Verified70% confidenceFactExact time
The size of the KV cache grows linearly with context length; the KV cache for Qwen 2.5 32B at a 64K context may consume several additional gigabytes of memory.
4
Sources
70%
Confidence
Long-term
Relevance
7/8/2026
First Seen
Sources
Related Claims
UnverifiedThe VRAM consumption of the KV cache is proportional to context length, number of model layers, and number of attention heads; under an 8K context, a typical 27B model may consume an additional 2–4 GB of VRAM.80% similarUnverifiedQuantized KV caches (such as storing KV vectors in FP8 or INT4) can compress the cache size to 1/2 or even 1/4 of the original.79% similarUnverifiedLLaMA-2 7B's KV Cache occupies approximately 2 GB with a 4096-token context, and can surge to over 16 GB when extended to a 32K context77% similarUnverifiedModel weights cannot fill up VRAM entirely, as space must be reserved for the KV cache and inference processes; the KV cache required for a 128K context can reach as high as several GB or even exceed the model weights themselves.75% similarUnverifiedThe size of the KV cache grows linearly with context length, and VRAM consumption increases significantly when processing longer texts.75% similar
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