这篇论文揭示了LLM量化损伤的分布规律，证明全局量化比局部修复更有效，对模型部署有实际指导意义。
研究团队分析了9个开源模型在4种架构中的量化损伤分布。通过因果混合精度干预方法，发现恢复75%精度差距需要修复约一半层级的参数。在相同精度预算下，全局采用更细粒度量化比选择性修复关键层效果更好，性能提升21-52点。Qwen3-8B模型是唯一例外，其精度恢复高度集中在特定层级。
The Structure of Quantization Damage in LLMs: Why the Next Bit Should Be Spent Globally
Post-training quantization (PTQ) is widely used to reduce the cost of serving large language models (LLMs), but its accuracy cost is uneven and is often tuned per model. We study where quantization damage occurs and how to allocate a small additional precision budget. Using causal mixed-precision intervention as ground truth (raise each layer to 8-bit in turn and measure the accuracy it recovers) across 9 open-weight models in 4 architecture families, we test 3 intuitive hypotheses: that quantization damage lives in task circuits, where the model computes, or in weight statistics. None of them predicts which layers benefit from restored precision. Recovery is instead diffuse: for 8 of 9 models, recovering 75% of the gap takes roughly half the layers; the lone exception, Qwen3-8B, is sharply concentrated. At a matched precision budget, spending it globally on finer quantization granularity beats locally repairing the most recoverable layers for all 8 group-128-compatible models (all but OpenLLaMA, whose width rules out group-128), by 21-52 points, including the concentrated Qwen3-8B. We report 2 secondary findings: the residual is budget-limited (8-bit is near-lossless in our evaluation across RTN, GPTQ, and AWQ), and the location of peak recovery correlates with architecture within a family, though not across families. Within this budget setting, global granularity is a better default than selectively protecting critical layers. More broadly, cheap signals that correlate with quantization damage do not necessarily identify where restoring precision improves accuracy; this must be tested with causal intervention.