  Recently, in the super-resolution (SR) domain, transformers have outperformed
CNNs with fewer FLOPs and fewer parameters since they can deal with long-range
dependency and adaptively adjust weights based on instance. In this paper, we
demonstrate that CNNs, although less focused on in the current SR domain,
surpass Transformers in direct efficiency measures. By incorporating the
advantages of Transformers into CNNs, we aim to achieve both computational
efficiency and enhanced performance. However, using a large kernel in the SR
domain, which mainly processes large images, incurs a large computational
overhead. To overcome this, we propose novel approaches to employing the large
kernel, which can reduce latency by 86\% compared to the naive large kernel,
and leverage an Element-wise Attention module to imitate instance-dependent
weights. As a result, we introduce Partial Large Kernel CNNs for Efficient
Super-Resolution (PLKSR), which achieves state-of-the-art performance on four
datasets at a scale of $\times$4, with reductions of 68.1\% in latency and
80.2\% in maximum GPU memory occupancy compared to SRFormer-light.
