Most current laptops struggle to run AI large language models (LLMs) due to limited hardware capabilities. Typical older models, often equipped with a standard CPU, lack the specialized hardware like a dedicated graphics processing unit (GPU) or neural processing unit (NPU) needed for efficient AI tasks. Even high-end laptops can find themselves overwhelmed by the demands of LLMs, which require significant memory and processing power. The largest models have over a trillion parameters, necessitating hundreds of gigabytes of RAM, far beyond what most consumer devices can support. To address this, manufacturers are focusing on integrating NPUs into laptops. These chips are specifically designed for the matrix operations central to AI tasks, offering better energy efficiency than GPUs. Qualcomm’s Snapdragon X chip, for example, can deliver up to 100 TOPS (trillions of operations per second), while Dell's upcoming Pro Max Plus AI PC is set to reach up to 350 TOPS. The competition among companies like Qualcomm, AMD, and Intel is driving improvements in NPU performance, positioning them as key components for future AI capabilities. However, the development of powerful NPUs isn't the sole solution. Chip designers must balance AI processing needs with traditional PC functions, ensuring that CPUs and GPUs can still perform efficiently. Memory architecture also needs a re-evaluation, as current systems maintain separate memory pools for system and graphics tasks, limiting overall performance. As AI models continue to evolve, the push for more integrated, efficient hardware will reshape how PCs are designed and how effectively they can handle AI workloads.