Unverified50% confidenceFactExact time
The autoregressive generation process of large language models requires frequent reading of the KV Cache (key-value cache), placing extremely high demands on memory bandwidth and capacity.
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Long-term
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9/12/2026
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Related Claims
UnverifiedWhen large models generate long texts, they need to cache the key-value pairs (KV Cache) of all previous tokens, and the memory footprint grows linearly with context length, making it very easy for this to become a memory bottleneck in high-concurrency scenarios.79% similarUnverified自回归解码阶段每生成一个新token都需读取完整KV Cache，使解码阶段几乎总是受限于内存带宽而非计算能力78% similarUnverifiedFor million-token-level contexts, KV Cache can consume tens or even hundreds of gigabytes of GPU memory, becoming the primary bottleneck for long-context inference.77% similarUnverifiedIn large model API call scenarios, cache hit rate refers to the reuse ratio of the KV Cache (key-value cache), where the model can directly read the attention key-value pairs of already-processed tokens from the cache.75% similarUnverified大语言模型动辄数十GB，其加载时间和内存占用对传统弹性伸缩策略提出新挑战74% similar
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