Unverified50% confidenceFactExact time
In long-context or multi-turn dialogue scenarios, the KV cache occupies a large amount of VRAM, and speed may drop off a cliff.
1
Sources
50%
Confidence
Long-term
Relevance
10/14/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe longer the context length, the greater the KV Cache usage, and inference speed typically decreases.76% similarUnverifiedThe KV Cache, which grows dynamically during inference, continuously occupies VRAM and may suddenly trigger an OOM crash during long conversations.76% similarUnverifiedA longer context makes model conversations more coherent, but also consumes more tokens and may hit the model's maximum input length limit.69% similarUnverifiedThe longer the context, the more VRAM the KV Cache occupies proportionally; manually increasing the context length requires ensuring sufficient remaining VRAM, otherwise an out-of-memory (OOM) error may occur and cause a crash.69% similarUnverified长文档处理中，KV Cache机制导致显存占用随上下文长度近似平方级增长68% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1022712API
curl https://kongchang.com/api/v1/knowledge/claims/1022712MCP
get_claim(id=1022712)