Unverified50% confidenceFactExact time
Quantization only compresses the weights and does not change the context length, so a 1-million-token KV cache still consumes a large amount of additional memory.
1
Sources
50%
Confidence
Long-term
Relevance
10/16/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedQuantized 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.76% similarVerifiedModel 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.72% similarUnverifiedIn cache-intensive, long-context, multi-turn agent workflows, the incremental cost of switching to a more powerful model is diluted by cache reads and does not necessarily mean costs double.71% similar
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