Unverified50% confidenceCautionExact time
Even when model weights are quantized to Int4, the KV Cache may still use FP16 or BF16, and in long-text high-concurrency scenarios the cache can become the largest consumer of GPU memory.
1
Sources
50%
Confidence
Long-term
Relevance
9/17/2026
First Seen
Sources
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Related Claims
Unverified长上下文场景下2×32G方案因KV Cache无需跨卡读取，稳定性优于4×16G方案73% similarPartially VerifiedA standard FP32 model occupies 4 bytes per parameter, while INT4 quantization compresses this to 0.5 bytes, theoretically achieving an 8x memory saving.70% similarUnverifiedINT4 quantization can further compress the KV Cache to 0.5 bytes, but may require calibration data to ensure accuracy.69% 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.69% similarUnverifiedIt is recommended to keep KV cache precision at 8-bit or 16-bit when VRAM permits, especially in long-context or precise tool-calling scenarios.69% similar
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