Memory-efficient tensor parallelism for long-sequence Transformer training
Transformer-based models like large language models (LLMs) have attracted significant attention in recent years due to their superior performance. A long sequence of input tokens is essential for industrial LLMs to provide better user services. However, memory consumption increases quadratically wit...
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| Vydané v: | Frontiers of information technology & electronic engineering Ročník 26; číslo 5; s. 770 - 787 |
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| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Hangzhou
Zhejiang University Press
01.05.2025
Springer Nature B.V |
| Predmet: | |
| ISSN: | 2095-9184, 2095-9230 |
| On-line prístup: | Získať plný text |
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