Coordinated Mixed-Precision KV Cache Quantization for Efficient LLM-based Multi-agent Systems

Weizhong Huang, Jinchao Zhang, Xiawu Zheng

LLM-based multi-agent systems solve complex tasks through collaboration, but their growing, simultaneously resident key-value (KV) caches create a memory bottleneck. Applying single-model quantization policies independently ignores differences in agents' precision needs, potentially degrading collective reasoning. We introduce Coordinated Mixed-Precision KV Cache Quantization (CoMixKV), which combines online sensitivity estimation with joint agent-layer bit-width allocation under a shared memory budget. To our knowledge, CoMixKV is the first mixed-precision KV-cache quantization framework to jointly allocate precision across simultaneously resident agent caches under such a budget. The estimator derives sensitivity from a local attention-error bound using recent queries, keys, and values, without calibration data, trial quantization, or additional model passes. A shared controller combines these scores with remaining quantizable capacity and byte-level storage costs to minimize total estimated error. Allocations are refreshed as agents start or finish and new observations become available. Selected widths apply only to newly quantized blocks; recent states retain native precision…