Towards Uncertainty-Aware Low-Bit Quantized {LLMs} for On-Device Inference

Quantizing large language models ({LLMs}) significantly reduces memory usage and computational requirements, enabling efficient on-device inference. However, aggressive quantization can degrade model performance and exacerbate prediction uncertainty. To address this critical issue, we propose a logits-based calibration strategy where the model is restricted to generating a single token from a limited set of predefined decision tokens. By applying a temperature-scaled softmax directly on the logits corresponding to these tokens, we obtain calibrated and interpretable probability distributions, explicitly circumventing stochastic methods such as top-k sampling by directly leveraging deterministic logit values, revealing subtle behavioral shifts caused by quantization. Using Qwen-2.5 models ranging from 7 B to 72 B parameters at various quantization levels (2, 4, 6 and 8-bit), we evaluate our method across four recently released benchmarks encompassing regression ({README}++, {CompLex}-{ZH}, {GIRAI}) and classification ({DarkBench}) tasks. Thus, minimizing the risk of data leakage into pre-training data. Results indicate moderate quantization (4-bit) as optimal, particularly when combined with minimal few-shot prompting, enabling quantized {LLMs} to closely match or surpass proprietary models such as {GPT}-4o and {GPT}-4.1 in certain tasks. Our opensource toolkit facilitates straightforward deployment of reliable, uncertainty-aware quantized {LLMs} for privacy-preserving, ondevice inference, making them suitable for sensitive settings such as human-subject economic experiments and survey analysis.