Unverified50% confidenceTradeoffExact time
The longer the context length, the greater the KV Cache usage, and inference speed typically decreases.
1
Sources
50%
Confidence
Long-term
Relevance
10/8/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedInference speed drops sharply once the context exceeds 128K, due to the ballooning KV cache size and the quadratic growth of attention computation time with sequence length79% similarUnverified只有在LLM推理比embedding加向量检索慢数十倍以上时，语义缓存才能同时实现降本与提速78% similarUnverifiedCombining quantization with memory offloading theoretically allows loading models far exceeding capacity in extremely low-VRAM environments, but the data transfer introduces additional latency, and actual inference speed is often only a fraction of that achieved with pure VRAM operation.76% similarUnverifiedThe KV Cache, which grows dynamically during inference, continuously occupies VRAM and may suddenly trigger an OOM crash during long conversations.76% similarUnverifiedIn ultra-long context inference scenarios, the performance bottleneck is often not compute but the KV cache itself.76% similar
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