vAttention: Dynamic Memory Management for Serving LLMs without PagedAttention

PagedAttention is a popular approach for dynamic memory allocation in LLM serving systems. It enables on-demand allocation of GPU memory to mitigate KV cache fragmentation - a phenomenon that crippled the batch size (and consequently throughput) in prior systems. However, in trying to allocate physical memory at runtime, PagedAttention ends up changing the virtual memory layout of the KV cache from contiguous to non-contiguous. Such a design leads to non-trivial programming and performance overheads.

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Publication Details

Published
2025-02-06
DOI
https://doi.org/10.1145/3669940.3707256
Citations
20
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
43.12
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vAttention: Dynamic Memory Management for Serving LLMs without PagedAttention

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vAttention: Dynamic Memory Management for Serving LLMs without PagedAttention

Jayashree Mohan, Ramachandran Ramjee, Ashish Panwar, Ramya Prabhu, A.K. Nayak
article en
20 citations

Abstract

PagedAttention is a popular approach for dynamic memory allocation in LLM serving systems. It enables on-demand allocation of GPU memory to mitigate KV cache fragmentation - a phenomenon that crippled the batch size (and consequently throughput) in prior systems. However, in trying to allocate physical memory at runtime, PagedAttention ends up changing the virtual memory layout of the KV cache from contiguous to non-contiguous. Such a design leads to non-trivial programming and performance overheads.

Microsoft Research (India) (IN), Indian Institute of Science Bangalore (IN)
Openalex Percentile: Top 0%
Parallel Computing and Optimization Techniques
43.12
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