The article discusses how production systems using large language models face challenges with GPU memory allocation for serving requests. It highlights the issue of internal fragmentation caused by pre-reserving KV-cache space, which can lead to significant memory waste. To address this, the vLLM system introduces the PagedAttention model, which dynamically allocates KV-cache memory in fixed-size blocks as needed, thereby improving memory efficiency and throughput in serving large language models. This innovative approach mitigates redundancy and performance overhead commonly seen in traditional models.
Prior reservation wastes memory even if the context lengths are known in advance, demonstrating the inefficiencies in current KV-cache allocation strategies in production systems.
PagedAttention dynamically allocates memory for the KV-cache, splitting it into fixed-sized blocks and allocating only necessary memory per request, mitigating fragmentation issues.
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