IBM's TinyTimeMixer is a compact time-series forecasting model that operates with fewer than 1 million parameters. Unlike traditional AI models that often require hundreds of millions or even billions of parameters, this smaller model is designed for fast predictions and requires less computational power, making it suitable for standard devices like a Mac laptop.

Smaller AI models are gaining traction due to their efficiency and ability to perform well in resource-constrained environments, such as mobile devices and edge computing. They help minimize latency and enhance privacy by keeping data local. Additionally, the trend towards reducing model size without sacrificing accuracy is appealing to many users across various applications.

Knowledge distillation is a process in machine learning where a smaller model, referred to as the 'student,' is trained to replicate the behavior of a larger model, known as the 'teacher.' This approach allows developers to create more efficient models with fewer parameters while maintaining a similar level of accuracy. Although the initial process can be compute-intensive, it results in a model that can be used indefinitely.