ELUTQ: Efficient LUT-Aware Quantization for Deploying Large Language Models on Edge Devices

• URL: http://arxiv.org/abs/2510.19482v1
• Date: Wed, 22 Oct 2025 11:20:47 GMT
• Title: ELUTQ: Efficient LUT-Aware Quantization for Deploying Large Language Models on Edge Devices
• Authors: Xin Nie, Liang Dong, HaiCheng Zhang, JiaWang Xiao, G. Sun,
• Abstract summary: Large Language Models (LLMs) on CPU-based edge devices are crucial for enabling on-device intelligence and expanding AI accessibility.<n>We propose ELUTQ, an efficient quantization framework introducing a novel quantization format, Hierarchical Linear Quantization (HLQ)<n>HLQ better captures the statistical characteristics of weights without increasing the computational cost.<n>Experiments show that for LLaMA3-8B, HLQ reduces perplexity by about 8% at 3-bit and 85% at 2-bit precision.
• Score: 3.465218658690795
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The deployment of Large Language Models (LLMs) on CPU-based edge devices is crucial for enabling on-device intelligence and expanding AI accessibility. However, it remains challenging due to limited memory and computational resources. During edge inference, memory usage and latency are the primary bottlenecks. Although weight quantization can effectively reduce memory consumption, existing hardware-friendly approaches often rely on uniform quantization, which poorly fits weight distributions and incurs high dequantization overhead at low bit widths. To address these limitations, we propose ELUTQ, an efficient quantization framework introducing a novel quantization format, Hierarchical Linear Quantization (HLQ). HLQ better captures the statistical characteristics of weights without increasing the computational cost of Bit-serial LUT-based GEMM operations, thereby eliminating dequantization overhead. It is orthogonal to existing quantization algorithms and can be seamlessly integrated into various quantization pipelines. For efficient on-device deployment, ELUTQ provides optimized CPU kernels for end-to-end inference. Experiments show that for LLaMA3-8B, HLQ reduces perplexity by about 8% at 3-bit and 85% at 2-bit precision under post-training quantization, completing quantization within one hour. With efficient finetuning, HLQ further improves 2-bit performance within two hours. In terms of inference efficiency, our 2-bit LLaMA2-7B achieves over 25 tokens/s on an Apple M2 chip (4 threads, batch size = 1).

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