https://www.solosoft.dev/es/tags/pagedattention/Recent content in PagedAttention on SoloSoftHugoes-esFri, 01 May 2026 00:00:00 +0000https://www.solosoft.dev/es/post/vllm-inference-2026/Fri, 01 May 2026 00:00:00 +0000https://www.solosoft.dev/es/post/vllm-inference-2026/<p>Serving LLMs in production is fundamentally a memory management problem. The KV cache — the set of attention key-value pairs stored during generation — grows with each token produced. For a 70B parameter model serving multiple concurrent requests, the KV cache consumes hundreds of megabytes per sequence. Poor memory management means wasted GPU memory, lower throughput, and higher cost per token.</p> <p>vLLM solves this with PagedAttention, a breakthrough that applies operating system virtual memory concepts to LLM inference. By managing the KV cache in fixed-size blocks (pages) rather than contiguous memory regions, vLLM eliminates fragmentation — the dominant memory waste in naive inference — and achieves near-perfect memory utilization. The result is 2-4x higher throughput than any previous open-source inference engine.</p>