Unverified50% confidenceFactExact time
Enabling Flash Attention and setting the cache type to Q8 can reduce KV Cache VRAM usage by about half, with very little quality loss.
1
Sources
50%
Confidence
Long-term
Relevance
10/17/2026
First Seen
Sources
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Related Claims
UnverifiedGQA 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.77% similarUnverifiedChoosing 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.74% similarUnverifiedReducing the KV cache from FP16 to INT8 saves 50% of VRAM, and reducing it to INT4 saves 75%.73% similarUnverifiedLFM2.5-24B-A2B's KV cache grows far more slowly than a pure Transformer of the same depth, because only 10 layers are attention layers, making the memory overhead of long contexts significantly lower.72% similar
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