English(EN) Energy Consumption Analysis of Discrete Diffusion and Autoregressive Language Models

扩散语言模型比自回归模型能效高出2倍

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-15 04:00

一篇新发表在arXiv上的论文分析了离散扩散模型（DLLMs）和自回归（AR）语言模型在推理过程中的能耗。研究发现，在生成较长序列（200-600个token）时，像LLaDA-8B和Dream-7B这样的扩散模型比Llama-3.1-8B和Qwen 2.5 7B等AR模型能效高约两倍，每token能耗为2.5-2.7焦耳，而AR模型为5.3-5.7焦耳/token。研究还强调，KV缓存优化在较长序列长度下可将AR模型的能耗降低50%-70%。 AI

影响扩散模型为LLM推理提供了更节能的替代方案，有望降低运营成本和环境影响。

排序理由分析语言模型能耗的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 14 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 分析语言模型能耗的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, infra; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Breaking (< 6h); Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Boris Ruf, Marcin Detyniecki · 2026-10-15 04:00

离散扩散模型与自回归语言模型的能耗分析

arXiv:2610.19855v1 Announce Type: new Abstract: As Large Language Models (LLMs) continue to scale, their energy consumption during inference has become a critical concern. While Autoregressive (AR) models are the industry standard, Diffusion Language Models (DLLMs) have emerged a…

本文如何被排名

报道来源 [1]

离散扩散模型与自回归语言模型的能耗分析

相关实体

相关话题