Unverified50% confidenceFactExact time
For a model with L layers and hidden dimension d, the KV Cache footprint for a single token is 2×L×d×sizeof(float16) bytes.
1
Sources
50%
Confidence
Long-term
Relevance
8/7/2026
First Seen
Sources
vLLM架构深度解析：PagedAttention如何实现高吞吐LLM推理
Related Claims
UnverifiedvLLM引入PagedAttention技术将KV Cache分割为固定大小物理块按需分配，而llama.cpp采用预分配策略在模型加载时一次性保留完整KV Cache空间71% similarUnverifiedIn long-context scenarios (such as models with a 128K token window), the GPU memory occupied by the KV Cache may exceed that of the model weights themselves.69% similarUnverifiedvLLM的PagedAttention将KV Cache划分为固定大小物理块，通过块表实现地址映射，将碎片化率从60-80%降至不足4%，吞吐量提升可达24倍68% similarUnverified长上下文场景下2×32G方案因KV Cache无需跨卡读取，稳定性优于4×16G方案65% similarUnverifiedIn addition to the model itself, running a large model requires reserving 1–2 GB of VRAM for the KV Cache (key-value cache).65% similar
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