Unverified60% confidenceSolutionExact time
Choosing Q4_0 for KV cache quantization can compress the cache size to approximately one-quarter of FP16, with far less impact on output quality than model weight quantization.
2
Sources
60%
Confidence
Long-term
Relevance
9/1/2026
First Seen
Sources
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Related Claims
UnverifiedReducing the KV cache from FP16 to INT8 saves 50% of VRAM, and reducing it to INT4 saves 75%.79% similarVerifiedQuantized 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% 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.76% similarUnverifiedINT4 quantization can further compress the KV Cache to 0.5 bytes, but may require calibration data to ensure accuracy.76% similarUnverifiedGQA groups attention heads to share KV projections, which can compress the KV cache to 1/4 or less of the original size with almost no loss in model quality.76% similar
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