LLM训练能耗通过GPU时钟调整降低14%

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-10 11:00

特温特大学的研究人员开发了一种方法，可将大型语言模型训练的能耗降低高达14%。该技术被称为动态电压频率调整（DVFS），涉及智能地调整GPU计算核心和内存的时钟频率。通过对每个内核的频率进行微调，研究人员在不影响训练速度的情况下实现了显著的节能。 AI

影响降低了LLM训练巨大的能耗，可能降低成本和环境影响。

排序理由学术研究论文，提出了一种提高LLM训练能效的新技术。[lever_c_demoted from research: ic=1 ai=1.0]

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本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 学术研究论文，提出了一种提高LLM训练能效的新技术。[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: infra, paper; 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: 118 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

IEEE Spectrum — AI TIER_1 English(EN) · Dina Genkina · 2026-06-10 11:00

Timing Trick Cuts Energy Used in LLM Training by Up to 14 Percent

<img src="https://spectrum.ieee.org/media-library/abstract-illustration-of-a-pixelated-cube-leaking-vibrant-colors-onto-a-dark-grid.jpg?id=66884338&width=1245&height=700&coordinates=0%2C187%2C0%2C188" /><br /><br /><p><a href="https://openai.com/" rel="noopener norefe…