Vit Quantization: CPU-Centric Analysis of the Trade-Off between Size and Speed

Using Vision Transformer (ViT) models in real medical practice – for example, in hospitals or diagnostic centers – is often difficult because doctors' work computers usually do not have powerful graphics processors (GPUs), and computing resources are limited. This work investigates a complete practical pipeline for model inference, aimed at reducing computational costs without significant loss of predictive performance.


The proposed approach combines several optimization techniques. First, knowledge distillation (KD) is used, where a compact student model learns to mimic the behavior of a larger, more accurate teacher model. Second, Exponential Moving Average (EMA) of the model weights is applied to stabilize training and improve generalization. Third, post-training INT8 quantization (PTQ) is explored to reduce model size and accelerate inference. Additionally, a simplified quantization-aware training variant (QAT-lite) is considered, where the effects of quantization are partially incorporated during fine-tuning.


Experiments are conducted on the ISIC dataset, which contains dermoscopic images of skin lesions. Model performance is evaluated using standard classification metrics, including accuracy, macro-averaged F1 score, and area under the ROC curve (ROC-AUC). CPU performance is also analyzed, including inference latency, throughput, memory consumption, and the final model size.


The results show that post-training INT8 quantization preserves performance close to the FP32 baseline while substantially reducing memory and computational requirements. In contrast, QAT-lite does not consistently provide reproducible improvements over PTQ.

Vision Transformer, knowledge distillation, EMA, post-training quantization, quantization-aware training

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