The growing computational demands of advanced AI models necessitate innovative approaches to enhance efficiency while maintaining high performance. Our novel concept focuses on the empirical optimization of inference processes in Llama, an open-source LLM, through a combination of pruning, quantization, knowledge distillation, and dynamic computation techniques, achieving significant improvements in speed and resource usage. The methodology encompassed rigorous preprocessing of diverse datasets, deployment of the baseline model, and systematic application of each optimization technique, resulting in a substantial reduction in inference time and computational load with minimal impact on accuracy. Extensive benchmarking and statistical analyses validated the effectiveness of the optimized model, demonstrating its capability to outperform the baseline in various problem-solving tasks. The findings underscore the potential of the optimized model for deployment in real-world scenarios, such as healthcare and finance, where rapid and efficient model inference is crucial. By integrating multiple optimization strategies, the research provides a robust framework for achieving compute-optimal performance in large-scale AI applications, paving the way for more practical and cost-effective deployments.