Accurate estimation of nutritional content from food images can promote better dietary awareness and health management. Recent advances in deep learning have enabled transformer-based models to achieve state-of-the-art results for nutrition prediction; however, their high video memory (VRAM) requirements limit accessibility and scalability. In this work, we propose the integration of four VRAM optimization techniques into an existing state-of-the-art transformer-based nutrition prediction framework to reduce memory consumption during training. The optimization methods include Automatic Mixed Precision (AMP), gradient checkpointing, gradient accumulation, and the Adam8bit optimizer. The framework employs a Swin Transformer (Swin-T) backbone with a Feature Pyramid Network (FPN) and a Swin-TUNA segmentation module for food-region detection. Experiments on the Nutrition5k dataset demonstrate a 77% reduction in peak VRAM and a 78% reduction in reserved VRAM during training, while maintaining model accuracy (PMAE = 16.9%, improved from 17.2%). Although training time increased due to re-computation and accumulation overhead, the proposed optimization approach enables stable training of large transformer architectures on mid-range GPUs. This work provides a practical direction for developing memory-efficient nutrition prediction systems, improving the accessibility of transformer-based models for future research and deployment.