Unverified50% confidenceFactExact time
As context length increases, the memory footprint of the KV Cache grows dramatically, becoming a key factor limiting concurrency.
1
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50%
Confidence
Long-term
Relevance
9/11/2026
First Seen
Sources
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Related Claims
UnverifiedWhen large models generate long texts, they need to cache the key-value pairs (KV Cache) of all previous tokens, and the memory footprint grows linearly with context length, making it very easy for this to become a memory bottleneck in high-concurrency scenarios.79% similarUnverifiedThe longer the context, the larger the KV Cache that needs to be stored and retrieved, and GPU memory consumption grows linearly77% similarUnverifiedIn autoregressive inference, the memory footprint of the KV cache grows linearly with sequence length and may equal or exceed the weight volume in long-context scenarios.75% similarVerifiedWhen the context length is extended to one million tokens, the KV Cache itself can occupy tens of gigabytes of VRAM.73% similarUnverifiedWhen the context length is extended from 4K to 8K, the VRAM occupied by the KV Cache nearly doubles68% similar
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