# Disaggregated Quantization

The work proposes disaggregated quantization (DQ), which specializes computation formats, weights and storage placement to the prefill and decode phases of LLM inference, together with the QADD training method; on Qwen 3 and Gemma 3, removing activation quantization only on decode improves accuracy on decode-heavy tasks without increasing inference cost, training separate NVFP4 prefill weights accelerates prefill while raising accuracy at 2–3-bit decode, and on Qwen3.8-27B 1-bit GGUF decoders it more than doubles MMLU-Pro and MMMU-Pro accuracy, with offloaded disaggregated prefill streaming prefill weights from SSD delivering a time-to-first-token speedup over the weight-only baseline at 8K context.

The work proposes disaggregated quantization (DQ), which specializes computation formats, weights and storage placement to the prefill and decode phases of LLM inference, together with the QADD training method; on Qwen 3 and Gemma 3, removing activation quantization only on decode improves accuracy on decode-heavy tasks without increasing inference cost, training separate NVFP4 prefill weights accelerates prefill while raising accuracy at 2–3-bit decode, and on Qwen3.8-27B 1-bit GGUF decoders it more than doubles MMLU-Pro and MMMU-Pro accuracy, with offloaded disaggregated prefill streaming prefill weights from SSD delivering a time-to-first-token speedup over the weight-only baseline at 8K context.