#CS336

Percy Liang's closing argument reframes the KV cache as the structural root of Transformer inference inefficiency, pointing to architectures designed for inference from day one, such as SSMs, linear attention, and diffusion-based non-autoregressive generation. Engineers building or tuning inference stacks get a single coherent map from architecture to serving systems. The executable lecture notes with sympy-verified formulas make the material directly reproducible rather than slide-level theory.

shao__meng@shao__meng2 sourcesSource

Course materials are fully open, so self-learners can rent GPUs on Modal and replicate assignments without Stanford enrollment. The exam-heavy design assumes students use AI agents for experiments, then test mastery without them — a template other educators may copy. It suits practitioners who want empirical intuition for LLM training behavior rather than another from-scratch coding course.

shao__meng@shao__mengSource