---
id: 20260816-T0-01
title: "Dual-Flow Transformer：分开预填充和生成计算，推理成本降低"
title_en: "Dual-Flow Transformers Decouple Prefill Path from Decode Computation, Cutting Inference Costs"
url: https://ai.daily.yangsir.net/daily/20260816-T0-01
issue_date: 2026-08-16
publish_date: 2026-08-15T04:00:00.000Z
category: research
source_name: "arXiv cs.AI"
source_url: https://arxiv.org/abs/2608.12385
---

# Dual-Flow Transformer：分开预填充和生成计算，推理成本降低

一篇来自arXiv的新论文提出了Dual-Flow Transformers架构，将LLM推理的预填充阶段（prefill）和生成阶段（decode）的计算路径解耦。预填充是并行计算密集型，生成是顺序内存密集型，分开处理可以让硬件利用率更高。随着请求量增长，推理成本比训练成本更关键，该架构直接瞄准这一痛点，有望降低大规模部署的累积推理开销。

## English Version

**Dual-Flow Transformers Decouple Prefill Path from Decode Computation, Cutting Inference Costs**

A new arXiv paper proposes Dual-Flow Transformers, decoupling the prefill path from decode computation in LLM inference. Prefill is parallel and compute-bound, while decode is sequential and memory-bound; separating them improves hardware utilization. As cumulative inference costs overtake one-time training costs at scale, this architecture targets that bottleneck to reduce deployment overhead.

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**来源**：[arXiv cs.AI](https://arxiv.org/abs/2608.12385)

**详情页**：https://ai.daily.yangsir.net/daily/20260816-T0-01

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