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Disaggregated Quantization: Specializing LLM Prefill and Decode
arXiv cs.LG — Machine Learning
Factual evidence
What the source reports
Researchers propose disaggregated quantization, tailoring precision and weights separately for LLM prefill and decode phases.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.LG — Machine Learning · RESEARCH
- Published by source
- 23 September 2026
- Collected by OneBench
- 24 Sept 2026, 03:02 UK
- Original headline
- Disaggregated Quantization: Specializing LLM Prefill and Decode ↗
Stored source excerpt
arXiv:2609.26333v1 Announce Type: new Abstract: Prefill and decode reward different approaches to quantization: low-precision arithmetic accelerates prompt processing, while compact weights reduce memory traffic during…
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