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Paged Attention in Large Language Models LLMs

MarkTechPost / 3/25/2026

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Key Points

  • The article argues that, at large scale, GPU memory—not compute—is the main bottleneck for running LLMs because each request maintains a per-token KV cache.
  • Traditional serving allocates a fixed, maximum-sequence-length memory block per request, causing substantial wasted space and reducing concurrency.
  • It introduces “Paged Attention” as a technique intended to improve memory utilization by restructuring how KV cache memory is managed across requests.
  • The core takeaway is that more efficient KV-cache allocation can enable higher throughput and better hardware utilization when serving LLMs.
  • Overall, the post frames Paged Attention as an engineering-oriented research direction focused on scaling inference under real memory constraints.

When running LLMs at scale, the real limitation is GPU memory rather than compute, mainly because each request requires a KV cache to store token-level data. In traditional setups, a large fixed memory block is reserved per request based on the maximum sequence length, which leads to significant unused space and limits concurrency. Paged Attention […]

The post Paged Attention in Large Language Models LLMs appeared first on MarkTechPost.

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