Vision-Language Pretraining (VLP) for 3D CT scans is severely constrained by the massive GPU memory required for high-resolution volumes and large contrastive batches. We propose ECT-CLIP, a resource-efficient CT VLP framework that achieves negative scaling—scaling up both the quantity and quality of negative samples—under limited computational resources. To scale the quantity, we exploit the memory asymmetry between CT and text encoding to decouple the image and text batch sizes, thereby enlarging the negative pool. To scale the quality, we design two complementary strategies: (1) deterministically separating false negatives from informative hard negatives via UMLS concept normalization, and (2) fusing multiple frozen medical text encoders to enrich semantic diversity without extra training cost. Trained on a single consumer-grade RTX 4090 GPU, ECT-CLIP achieves state-of-the-art zero-shot classification AUCs of 83.4% on CT-RATE and 75.0% on the external RAD-ChestCT benchmark, outperforming prior methods that require 4–8 high-performance GPUs. Extensive ablation studies validate the effectiveness of each negative-scaling component.