Summary of Vattention: Dynamic Memory Management For Serving Llms Without Pagedattention, by Ramya Prabhu et al.
vAttention: Dynamic Memory Management for Serving LLMs without PagedAttention
by Ramya Prabhu, Ajay Nayak, Jayashree Mohan, Ramachandran Ramjee, Ashish Panwar
First submitted to arxiv on: 7 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Operating Systems (cs.OS)
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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 In this paper, researchers tackle the challenges faced by Large Language Model (LLM) serving systems when it comes to dynamic memory allocation. Specifically, they focus on the popular PagedAttention approach that aims to mitigate KV cache fragmentation, a problem that limits batch size and throughput in previous systems. While PagedAttention succeeds in allocating GPU memory on demand, its design leads to non-trivial programming and performance overheads due to changes in the virtual memory layout of the KV cache from contiguous to non-contiguous. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to make Large Language Model (LLM) serving systems more efficient. It’s all about memory allocation, which is a big problem right now because it makes the system slow and can’t handle as many tasks at once. The researchers are trying to solve this problem with something called PagedAttention, but it has some side effects that make things harder. They want to find a better way. |
Keywords
» Artificial intelligence » Large language model
