Unverified50% confidenceFactExact time
使用非GQA架构(KV头数等于查询头数)在相同条件下的KV缓存是GQA的4倍
1
Sources
50%
Confidence
Long-term
Relevance
8/12/2026
First Seen
Sources
Related Claims
UnverifiedGQA allows multiple query heads to share the same set of KV heads, compressing the KV cache size to 1/4 to 1/8 of the original.79% 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.70% similarUnverifiedQ4_K_M is the de facto standard choice for users with 8 GB of VRAM, significantly improving output quality compared to Q4_0 with almost no increase in VRAM usage.65% similarUnverifiedThe GGUF format supports multiple precision levels such as 4-bit and 8-bit, and can compress the memory footprint of a 7-billion-parameter model from approximately 14 GB (FP16) to approximately 4 GB (Q4_K_M quantization), with inference accuracy loss typically within 1–3%.64% similarUnverifiedvLLM的PagedAttention将KV缓存切分为固定大小物理块(通常16个token),使显存利用率从不足40%提升至接近90%64% similar
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