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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 in
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