Unverified50% confidenceFactExact time
When 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.
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50%
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Long-term
Relevance
9/6/2026
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Related Claims
Unverified上下文越长，模型需要缓存的 KV Cache 越大，KV Cache 使模型生成新 Token 时无需重新计算所有历史 Token 的注意力权重77% 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76% similarUnverifiedIn autoregressive inference, the memory footprint of the KV cache grows linearly with sequence length and may equal or exceed the weight volume in long-context scenarios.75% 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% similarUnverifiedvLLM compresses the KV Cache memory fragmentation rate from above 60% to near zero through PagedAttention technology, and can increase inference throughput by several times in high-concurrency scenarios.72% similar
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