Summary of Llm Inference Unveiled: Survey and Roofline Model Insights, by Zhihang Yuan et al.
LLM Inference Unveiled: Survey and Roofline Model Insights
by Zhihang Yuan, Yuzhang Shang, Yang Zhou, Zhen Dong, Zhe Zhou, Chenhao Xue, Bingzhe Wu, Zhikai Li, Qingyi Gu, Yong Jae Lee, Yan Yan, Beidi Chen, Guangyu Sun, Kurt Keutzer
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper presents a comprehensive survey of efficient Large Language Model (LLM) inference techniques, analyzing various methods and introducing a framework based on the roofline model to understand the practical challenges of deploying LLMs on hardware devices. The authors identify bottlenecks in memory usage and computation requirements, providing insights into how to choose the right hardware for specific models. The survey covers recent advancements in model compression (Knowledge Distillation and Quantization), algorithm improvements (Early Exit and Mixture-of-Expert), and system-level enhancements. By applying the roofline model, the authors demonstrate the impact of these methods on memory access and computation, making this resource valuable for researchers and practitioners seeking to deepen their understanding of efficient LLM deployment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to understand how to make computers work faster with big language models. This paper takes a step back and looks at all the different ways people have tried to speed up these models, and sorts them into categories. They found that some methods are good for memory usage, while others are better for computation. The authors even created a special tool called LLM-Viewer to help researchers understand how these different methods work together. This paper is important because it helps us see the bigger picture of how language models can be used in practice, and what tools we need to make them run efficiently. |
Keywords
» Artificial intelligence » Inference » Knowledge distillation » Large language model » Model compression » Quantization
