Unverified50% confidenceFactExact time
Prefix caching can save 50%–80% of prefill-stage computation by reusing already-computed KV Cache, and vLLM implements fine-grained cache hit matching through a radix tree structure.
1
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50%
Confidence
Long-term
Relevance
9/6/2026
First Seen
Sources
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Related Claims
UnverifiedvLLM 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.74% similarUnverifiedIn vLLM, each memory page typically stores KV vectors for a fixed number of tokens (e.g., 16), allowing KV Cache from different requests to be stored non-contiguously in physical memory.73% 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.72% similarUnverifiedThe KV Cache memory sharing mechanism can save up to 55% of GPU memory in scenarios such as beam search.71% similarUnverifiedvLLM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% similar
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