New course: Building fast LLM applications with specialized inference hardware

A new course has been released to teach developers how to build language model applications optimized for rapid response. The program was developed in partnership with Cerebras and led by specialists in the field. Inference speed—the time required for models to generate output—is a critical performance bottleneck. During inference, most processing time involves transferring model weights from storage to compute units. Specialized hardware directly addresses this constraint, enabling token generation several times faster than conventional GPU-based systems. Students will learn to evaluate different hardware architectures and their approaches to reducing latency. The curriculum combines theoretical foundations with hands-on projects targeting latency-sensitive scenarios: building responsive web interfaces, executing multi-stage analytical workflows, and deploying real-time translation and voice services. Practical coding techniques for efficient agent development with speed optimization are also covered.