Summary of Flash Communication: Reducing Tensor Parallelization Bottleneck For Fast Large Language Model Inference, by Qingyuan Li et al.
Flash Communication: Reducing Tensor Parallelization Bottleneck for Fast Large Language Model Inference
by Qingyuan Li, Bo Zhang, Liang Ye, Yifan Zhang, Wei Wu, Yerui Sun, Lin Ma, Yuchen Xie
First submitted to arxiv on: 6 Dec 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 paper introduces Flash Communication, a novel compression technique designed to alleviate the communication bottleneck during inference in large language models (LLMs) that exploit multi-dimensional parallelism across GPU clusters. The method substantially boosts intra-node communication speed by over 3x and reduces time-to-first-token by 2x with minimal loss of model accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a way to make fast inference possible for very large language models on computers with many processors (GPUs) that work together. Right now, this approach can be slow because the data needs to travel between the GPUs, which takes time. The authors developed a new method called Flash Communication that makes it faster and more efficient by compressing the data. This helps reduce the time it takes for the model to make predictions. |
Keywords
» Artificial intelligence » Inference » Token
