As the demand for local AI workflows grows, understanding the differences between Neural Processing Units (NPUs) and Graphics Processing Units (GPUs) is increasingly important. NPUs are designed for efficiency in lightweight tasks like noise suppression and real-time transcription, making them ideal for consumer devices. However, for resource-heavy applications such as running large language models (LLMs) or generating high-resolution images, GPUs remain the preferred choice due to their superior computational power and flexibility. In this feature, PC Sense explores the distinct roles of NPUs and GPUs, emphasizing how their capabilities align with various AI workloads. Discover how to evaluate hardware choices based on your specific AI needs. This breakdown highlights the critical role of VRAM in GPU performance, particularly for managing complex models and avoiding bottlenecks. You’ll also gain insight into scenarios where NPUs excel, such as enhancing video calls and audio clarity and learn why GPUs dominate in tasks like model fine-tuning and video AI. Whether you’re a developer, creator, or hobbyist, this guide offers practical considerations to help you make informed decisions about your …