The article discusses various optimization techniques for large language model inference, focusing on reducing latency and computational cost. It covers methods such as quantization, model pruning, efficient caching, and hardware‑specific acceleration. These strategies aim to make AI deployment faster and more affordable without sacrificing model quality.