Unverified50% confidenceFactExact time
传统LLM推理引擎在处理KV Cache时存在严重的内存碎片问题，对于13B参数模型，单个请求的KV Cache可能占用数GB内存，而实际利用率可能低于50%
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
7/2/2026
First Seen
Valid until: 9/30/2026
Sources
vLLM Deep Dive: How PagedAttention Enables High-Throughput LLM Inference
githubvllm-project6/6/2026
Related Claims
Unverified对于70B参数的模型，8K上下文的KV Cache可能额外占用数GB显存77% similarUnverified数千亿参数模型处理128K上下文窗口时，KV Cache可能占据数十GB显存77% similarUnverified在长上下文场景（如128K token窗口模型）中，KV Cache的显存占用可能超过模型权重本身76% similarUnverified对于拥有32层、32头注意力的典型7B模型，每个token需约0.5MB显存存储KV Cache，生成1000个token则需额外约500MB显存74% similarVerifiedKV Cache的显存占用随序列长度线性增长，700亿参数采用GQA的模型处理10万Token时KV Cache占用可能高达数十GB73% similar
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