Feb. 6, 2024, 5:46 a.m. | Zirui Liu Jiayi Yuan Hongye Jin Shaochen Zhong Zhaozhuo Xu Vladimir Braverman Beidi Chen Xia H

cs.LG updates on arXiv.org arxiv.org

Efficiently serving large language models (LLMs) requires batching many requests together to reduce the cost per request. Yet, the key-value (KV) cache, which stores attention keys and values to avoid re-computations, significantly increases memory demands and becomes the new bottleneck in speed and memory usage. This memory demand increases with larger batch sizes and longer context lengths. Additionally, the inference speed is limited by the size of KV cache, as the GPU's SRAM must load the entire KV cache from …

cs.cl cs.lg cs.pf

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