pretraining — The first, biggest phase of building a model: training it on enormous amounts of text so it learns language, facts, and reasoning in general.
inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.
Why it matters
Shows post-training at scale as an orchestration problem reusing pretrainingThe first, biggest phase of building a model: training it on enormous amounts of text so it learns language, facts, and reasoning in general.Full definition → infrastructure, and names the two gaps worth building: dataset inspection and direct GPU-to-GPU weight transfer between training and inferenceRunning a trained model to get answers — the phase where AI is actually used, as opposed to trained.Full definition →.
