IMPQ: Interaction-Aware Layerwise Mixed Precision Quantization for LLMs

• URL: http://arxiv.org/abs/2509.15455v1
• Date: Thu, 18 Sep 2025 21:59:40 GMT
• Title: IMPQ: Interaction-Aware Layerwise Mixed Precision Quantization for LLMs
• Authors: Junchen Zhao, Ali Derakhshan, Dushyant Bharadwaj, Jayden Kana Hyman, Junhao Dong, Sangeetha Abdu Jyothi, Ian Harris,
• Abstract summary: Mixed-precision quantization offers a compelling solution, but existing methods struggle when the average precision drops below four bits.<n>In this paper, we propose two innovations to address these limitations.<n>First, we frame the mixed-precision quantization problem as a cooperative game among layers and introduce Shapley-based Progressive Quantization Estimation (SPQE)<n>Second, we propose Interaction-aware Mixed-Precision Quantization (IMPQ) which translates these Shapley estimates into a binary quadratic optimization formulation.
• Score: 4.655407920049974
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) promise impressive capabilities, yet their multi-billion-parameter scale makes on-device or low-resource deployment prohibitive. Mixed-precision quantization offers a compelling solution, but existing methods struggle when the average precision drops below four bits, as they rely on isolated, layer-specific metrics that overlook critical inter-layer interactions affecting overall performance. In this paper, we propose two innovations to address these limitations. First, we frame the mixed-precision quantization problem as a cooperative game among layers and introduce Shapley-based Progressive Quantization Estimation (SPQE) to efficiently obtain accurate Shapley estimates of layer sensitivities and inter-layer interactions. Second, building upon SPQE, we propose Interaction-aware Mixed-Precision Quantization (IMPQ) which translates these Shapley estimates into a binary quadratic optimization formulation, assigning either 2 or 4-bit precision to layers under strict memory constraints. Comprehensive experiments conducted on Llama-3, Gemma-2, and Qwen-3 models across three independent PTQ backends (Quanto, HQQ, GPTQ) demonstrate IMPQ's scalability and consistently superior performance compared to methods relying solely on isolated metrics. Across average precisions spanning 4 bit down to 2 bit, IMPQ cuts Perplexity by 20 to 80 percent relative to the best baseline, with the margin growing as the bit-width tightens.

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