Summary of Clusterkv: Manipulating Llm Kv Cache in Semantic Space For Recallable Compression, by Guangda Liu et al.
ClusterKV: Manipulating LLM KV Cache in Semantic Space for Recallable Compression
by Guangda Liu, Chengwei Li, Jieru Zhao, Chenqi Zhang, Minyi Guo
First submitted to arxiv on: 4 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Performance (cs.PF)
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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 an innovative approach to compressing the key-value (KV) cache of Large Language Models (LLMs), enabling efficient inference on long contexts. The authors propose ClusterKV, a method that recalls tokens at the granularity of semantic clusters, allowing for negligible accuracy loss across various tasks with 32k context lengths and using only a 1k to 2k KV cache budget. The approach achieves up to a 2x speedup in latency and a 2.5x improvement in decoding throughput compared to state-of-the-art (SoTA) recallable KV compression methods, while maintaining or exceeding inference efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding a way to make computer models that understand language work faster and better when they have to look at really long pieces of text. Right now, these models can get slow and less accurate because they need to remember so much information. The authors created a new method called ClusterKV that helps the model remember only what it needs, without losing accuracy. This makes the model run faster and be more efficient. |
Keywords
» Artificial intelligence » Inference
