Paged Attention from First Principles: A View Inside vLLM | Hamza's Blog
Large language models (LLMs) are trained in highly parallel, compute-bound workloads, but serving them is very different: inference is memory-bound and seque...
Paged Attention from First Principles: A View Inside vLLM – Hamza's Blog Paged Attention from First Principles: A View Inside vLLM 11 Sep, 2025 Large language models (LLMs) are trained in highly parallel, compute-bound workloads, but serving them is very different: inference is memory-bound and sequential. Optimising inference is critical because no one will use a chatbot that lags behind typing or a tool that takes minutes to respond. On the business side, squeezing more out of each GPU directly reduces costs and maximises ROI. A key bottleneck is the key-value (KV) cache , which stores conte
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