TTQ: Activation-Aware Test-Time Quantization to Accelerate LLM Inference On The Fly

Toshiaki Koike-Akino; Jing Liu; Ye Wang

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arXiv · 2603.19296

TTQ: Activation-Aware Test-Time Quantization to Accelerate LLM Inference On The Fly

Toshiaki Koike-Akino·Jing Liu·Ye Wang

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Abstract

To tackle the huge computational demand of large foundation models, activation-aware compression techniques without retraining have been introduced. However, since these methods highly rely on calibration data, domain shift issues may arise for unseen downstream tasks. We propose a test-time quantization (TTQ) framework which compresses large models on the fly at inference time to resolve this issue. With an efficient online calibration, instant activation-aware quantization can adapt every prompt regardless of the downstream tasks, yet achieving inference speedup. Several experiments demonstrate that TTQ can improve the quantization performance over state-of-the-art baselines.

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BibTeX RIS

Toshiaki Koike-Akino, Jing Liu, Ye Wang. 2026-03-11. TTQ: Activation-Aware Test-Time Quantization to Accelerate LLM Inference On The Fly. https://arxiv.org/abs/2603.19296

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