Summary of Llm-pq: Serving Llm on Heterogeneous Clusters with Phase-aware Partition and Adaptive Quantization, by Juntao Zhao et al.
LLM-PQ: Serving LLM on Heterogeneous Clusters with Phase-Aware Partition and Adaptive Quantization
by Juntao Zhao, Borui Wan, Yanghua Peng, Haibin Lin, Chuan Wu
First submitted to arxiv on: 2 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 proposes a novel system called LLM-PQ that enhances the serving efficiency of large-scale language models (LLMs) on heterogeneous GPU clusters. The authors address the high resource demand and cost associated with running these models by introducing adaptive model quantization and phase-aware partitioning strategies. The proposed system, which combines mixed-precision model quantization with phase-aware model partitioning and micro-batch sizing, achieves a significant 2.88x (2.26x on average) throughput improvement in inference while meeting user-specified model quality targets. The authors demonstrate the effectiveness of LLM-PQ through extensive experiments on production inference workloads in 11 different clusters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMs have made huge progress in various tasks, but running them requires a lot of computing power and money. This paper suggests a way to make it more efficient by using computers with different abilities. Instead of just putting the model on one very powerful computer, they divide it into smaller parts and spread it across many computers that are not all equally strong. They also change how the model works so it uses less energy. This helps the computers work together better and makes the process faster. The result is that it’s 2.88 times (on average) faster than other ways of doing this. |
Keywords
* Artificial intelligence * Inference * Precision * Quantization
