SplitQuantV2: Enhancing Low-Bit Quantization of LLMs Without GPUs

• URL: http://arxiv.org/abs/2503.07657v1
• Date: Fri, 07 Mar 2025 14:59:07 GMT
• Title: SplitQuantV2: Enhancing Low-Bit Quantization of LLMs Without GPUs
• Authors: Jaewoo Song, Fangzhen Lin,
• Abstract summary: SplitQuantV2 is an innovative algorithm designed to enhance low-bit linear quantization of large language models.<n>It can achieve results comparable to those of advanced algorithms.
• Score: 10.036727981085223
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The quantization of large language models (LLMs) is crucial for deploying them on devices with limited computational resources. While advanced quantization algorithms offer improved performance compared to the basic linear quantization, they typically require high-end graphics processing units (GPUs), are often restricted to specific deep neural network (DNN) frameworks, and require calibration datasets. This limitation poses challenges for using such algorithms on various neural processing units (NPUs) and edge AI devices, which have diverse model formats and frameworks. In this paper, we show SplitQuantV2, an innovative algorithm designed to enhance low-bit linear quantization of LLMs, can achieve results comparable to those of advanced algorithms. SplitQuantV2 preprocesses models by splitting linear and convolution layers into functionally equivalent, quantization-friendly structures. The algorithm's platform-agnostic, concise, and efficient nature allows for implementation without the need for GPUs. Our evaluation on the Llama 3.2 1B Instruct model using the AI2's Reasoning Challenge (ARC) dataset demonstrates that SplitQuantV2 improves the accuracy of the INT4 quantization model by 11.76%p, matching the performance of the original floating-point model. Remarkably, SplitQuantV2 took only 2 minutes 6 seconds to preprocess the 1B model and perform linear INT4 quantization using only an Apple M4 CPU. SplitQuantV2 provides a practical solution for low-bit quantization on LLMs, especially when complex, computation-intensive algorithms are inaccessible due to hardware limitations or framework incompatibilities.

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