AI & Machine Learning

Stop Wasting GPU Memory: A Deep Dive Into vLLM's PagedAttention

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vLLM introduces PagedAttention, a memory management technique for large language models that significantly reduces GPU memory waste. By partitioning KV caches into non-contiguous blocks, vLLM minimizes over-provisioning and fragmentation, boosting server throughput up to 4x on the same hardware. This innovation is crucial for developers aiming to optimize resource usage in LLM deployments.

Read the full article at Towards AI - Medium

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