Raschka’s Reasoning-Model Course: Start on CPU Before Planning GPU Work
You do not need to make a hardware purchase your first step toward understanding reasoning models. A better starting point is a small, working example: load the model, generate an answer, and understand how that answer is evaluated. Then decide what your next experiment needs.
What the new course actually starts with
Sebastian Raschka’s October 24, 2026 course announcement presents eight coding videos totaling more than 11 hours. The series uses Python and PyTorch and begins with a pretrained Qwen3 model. Here, “from scratch” concerns implementing the reasoning methods, rather than pretraining the base model from nothing. The author recommends familiarity with Python and PyTorch; his earlier LLM course is optional.
Raschka: course scope, prerequisites and lecture links
Separate a CPU starting point from the training stage
The official code repository says chapters 2–4 work on CPUs and GPUs, while recommending a GPU to reproduce the results in chapters 5 and 6. The announcement similarly distinguishes early generation and evaluation examples from later experiments, especially training. This is guidance about stages of the material, not a promise that every exercise will be quick on every laptop.
Official code repository: chapter notebooks and hardware guidance
Choose a small first objective that you can complete with your available machine. For example, aim to explain the inputs, outputs and intermediate steps of one generation example. Record the repository revision and environment you used so you can return to the same starting point after a later change.
Use a learning checkpoint before expanding the experiment
- Confirm that the example loads and produces an output; keep the actual error message if it fails.
- Identify the code that selects the compute device, rather than assuming that an available GPU is being used.
- Explain how a prompt becomes the model’s input and where generated tokens become readable text.
- Inspect one correct and one incorrect answer before treating an overall score as a complete explanation.
- Change one documented setting, repeat the example and write down what changed.
These are suggested study checkpoints, not requirements imposed by the author. Their purpose is to turn watching into a reproducible exercise. If you cannot yet explain the baseline, a larger machine may make the same uncertainty run faster without resolving it.
Keep inference experiments and model training distinct
The course covers both inference-time methods and training methods, including reinforcement learning and distillation. Treat them as different learning questions. For an inference experiment, record which model you used and how you generated or selected answers. For a training experiment, also record what was updated and which saved checkpoint you evaluated.
Before moving to a more demanding stage, estimate its scope from the current instructions and a small trial. Record elapsed time and resource use on your own setup. If you decide to use paid compute, define a budget and a stopping point before launching a longer run. This article does not supply a measured GPU-memory requirement, training duration or cloud price.
Choose the next step from what you learned
Continue with the early examples if you are still learning the code and evaluation process. Consider the recommended GPU path when you reach an experiment that calls for it, using the repository’s current guidance for that stage. Do not treat a successful local generation as proof that you have reproduced a training result.
The useful outcome of a first session is modest: one example you can rerun, one behavior you can explain, and one clear question for the next session. No course notebooks or hardware configurations were independently executed for this article, and no benchmark improvement or job-readiness outcome is claimed.
