FraQAT: Quantization Aware Training with Fractional bits

Luca Morreale; Alberto Gil C. P. Ramos; Malcolm Chadwick; Mehid Noroozi; Ruchika Chavhan; Abhinav Mehrotra; Sourav Bhattacharya

arXiv · 2510.14823

FraQAT: Quantization Aware Training with Fractional bits

Luca Morreale·Alberto Gil C. P. Ramos·Malcolm Chadwick·Mehid Noroozi·Ruchika Chavhan·Abhinav Mehrotra·Sourav Bhattacharya

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Abstract

State-of-the-art (SOTA) generative models have demonstrated impressive capabilities in image synthesis or text generation, often with a large capacity model. However, these large models cannot be deployed on smartphones due to the limited availability of on-board memory and computations. Quantization methods lower the precision of the model parameters, allowing for efficient computations, \eg, in \INT{8}. Although aggressive quantization addresses efficiency and memory constraints, preserving the quality of the model remains a challenge. To retain quality in previous aggressive quantization, we propose a new fractional bits quantization (\short) approach. The novelty is a simple yet effective idea: we progressively reduce the model's precision from 32 to 4 bits per parameter, and exploit the fractional bits during optimization to maintain high generation quality. We show that the \short{} yields improved quality on a variety of diffusion models, including SD3.5-Medium, Sana, \pixart, and FLUX.1-schnell, while achieving $4-7\%$ lower FiD than standard QAT. Finally, we deploy and run Sana on a Samsung S25U, which runs on the Qualcomm SM8750-AB Snapdragon 8 Elite Hexagon Tensor Processor (HTP).

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Luca Morreale, Alberto Gil C. P. Ramos, Malcolm Chadwick, Mehid Noroozi, Ruchika Chavhan, Abhinav Mehrotra, Sourav Bhattacharya. 2025-10-16. FraQAT: Quantization Aware Training with Fractional bits. https://arxiv.org/abs/2510.14823

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