TTQ: Activation-Aware Test-Time Quantization to Accelerate LLM Inference On The Fly
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866918405648416768 |
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| author | Koike-Akino, Toshiaki Liu, Jing Wang, Ye |
| author_facet | Koike-Akino, Toshiaki Liu, Jing Wang, Ye |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_19296 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TTQ: Activation-Aware Test-Time Quantization to Accelerate LLM Inference On The Fly Koike-Akino, Toshiaki Liu, Jing Wang, Ye Machine Learning Computation and Language Signal Processing 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. |
| title | TTQ: Activation-Aware Test-Time Quantization to Accelerate LLM Inference On The Fly |
| topic | Machine Learning Computation and Language Signal Processing |
| url | https://arxiv.org/abs/2603.19296 |