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Summary of Cost-efficient Large Language Model Serving For Multi-turn Conversations with Cachedattention, by Bin Gao et al.


Cost-Efficient Large Language Model Serving for Multi-turn Conversations with CachedAttention

by Bin Gao, Zhuomin He, Puru Sharma, Qingxuan Kang, Djordje Jevdjic, Junbo Deng, Xingkun Yang, Zhou Yu, Pengfei Zuo

First submitted to arxiv on: 23 Mar 2024

Categories

  • Main: Computation and Language (cs.CL)
  • Secondary: Machine Learning (cs.LG)

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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
A novel attention mechanism called CachedAttention is proposed in this paper to improve the efficiency of large language models (LLMs) serving engines executing multi-turn conversations. The existing engines are inefficient due to the need to repeatedly compute key-value (KV) caches of historical tokens, resulting in high serving costs. The proposed method maintains a hierarchical KV caching system that leverages cost-effective memory/storage mediums to save KV caches for all requests. To reduce KV cache access overheads from slow mediums, CachedAttention employs layer-wise pre-loading and asynchronous saving schemes to overlap the KV cache access with GPU computation. Additionally, the method ensures that KV caches are placed in the fastest hierarchy by employing scheduler-aware fetching and eviction schemes. The proposed approach also enables saved KV caches to remain valid by decoupling positional encoding and truncating KV caches. Experimental results demonstrate a significant decrease in time to the first token (TTFT) of up to 87%, an improvement in prompt prefilling throughput of up to 7.8 times for multi-turn conversations, and a reduction in end-to-end inference cost of up to 70%. The paper’s contributions have far-reaching implications for the development of efficient LLM serving engines.
Low GrooveSquid.com (original content) Low Difficulty Summary
Large language models (LLMs) are powerful tools that can interact with humans through multi-turn conversations. However, current LLM serving engines are not very efficient because they need to repeatedly compute information from previous conversations. This makes them slow and expensive to use. The authors of this paper propose a new way to do this computation called CachedAttention. It works by saving the most important information from previous conversations in a special kind of memory that can be quickly accessed later on. This saves time and makes the engine more efficient. The authors tested their method with real-world data and found that it made the engine up to 70% faster and cheaper. This is an important step forward for LLMs and could lead to new applications in areas like customer service chatbots.

Keywords

» Artificial intelligence  » Attention  » Inference  » Positional encoding  » Prompt  » Token