AQUA: Attention via QUery mAgnitudes for Memory and Compute Efficient Inference in LLMs

• URL: http://arxiv.org/abs/2509.11155v1
• Date: Sun, 14 Sep 2025 08:20:48 GMT
• Title: AQUA: Attention via QUery mAgnitudes for Memory and Compute Efficient Inference in LLMs
• Authors: Santhosh G S, Saurav Prakash, Balaraman Ravindran,
• Abstract summary: AQUA (Attention via QUery mAgnitudes) is a novel and versatile approximation strategy.<n>We show that a 25% reduction in the attention dot-product can be achieved with a statistically insignificant impact on performance.
• Score: 7.603859408568262
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The quadratic complexity of the attention mechanism remains a fundamental barrier to scaling Large Language Models (LLMs) to longer contexts, creating a critical bottleneck in both computation and memory. To address this, we introduce AQUA (Attention via QUery mAgnitudes) a novel and versatile approximation strategy that significantly reduces the cost of attention with a graceful performance trade-off. Our method operates in two phases: an efficient offline step where we compute a universal, language agnostic projection matrix via SVD on a calibration dataset, and an online inference step where we project query and key vectors and dynamically select a sparse subset of dimensions based on the query's magnitude. We provide a formal theoretical analysis of AQUA, establishing the break-even point at which it becomes more computationally efficient than standard attention. Our empirical evaluations on state-of-the-art models like Llama-3.1-8B demonstrate that a 25% reduction in the attention dot-product computation can be achieved with a statistically insignificant impact on performance across a wide range of benchmarks. We further showcase the versatility of AQUA by demonstrating its ability to synergistically accelerate existing token eviction methods like H2O and to directly reduce KV-cache memory size. By offering a controllable knob to balance efficiency and accuracy, AQUA provides a practical and powerful tool for making large-scale LLM inference more accessible and sustainable.

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