Unverified50% confidenceFactExact time
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
1
Sources
50%
Confidence
Long-term
Relevance
7/22/2026
First Seen
Sources
ChatGPT接入本地大模型：零成本无限Token完整教程
Related Claims
UnverifiedKV Cache（键值缓存）用于避免自回归生成时对历史 token 的重复计算，但长上下文场景下预填充阶段需占用大量 HBM 显存带宽，拉长首 token 延迟（TTFT）83% 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.80% similarVerifiedThe VRAM usage of KV Cache grows linearly with sequence length; for a 70-billion-parameter model using GQA, the KV Cache usage can reach tens of GB when processing 100,000 tokens.74% similarUnverifiedPrompt Caching reduces latency and token costs by reusing the KV Cache of identical prefixes to skip redundant computation.72% similarUnverifiedFor a typical 7B-parameter model, the KV Cache per 1K tokens requires approximately 0.5–1 GB of VRAM.72% similar
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