On a fixed governance stack on one RTX 5070 Ti, we measured what quantization regime — QAT Q4_0 versus PTQ Q4_K_M — does to governed routing quality, in a 2×2 design over Gemma 4 12B (dense; a clean same-base pair) and a 26B-class MoE released pair (25.8B PTQ vs 25.2B QAT; regime confounded with a possible base revision). The answer: on this stack the QAT premium is not a scalar — it differs in sign across the two released pairs — and regime choice is a behavioral change even where quality scores barely move.
| Cell (Core-500, N=500) | route_correctness | mean latency |
|---|---|---|
| 12B dense, PTQ Q4_K_M | 0.760 | 2,482 ms |
| 12B dense, QAT Q4_0 | 0.748 | 2,626 ms |
| 26B MoE, PTQ Q4_K_M | 0.738 | 3,252 ms † |
| 26B MoE, QAT Q4_0 | 0.756 | 1,613 ms |
† the 26B PTQ artifact does not fit the 16 GB card and runs ~26/74 CPU/GPU-split; its latency reflects offload and is excluded from all conclusions.
- Within-pair regime effects are marginal and oppositely signed: 12B dense −0.012 under QAT (McNemar p=0.070); 26B pair +0.018 (p=0.078). Smoke-100 agrees in sign for both.
- The interaction — an exploratory, single-run headline — survives clustering-robust analysis: delta-of-deltas +0.030, stem-clustered bootstrap 95% CI [+0.010, +0.052] (Core-500 is 100 stems × 5 paraphrases; naive task-iid CI [+0.008, +0.052]). Aggregate averaging cancels the two effects and hides the structure. No equivalence test was run on the cell aggregates; one cross-model contrast (12B-PTQ vs 26B-PTQ) is nominally significant (p=0.035).
- The interaction concentrates in the volatile-current family, whose four cells form a crossed pattern (12B: 0.5314→0.4971; 26B: 0.4971→0.5314; the numeric identity of the crossed counts is coincidence, not a tested effect).
- Safety-critical rates: 0.000 in 7 of 8 runs; one over-verification event in 26B-PTQ Core-500 (0.008). With events this rare the data bound, rather than establish, regime-invariance.
- Regime is a behavioral change: only 23–32 % of same-task
temperature-0 outputs are byte-identical across regimes (median first
divergence 46–73 characters), against same-configuration repeat
baselines of 100/100 in all four cells — including the
CPU-offloaded PTQ MoE cell, across run windows — and a 100/100
num_ctxbyte-identity control on the 12B side. Hash-based caching, audit replay, and golden-file tests do not survive a quantization-regime swap.
Practical implications
- Choose quantization regimes per released artifact pair and per task family, not by recipe reputation; aggregate "quality parity" can conceal offsetting family-level shifts.
- On this stack, the QAT 26B artifact is best-or-tied on the two hardest families while being the fastest memory-fitting cell — consistent with its same-day adoption as a production binding (a partially circular check: two of the four cells are that decision's own runs; the anti-diagonal is the independent evidence). The dense-tier fallback is better served by the PTQ 12B (clean-pair QAT effect ≤ 0).
- Treat any quantization swap as a behavioral migration with full re-validation, not a drop-in.
Honest limits
The 26B released pair may embed a base revision (25.8B vs 25.2B) and its expert configuration is unverifiable from runtime metadata; the PTQ 26B cell ran partially on CPU — so regime, revision, and compute path are confounded on that side; the 12B pair is clean. All inferential results are exploratory: no preregistration, single run per cell, ≥5 inferential quantities without multiplicity correction, paraphrase-clustered rows (effective N nearer 100 than 500). One GPU, one runtime (Ollama 0.30.6), one governance stack, routing tasks only; answer-content quality is unmeasured. The general "QAT beats PTQ" direction is vendor-documented prior art; the intended contribution is the governed-routing task class, the two-scale paired design exposing the signed interaction, and the regime-to-regime exact-output divergence measurement (the last is plausibly adjacent to community cross-quantization regression reports).