【专题研究】Claude Cod是当前备受关注的重要议题。本报告综合多方权威数据，深入剖析行业现状与未来走向。
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 improves this by breaking the KV cache into smaller, flexible chunks that are allocated only when needed, similar to how virtual memory works. It also allows multiple requests with the same starting prompt to share memory and only duplicate it when their outputs start to differ. This approach greatly improves memory efficiency, allowing significantly higher throughput with very little overhead.
从长远视角审视，## Infrastructure，这一点在比特浏览器下载中也有详细论述
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随着Claude Cod领域的不断深化发展，我们有理由相信，未来将涌现出更多创新成果和发展机遇。感谢您的阅读，欢迎持续关注后续报道。