Summary of Pie: Pooling Cpu Memory For Llm Inference, by Yi Xu et al.
Pie: Pooling CPU Memory for LLM Inference
by Yi Xu, Ziming Mao, Xiangxi Mo, Shu Liu, Ion Stoica
First submitted to arxiv on: 14 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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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 This paper tackles a crucial challenge in large language models (LLMs), which have transformed natural language processing and AI analysis. As LLMs continue to grow, their increasing size and memory demands pose significant hurdles. One common solution is to offload data to CPU memory, but traditional GPU-CPU memory swapping techniques often lead to higher latency and lower throughput. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem with really smart computers that understand language. These super-smart computers are getting bigger and need more space to remember things. When they get too full, they can move some of the information to other parts of the computer. But this doesn’t work very well because it makes the computer slower and less efficient. |
Keywords
* Artificial intelligence * Natural language processing
