arxiv
PublishedSeptember 2, 2026 at 4:00 AM
—neutral
The Structure of Quantization Damage in LLMs: Why the Next Bit Should Be Spent Globally
Publisher summary· verbatim
arXiv:2609.01587v1 Announce Type: cross Abstract: 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
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Originally published on arxiv ↗