Unverified50% confidenceFactExact time
During 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.
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50%
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Long-term
Relevance
9/3/2026
First Seen
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Related Claims
VerifiedThe 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.76% similarUnverifiedAutoregressive models use KV Cache to cache the Key and Value matrices of historical tokens, with memory usage growing linearly with sequence length.75% similarUnverifiedThe available memory and VRAM of a local model directly determine the obtainable context window length, because inference requires maintaining a KV Cache in VRAM.75% similarVerifiedKV Cache accelerates inference by avoiding redundant computation of the Key and Value for already-generated tokens.72% similarUnverifiedEach inference requires loading model weights into GPU memory and generating responses token by token.70% similar
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