Running large Transformer models on mobile and edge devices offers significant advantages in terms of privacy, low latency, and offline usage by processing data directly on the device rather than in the cloud. However, the computational demands of these models present challenges under the constraints of mobile hardware. Techniques such as quantization, knowledge distillation, and model pruning can optimize these models for mobile use. Quantization reduces model size and memory usage by lowering the numerical precision of model weights and activations, while distillation transfers knowledge from a large model to a smaller one, maintaining performance with fewer parameters. Model pruning involves removing unnecessary model parts, which can lead to efficiency gains. The Hugging Face ecosystem, particularly through tools like ONNX, Core ML, and Hugging Face Optimum, facilitates the conversion and optimization of models for deployment on mobile platforms. These methods allow developers to leverage specialized hardware, such as Apple's Neural Engine and Android's NNAPI, for efficient on-device AI processing, making advanced machine learning accessible and practical on mobile devices.