Summary of Xkv: Personalized Kv Cache Memory Reduction For Long-context Llm Inference, by Weizhuo Li et al.
XKV: Personalized KV Cache Memory Reduction for Long-Context LLM Inference
by Weizhuo Li, Zhigang Wang, Yu Gu, Ge Yu
First submitted to arxiv on: 8 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 proposes a novel approach to optimizing the memory usage of Large Language Models (LLMs) during inference tasks. By leveraging the observation that cached data have varying impacts on accuracy across different network layers, the authors develop personalized cache allocation strategies to reduce memory consumption while maintaining comparable accuracy. The approach is validated through experimental and theoretical analyses, and simulated as a combinatorial optimization problem to find the global optimal solution. To further accelerate inference, the authors also introduce a lightweight variant of the LLM model and devise mini- and sampling-based inference methods. Experimental results on real-world datasets demonstrate significant memory reduction (61.6%), improved computational efficiency (2.1x), and increased throughput (up to 5.5x). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make language models more efficient by reducing how much memory they use while still being accurate. It does this by recognizing that some parts of the model are more important than others, and adjusting the way it stores information accordingly. This can be especially helpful for tasks that require a lot of processing power, like understanding long texts or generating text on the fly. |
Keywords
» Artificial intelligence » Inference » Optimization
