Gamut compression emerges as a key technology in digital printing, ensuring minimal visual loss and color distortion from the design phase on monitors to the final print output. Monitors usually operate in a wide gamut (sRGB), and this color-rich representation is transformed and clipped to fit the printers’ smaller gamut (CMYK) when preparing images for printing, making it challenging to preserve the visual effects of the image. Existing algorithms either result in noticeable distortion after gamut mapping or are extremely time-consuming, and they all incur significant memory overhead. In this paper, we first introduce an embeddable lightweight gamut compression model based on a partitioning mechanism. Our specially designed masking encoder module divides the image into four partitions based on gamut and luminance features, tailored to meet the unique mapping characteristics of various regions. GCMLP requires only 31KB of storage to be saved as an annotation field in the original image, eliminating the need for additional memory space to store the processed image. For printing tasks, the model can be directly extracted from the original image for gamut compression. Comparative experiments show that GCMLP outperforms the industry-standard SGCK algorithm, reducing iCID by 22.58% and increasing SSIM by 12.30%. It achieves near-optimal performance in just 1/26 of the time. As part of this effort, we introduce a new gamut compression dataset of 2000 sRGB/CMYK images, which will aid in advancing research in this field.