Unverified50% confidenceFactExact time
In traditional LLM inference, the approach of pre-allocating contiguous GPU memory for the KV cache of each request often results in actual GPU memory utilization of less than 50%.
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50%
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Long-term
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9/7/2026
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Unverified传统推理框架中每个请求的 KV Cache 需预分配等长连续显存，显存利用率通常仅有 20%-40%，PagedAttention 可将其提升到接近 100%79% similarUnverified传统 LLM 推理中因需按最大长度预留显存，KV Cache 的实际利用率通常不到 30%73% similarUnverifiedTraditional inference frameworks pre-allocate contiguous KV Cache memory blocks for each request, resulting in an average waste rate as high as 60–80%.71% similarUnverified推理时的KV缓存、激活值缓冲区和中间计算状态会额外占用30%-50%显存70% similarUnverified传统KV Cache分配方案因按最大序列长度预分配连续显存块，导致平均显存利用率仅20%-40%69% similar
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