Foundation models, while highly effective, are often resource-intensive, requiring substantial inference time and memory. This paper addresses the challenge of making these models more accessible with limited computational resources through meta-channel learning approaches. Our goal is to enable users to run large pre-trained foundation models on standard GPUs without sacrificing performance. We propose a latent space compression strategy that restructures the feature space while preserving essential temporal information. Surprisingly, we show that reducing the latent space to only 2.10% of its original size retains 96.15% of the classification accuracy of the full-sized model. To achieve this, we investigate both classical methods and neural network-based adapters for optimizing multivariate time series representations. Our experiments demonstrate up to a 10x speedup compared to the baseline model without performance degradation, while allowing up to 4.5 x more datasets to fit on a single GPU. This enhancement makes foundation models more practical and scalable for real-world applications.