GPU (Graphics Processing Unit) is a type of processor dedicated to performing graphical operations. It is capable of rapidly rendering images and videos for output on the screen. GPUs are integral parts of modern computer systems, allowing users to take advantage of powerful graphics capabilities not available with traditional CPUs. GPUs are typically used in gaming PCs, workstations, and high-end servers. They are also used to accelerate deep learning applications, and for other specialized tasks such as mining cryptocurrencies. Most modern GPUs leverage the power of parallel processing, allowing them to perform multiple operations simultaneously at a much faster rate than a regular CPU can. Their advanced technology makes them a powerful tool for both gaming and professional applications. 

With their specialized functions and high performance capabilities, GPUs are becoming increasingly important in the machine learning space. By leveraging GPUs to process large amounts of data quickly and efficiently, machine learning algorithms can produce better results with less time spent on computation. Furthermore, GPUs enable distributed computing for training models which require massive datasets. This capability allows users to take advantage of a variety of resources, scaling up their training process and delivering optimal performance. For these reasons, GPUs are becoming an essential part of many machine learning architectures. 

However, GPUs have a few shortcomings. They are limited in terms of memory and their processing power is not as effective for certain types of tasks. For example, GPUs may struggle with data-intensive computations that require frequent communication between different cores. Additionally, GPUs are usually optimized for performance at the expense of accuracy; which can be problematic for deep learning applications where precision is crucial. 

Cerebras Systems has developed an alternative to GPU technology known as Wafer Scale Engine (WSE). Rather than using multiple smaller processor units like a traditional GPU, WSE uses one massive chip containing more than 1.2 trillion transistors. This single chip is capable of performing calculations that would otherwise require thousands of GPUs – thereby drastically reducing cost and complexity. Additionally, WSE is optimized for accuracy rather than speed – making it ideal for deep learning applications where precision is critical. Finally, by handling all computation within a single chip, communication between cores is much faster and more efficient than with GPUs. In short, Cerebras Systems offers an alternative to traditional GPU technology that promises greater performance, scalability, and accuracy.