Large Language Models (LLMs) are impressive, but their size makes them resource-intensive. A new technique called Read-ME offers a clever way to make these massive models leaner and faster without sacrificing performance. It transforms pre-trained LLMs into a collection of smaller, specialized experts, similar to how a company might have different departments for different tasks. This approach, known as a Mixture-of-Experts (MoE) architecture, allows the model to dynamically activate only the necessary “experts” for a given task, saving memory and speeding up processing. Read-ME tackles two major challenges in MoE models: inefficient memory management and slow batch processing. It introduces a “pre-gating” router that determines which experts are needed *before* processing, enabling the system to prefetch the relevant data and optimize batching. Imagine a restaurant preparing ingredients ahead of time based on customer orders—this is essentially what pre-gating allows the model to do. This pre-planning leads to faster processing and smarter memory usage. Experimental results show Read-ME significantly improves efficiency and even boosts performance on standard language tasks compared to similar-sized models. It reduces latency by up to 6.1% and improves tail latency by 10%. The research demonstrates the potential of algorithm-system co-design to unlock greater efficiency in powerful AI models, paving the way for running complex LLMs on more accessible hardware.