Unverified60% confidenceFactExact time
Autoregressive models use KV Cache to cache the Key and Value matrices of historical tokens, with memory usage growing linearly with sequence length.
2
Sources
60%
Confidence
Long-term
Relevance
7/20/2026
First Seen
Sources
Related Claims
UnverifiedDuring inference, each token requires maintaining a KV cache (Key-Value cache), whose GPU memory usage is proportional to sequence length and the number of model layers.75% similarUnverifiedThe context cache (KV Cache) is the memory area that stores the Key and Value vectors of previous tokens during inference of Transformer-type models, and its size grows linearly with context length, batch size, and the number of model layers.75% similarUnverifiedKV Cache技术允许模型在自回归生成时复用历史token的注意力Key-Value矩阵计算结果，使长对话推理延迟从O(n²)降低至接近线性74% similarVerifiedThe autoregressive generation process of reasoning models is strictly sequential; KV Cache VRAM usage grows linearly with the length of the thinking token sequence, making memory bandwidth the performance bottleneck.74% similarUnverifiedKVCache is a mechanism in the Transformer architecture that caches the Key and Value vectors of all previous tokens to avoid redundant computation, with its memory footprint growing linearly with context length.73% similar
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