Summary of Training and Serving System Of Foundation Models: a Comprehensive Survey, by Jiahang Zhou et al.
Training and Serving System of Foundation Models: A Comprehensive Survey
by Jiahang Zhou, Yanyu Chen, Zicong Hong, Wuhui Chen, Yue Yu, Tao Zhang, Hui Wang, Chuanfu Zhang, Zibin Zheng
First submitted to arxiv on: 5 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract discusses the rapid growth of foundation models (e.g., ChatGPT, DALL-E) in artificial general intelligence areas like natural language processing and visual recognition. As these massive models require significant resources for training and serving, efficient strategies are crucial to address challenges like computing power, memory consumption, and bandwidth demands. To this end, the paper surveys state-of-the-art methods for training and serving foundation models from various perspectives, providing a detailed categorization of network, computing, and storage aspects. The work also summarizes challenges and offers insights on future development directions, aiming to provide a solid theoretical basis and practical guidance for researchers and applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Foundation models like ChatGPT and DALL-E are very smart computers that can do many things, like understand language and recognize pictures. These models have gotten so good that big tech companies are investing a lot of money and time in them. However, training and using these models requires a lot of computer power, memory, and internet bandwidth, which is a challenge. To solve this problem, researchers are working on new ways to train and use foundation models efficiently. This paper looks at the different methods that have been developed so far and categorizes them into different types. It also talks about the challenges that come with using these models and where they might go in the future. |
Keywords
» Artificial intelligence » Natural language processing
