arXiv:2606.10890v1 Announce Type: new Abstract: Post-training quantization (PTQ) compresses large language models by mapping weights to low-bit representations. The scaling factor that defines the quantization grid is typically chosen using simple, data-free heuristics. In this work, we present PiSO (Piecewise Scale Optimization), an algorithm that leverages calibration data to compute the optimal channel-wise weight scales exactly and efficiently under round-to-nearest quantization. PiSO partitions the scale search space into finitely many intervals on which the objective admits a closed-form
The increasing size and computational cost of large language models are driving a critical need for efficient compression techniques like post-training quantization.
Improved quantization methods directly impact the deployability and cost-effectiveness of advanced AI models, making them more accessible and reducing their operational energy footprint.
A new algorithmic approach provides a more precise and efficient way to optimize quantization scaling factors, potentially leading to better performance for compressed AI models.
- · AI developers
- · Cloud providers
- · Edge AI hardware manufacturers
- · Organizations deploying large language models
More efficient and performant deployment of large language models on resource-constrained hardware.
Reduced operational costs and energy consumption for AI inference, contributing to lower carbon footprints for AI infrastructure.
Acceleration of wider AI adoption in new applications and devices as computational demands become less prohibitive.
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Read at arXiv cs.LG