A Mac has no NVIDIA GPU, so it can't really run vLLM — but Colab's free T4 (compute capability 7.5, exactly vLLM's minimum) can. Serve the 0.6B Qwen3 as an OpenAI-compatible API with vllm serve, fire 8 requests at once, read /metrics, and you can watch continuous batching happen.
The first half of lecture 1 covers course rules and lightning talks. The middle answers "why learn the principles?": Yen-Lung Tsai splits the anxiety of learning AI into three kinds and argues that knowing the principles tells you a model's limits, so you stop chasing every new tool. The second half is a Colab primer, from magic commands and the four standard import lines to plt.plot, Markdown, and ipywidgets. Homework 1 is to plot a function in Colab. On the Chang Gung satellite rubric, a tweaked copy of the demo earns 6 points; a function not taught in class, with well-written Markdown notes, earns 10.
MIT 6.S191's 2026 edition publishes nine lecture videos, slides, three software labs, and solutions, making it an A3 self-study course. The supplied path still depends on Google/Colab, Comet, and OpenRouter for Lab 3, while unaffiliated learners do not receive MIT credit, project feedback, or API credits.