Performance is a critical factor in computer science, particularly when machine learning algorithms are used. Computer scientists use performance metrics to understand how well the system or algorithm is performing and to identify areas where improvement can be made. Performance testing is often done to ensure that an algorithm meets the criteria for quality and accuracy before being put into production usage. Performance optimization techniques can also be employed to increase system efficiency and minimize resource utilization, thereby reducing costs and improving user experience. Performance-oriented computer scientists strive for systems and algorithms that provide optimal results with minimal investment of resources. Ultimately, performance should be measured against the goals of the application or task at hand in order to determine its effectiveness in achieving those goals. This evaluation process allows computer scientists to create robust and reliable computer systems that operate efficiently and reliably. Performance testing is a key step in the development of computer systems, ensuring that the system meets its objectives for both users and developers.  

Performance within the industry of Artificial Intelligence (AI) is a measure of how well machine learning models are able to achieve their given tasks. It is important because machine learning models are only useful when they can consistently predict accurate results and make decisions that produce the desired outcome. Poor AI performance can cause delays, incorrect or incomplete data, and missed opportunities. Therefore, monitoring and optimizing AI performance is essential for businesses that rely on machine learning solutions to run smoothly and efficiently. By improving AI performance, organizations can increase cost savings, reduce errors, enhance customer satisfaction, and improve overall business operations. As machine learning continues to gain traction in many industries, ensuring optimal machine learning model performance should be a top priority for any organization looking to stay competitive in today’s ever-evolving technological landscape. 

Cerebras Systems is the only company to undertake the ambitious task of designing a system from the ground up to accelerate AI performance. The result was the Cerebras CS-1 — the world’s fastest AI accelerator — and then two years later, the CS-2, which more than doubled the best-in-industry performance of the CS-1.