Efficient Generative Models

3 credits · Graduate

As generative models become central to computing, their progress increasingly depends on algorithms that trade reasoning depth, computational cost, latency, memory, and data. This course studies those tradeoffs with a primary focus on modern language models. We examine how objectives, architectures, data pipelines, inference procedures, and serving systems are shaped by computational constraints, and how these constraints open new algorithmic questions. Students give one presentation and complete either a survey or a research project. May be repeated for credit.