CAREER: ProTrain: Enabling Efficient Large Language Model Training via Performance, Energy — NSF Award to University of Oregon Eug
The rapid growth of large language models (LLMs) has enabled major advances in artificial intelligence (AI), including systems that assist with writing, coding, education, and decision-making. However, training these models demands enormous computing resources, creating significant challenges across multiple dimensions
| Award title | CAREER: ProTrain: Enabling Efficient Large Language Model Training via Performance, Energy |
|---|---|
| Award ID | 2540555 |
| Awardee | University of Oregon Eugene |
| City | EUGENE |
| State | OR |
| Amount obligated | $308,232 |
| Principal investigator | Jieyang Chen |
| Program | Software & Hardware Foundation |
| Start date | 09/01/2026 |
| Abstract | The rapid growth of large language models (LLMs) has enabled major advances in artificial intelligence (AI), including systems that assist with writing, coding, education, and decision-making. However, training these models demands enormous computing resources, creating significant challenges across multiple dimensions, including model quality, training time, energy efficiency, and reliability. Although many optimization techniques have been proposed, most focus on only one or a few aspects of t |
| Source | NSF Awards |
$799/mo
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