C
Evidence Grade C
Use with Hedging
Aging
114
114 — memory compression ratio for Infini-Transformer architecture (2024)
Source:
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Curated by Aaron Agius
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Source verified
Evidence graded
Value
114
Unit
Multiple
Data Vintage
2024
Entity
Study Type
Unknown
Trend
Stable
Evidence Quality
C
Moderate quality evidence
Useful directional data with some methodological gaps. Treat as indicative rather than definitive — corroborate before citing in formal research.
Sources (2)
huxiu.com
Primary
Tier A
https://www.huxiu.com/article/2896806.html ↗
Source Article
本文来自微信公众号：AINLPer（ID：gh_895a8687a10f），作者：ShuYini，原文标题：《Google | 提出Infini-Transformer架构，可让LLMs处理无限长上下文，内存节约114倍！》，题图来自：视觉中国
引言
为解决大模型（LLMs）在处理超长输入序列时遇到的内存限制问题，本文作者提出了一种新型架构：Infini-Transformer，它可以在有限内存条件下，让基于Transformer的大语言模型（LLMs）高效处理无限长的输入序列。实验结果表明：Infini-Transformer在长上下文语言建模任务上超越了基线模型，内存最高可节约114倍。
…
Also cited by
硅星人Pro (Silicon Planet Pro)
https://www.huxiu.com/article/2909697.html
2026-05-04
Original Research
Google
· Original study ↗
Methodology
Experimental results on PG19 and Arxiv-math datasets for long context language modeling tasks
Study: Unknown
Cite this stat
Suggested hedging: based on claims without stated methodology, with unverified sample sizes...
Plain Text
Infini-Transformer architecture: 114 memory compression ratio. Source: huxiu.com (2024). Via Lighthouse Research Data — https://lighthousedata.io/data/infini-transformer-memory-savings-114x
HTML Embed
<blockquote cite="https://lighthousedata.io/data/infini-transformer-memory-savings-114x" style="border-left:3px solid #2563eb;padding:12px 16px;margin:16px 0;font-family:system-ui,sans-serif;background:#f0f4ff;"><strong>114</strong> — memory compression ratio for Infini-Transformer architecture<br><small>Source: huxiu.com (2024) · <a href="https://lighthousedata.io/data/infini-transformer-memory-savings-114x" target="_blank" rel="noopener">Lighthouse Data</a></small></blockquote>
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